Hedging Weather Risk Using Weather Derivatives

Hedging Weather Risk Using Weather Derivatives:

Overview & Case Study AB InBev Word count: 22.765

Arnaud Hoornaert Student number: 01408790

Supervisor: Prof. Dr. Dries Heyman

A dissertation submitted to Ghent University in partial fulfilment of the requirements for the degree of Master in Corporate Finance

Academic year: 2017 – 2018

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Hedging Weather Risk Using Weather Derivatives:

Overview & Case Study AB InBev Word count: 22.765

Arnaud Hoornaert Student number: 01408790

Supervisor: Professor Dries Heyman

A dissertation submitted to Ghent University in partial fulfilment of the requirements for the degree of Master in Corporate Finance

Academic year: 2017 – 2018

Abstract

Purpose: The purpose of this master’s dissertation is twofold. On the one hand, a comprehensive overview of the existing literature regarding weather risk management and more particular weather derivatives is given. On the other hand, we zoom in on the weather exposure of the beer consumption in Western-Europe using data from the world’s biggest brewery AB InBev.

Methodology: We perform statistical analyses to lay bare the weather exposure of one of the largest FMCG companies in the world: AB InBev. Moreover, we design a weather hedging strategy using exchange-traded weather options. The option premium payments are calculated via Burn Analysis and Monte Carlo Simulation.

Findings: We have found a significant relationship between the quarterly average of daily maximum temperatures in London Heathrow and the beer sales volume (in litres) of AB InBev in West-Europe. The inverse regression model suggests that a rise in the quarterly average of daily maximum temperatures in London Heathrow from 8°C to 13°C equals a 15% increase in beer sales volume. The profitability of the hedging strategy greatly depends on the time horizon.

Keywords: Weather Derivatives, Risk Management, CME, AB InBev, Hedging, Futures and Options

Acknowledgement

I want to break the ice by thanking my parents for supporting me in the days when I felt snowed under. Rain or shine, they have helped me to improve the quality of this master’s dissertation.

Moreover, I want to thank Prof. Dr. João Amaro de Matos from NOVA School of Business and Economics in Lisbon for awakening my interest in the derivatives market.

A special word of appreciation goes out to my promotor Professor D. Heyman for shedding light on topics from the derivative literature. In days when I was scattered to the four winds, Professor D. Heyman provided me guidance and gave me the liberty to explore the weather derivative literature.

Writing this master thesis brought me a ray of sunshine and in the end, I am on cloud nine with the result.

With sincere gratitude,

Arnaud Hoornaert, May 28th, 2018

Table of Content

Abstract ...... V

Acknowledgement ...... VI

Table of Content ...... VII

Abbreviations ...... X

List of Figures ...... XI

List of Tables ...... XII

Introduction ...... 1

Chapter 1. Risk Management ...... 4

1.1 Types of Risk ...... 4

1.1.1 Preventable Risk ...... 4

1.1.2 Strategic Risk ...... 4

1.1.3 External Risk ...... 4

Chapter 2. Weather Risk...... 6

2.1 Impact Classification ...... 7

2.1.1 Impact on Volume ...... 7

2.1.2 Impact on Supply, Demand and Operational Exposure ...... 8

2.2 Weather Risk Management ...... 9

2.3 Recent Trend ...... 10

2.4 Integral Part of Risk Management ...... 12

Chapter 3. Conventional Derivatives ...... 14

3.1 Overview/Description ...... 14

3.1.1 Futures and Forwards ...... 14

3.1.2 Options...... 14

3.1.3 Swaps ...... 15

3.2 Hedging using Conventional Derivatives ...... 15

Chapter 4. Weather Derivatives ...... 16

4.1 Overview/Description ...... 16

VII

4.1.1 Brief History ...... 17

4.1.2 Different Exchange-Traded Weather Derivatives Contracts ...... 18

4.2 Hedging using Weather Derivatives ...... 22

4.2.1 Weather Hedging Strategies using Options ...... 23

4.2.2 Weather-Exposed Industries ...... 25

4.2.3 OTC vs Exchange-Traded Weather Derivatives ...... 30

4.2.4 Empirical Results on Hedging using Weather Derivatives ...... 31

4.2.5 Practical Examples of Hedging using Weather Derivatives...... 32

4.3 Pricing Models for Weather Derivatives ...... 34

4.3.1 Burn Analysis ...... 35

4.3.2 Monte Carlo Simulation ...... 36

4.4 Alternatives for Weather Derivatives ...... 38

4.4.1 Insurance ...... 38

4.4.2 Natural Hedging ...... 38

Chapter 5. Case Study AB InBev ...... 40

5.1 Objective of the Case Study ...... 40

5.2 Company Presentation ...... 41

5.2.1 History of the company ...... 41

5.2.2 Core Business ...... 42

5.2.3 Financial Performance ...... 43

5.3 Data-Analysis ...... 43

5.3.1 Beer Sales ...... 43

5.3.2 Choice of Weather Station...... 47

5.3.3 Testing Variables ...... 48

5.4 Weather Hedging Strategy: Hedging against relatively cold winter ...... 57

5.4.1 Call Option Premium Payments...... 59

5.4.2 Result from the Hedging Strategy ...... 64

5.5 Reflections on the Case Study ...... 68

Conclusion ...... 70

VIII

Further research ...... 72

References ...... XIV

Abbreviations

CME Chicago Mercantile Exchange EUMETSAT European Organisation for the Exploitation of Meteorological Satellites GDP Gross Domestic Product WEF World Economic Forum OTC Over-the-counter hl Hectolitre CAT Cumulative Average Temperature HDD Heating Degree Day CDD Cooling Degree Day °C Degrees Celcius StDev Standard Deviation s.d Sine Datum AB InBev Anheuser-Busch InBev British Met Office British Meteorological Office NASA National Aeronautics and Space Administration FMCG Fast Moving Consumer Goods

List of Figures

Figure 1: Weather-related billion-dollar disasters. Source: World Economic Forum, 2018 ....11 Figure 2: Relationship between average temperature (<18°C) and HDD Index. Source: Hoornaert, 2018 ...... 20 Figure 3: Profit/Loss Diagram Weather Put Option. Source: Müller & Grandi, 2000...... 24 Figure 4: The M&A history of Anheuser-Busch Inbev, pre-2015. Source: AB InBev 2018. ....42 Figure 5: AB InBev’s quarterly sales (in litres) of West-Europe. Source: AB InBev Quarterly Reports 2005-2013...... 44 Figure 6: The downward trend in AB InBev’s sales (West-Europe). Source: AB InBev Quarterly Reports 2005-2013 ...... 45 Figure 7: Detrended time series ...... 47 Figure 8: Quarterly average of daily maximum temperatures in London Heathrow. Source: British Met Office ...... 49 Figure 9: Detrended Sales versus London Temperatures...... 49 Figure 10: Boxplot of quarterly average of daily maximum London temperatures and Detrended Sales...... 50 Figure 11: Curve Estimation of Detrended Sales and London Temperatures ...... 51 Figure 12: : Boxplot of the Sunshine hours in London and Detrended Sales ...... 53 Figure 13: Curve Estimation Detrended Sales and Sunshine Hours ...... 54 Figure 15: Boxplot of Accumulated Rainfall and Detrended Sales ...... 55 Figure 16: Histogram of detrended HDDs London Heathrow ...... 62 Figure 17: Profit/Loss Diagram Call option in the year 1981. Source: Hoornaert, 2018...... 65

List of Tables

Table 1: Comparison between Conventional and Weather Derivatives. Source: Müller & Grandi, 2000 ...... 16 Table 2: Practical example relationship average temperature and HDD index. Source: Hoornaert, 2018 ...... 21 Table 3: Hedging Strategies. Source: Müller & Grandi, 2000 ...... 23 Table 4: Contract specifications of an exchange-traded weather option at CME...... 33 Table 5: Linear regression of the downward trend ...... 45 Table 6: Linear Regression of Detrended Sales ...... 47 Table 7: Correlation between Detrended Sales and London Temperatures ...... 50 Table 8: Curve Estimation of Detrended Sales and London Temperatures ...... 51 Table 9: Curve Estimation between Detrended Sales and Sunshine hours...... 54 Table 10: Correlation between Rainfall and Detrended Sales ...... 55 Table 11: Weather Hedging Strategies applied to the case of AB InBev. Source: Müller & Grandi, 2000 ...... 57 Table 12: Coefficient of determination of linear regression between quarterly average of daily maximum temperatures and quarterly beer sales volume...... 58 Table 13: Standard Deviation quarter 1 and 4 versus quarter 2 and 3 ...... 59 Table 14: F-Test for variances ...... 59 Table 15: Option Premium payments at different strike indices via Burn Analysis ...... 61 Table 16: Normality tests for detrended quarterly HDDs London Heathrow ...... 63 Table 17: Option premium payments at different strike indices via Monte Carlo Simulation ..64 Table 18: Option Premium payments Monte Carlo Simulation versus Burn Analysis ...... 64 Table 19: Profit/Loss of hedging strategy 1999-2017 ...... 66 Table 20: Profit/Loss of hedging strategy during the case study ...... 66 Table 21: Profit/Loss of hedging strategy 2011-2017 ...... 67

XII

Introduction

“Too cold or too hot temperatures, no wind or too strong wind, drought or flooding, May seriously harm industries and constitute a major risk to revenues. Weather derivatives are designed to offer a financial tool for hedging this volume risk”. (Benth & Benth, 2013)

According to EUMETSAT (2017), over one-third of the European economy is weather- sensitive. (EUMETSAT, 2017) Allianz has found a similar number for the USA. According to the research of the National Oceanic and Atmospheric Administration (2018), approximately $5,7 trillion of the $15,7 trillion in GDP of the USA is sensitive to weather variables. (National Oceanic and Atmospheric Administration, 2018) (Allianz Global Corporate & Specialty, 2016)

This means that a significant proportion of demand for European and American products is related to weather variables such as temperature, rainfall and sunshine. Weather variables amplify the volatility in the financial performance of various industries as there are: the energy sector, transportation, construction, agriculture, tourism, beverage industry, ski-area operators and outdoor amusement events. The companies in the abovementioned sectors all face a certain weather risk that can impact their cash flows and profits severely. Whereas a relatively mild winter can cause sales drops of gas providers, a cold summer will withhold people from consuming ice-cream, having BBQ’s and drinking beers.

According to Barrieu & Scaillet (2010), a weather risk can be defined as: “A risk that is part of everyday life, having limited economic consequences on an everyday basis, but with huge potential consequences in its accumulation or repetition.” (Barrieu & Scaillet, 2010)

In this paper, we zoom in on the beer consumption and in particular the West-European beer consumption. With more than 1600 different beers, the first beer pipeline in the world and home to the world’s largest brewer AB InBev, Belgium is the beer capital of the world. Belgian beer culture is even awarded “Intangible cultural heritage of humanity” by UNESCO in 2016. (The Guardian, 2016) On warm days, terraces are full of people enjoying a panoply of Belgian beers. However, a relatively cold season will affect the beer consumption. It is crystal clear that beer has a demand pattern related to weather variables. Data from Germany (Lisson, Brunhart, & Gasteige, 2013) and the UK (Lloyd Insurance, 1999) found a significant relationship between beer sales and temperature. The British Meteorological Office

1 has calculated that if temperatures rise with 3°C in the UK, the demand for beer increases with 10%. (Lloyd Insurance, 1999)

This master’s dissertation includes a case study in which regression analyses are performed between the quarterly average of daily maximum temperatures in London Heathrow and the beer sales volume (in litres) of AB InBev in the period 2005-2013 in West-Europe.

The purpose of this master’s dissertation is twofold. On the one hand, a comprehensive overview of the existing literature regarding weather risk management and more particular weather derivatives is given. On the other hand, the weather exposure of the West-European beer consumption using data from the world’s biggest brewery AB InBev is examined.

Weather derivatives can be defined as “financial instruments whose payoffs are contingent on weather conditions.” (Perez-Gonzalez & Yun, 2013) The underlying asset/element in the contract is a weather index. This can either be rainfall, temperature, sunshine or frost. The payoff results from the difference between the actual (spot) value of the underlying weather index and the contracted strike index. (Perez-Gonzalez & Yun, 2013) (Müller & Grandi, 2000)

The structure of the paper is as follows. In the first chapter, risk management in general is described. In the second chapter, the focus is specifically on weather risk exposure and its impact on the financial statements. Chapter three intends to give a concise overview of the existing literature regarding conventional derivatives.

In the penultimate chapter, the focus is shifted towards weather derivatives. It is vital to briefly introduce the concepts of conventional derivatives (chapter three) to analyse and explain the concept of weather derivatives adequately. Furthermore, the existing literature regarding the application of weather derivatives to hedge revenues of companies in weather-sensitive industries is presented. In 4.2.1, hedging strategies using weather derivatives are designed. In 4.3, we present two pricing models for weather options: Burn Analysis and Monte Carlo Simulation. In the last section of the penultimate chapter, we discuss two different alternatives for weather derivatives as a weather risk management tool, i.e. insurance and natural weather hedging. Lastly, the case study with data from AB InBev will be elaborated in chapter five. Literature from Germany (Lisson, Brunhart, & Gasteige, 2013) and the UK (Lloyd Insurance, 1999) have found a positive relationship between temperature and beer sales. As a result, we expect demand for beer to go up as temperature goes up.

The contribution of this master’s dissertation to the existing literature does not solely originate from drawing the relationship between temperature and the sales volume of the world’s biggest brewer. Moreover, the analysis of two other weather variables sunshine and rainfall on the one hand and the design of a weather hedging strategy using European weather options on the other hand is also an enhancement of the existing literature.

Chapter 1. Risk Management

In the article “Managing risk, a new framework” published in Harvard Business Review, the authors Kaplan & Mikes (2012) create a typology for the risks that organizations face. (Kaplan & Mikes, 2012) With this typology, organizations can establish more effective risk management tools for each of the different risk types. The proposed risk types have different characteristics, different roots and ask for different risk management tools. Only two out of the three risks can be mitigated using derivatives as a risk management tool. All risk types are equally important and can be destructive for the survival of the company.

1.1 Types of Risk

1.1.1 Preventable Risk

Preventable risk is the first risk type. Preventable risks are perfectly avoidable within the firm, because the management can control them. For example, when employees prefer short-term gains over adding long-term value to the company or when political games between managers harm the team spirit.

1.1.2 Strategic Risk

The second risk type is the strategic risk. This is the risk the company bears when executing its strategy. A financial institution carries default risk when providing a loan for example. Contrary to preventable risk, strategic risk is not preventable, nor is it entirely avoidable. A straightforward strategy will always include a specific risk. The higher the expected returns resulting from the operations/processes of a project, the more significant the risks are. (Sharpe, 1964) Conventional derivatives can be used as a tool to hedge some of the strategic risks. Derivatives like futures, options and swaps enable a firm to hedge its risk exposure without an “identifiable cash position in the underlying commodity”. (Nguyen, 2015) (Smith & Stulz, 1985). Later in this master’s dissertation, we will explain the concept of hedging further into detail.

1.1.3 External Risk

External risk is the last risk type in the classification of Kaplan & Mikes (2010). External risks have their origin outside the company and are hard to mitigate.

This risk type includes political disasters, macroeconomic shocks and extreme weather events. (Kaplan & Mikes, 2012) Although it is impossible for organizations to neutralize external risk, it is vital for a firm to recognize this risk and to minimize its impact.

Weather derivatives and insurance can be adopted as a tool to mitigate the impact of exposure to external weather risk. However, it is important to question whether a hedging strategy using weather derivatives and to a greater extent all derivatives can add value to the firm. In 4.2.4 we will focus on the added value of a hedging strategy using weather derivatives.

Chapter 2. Weather Risk

According to EUMETSAT, over one-third of the European economy is weather-sensitive. (EUMETSAT, 2017) Allianz has found a similar number for the USA. (Allianz Global Corporate & Specialty, 2016) According to the research of the National Oceanic and Atmospheric Administration (2018), approximately $5,7 trillion of the $15,7 trillion in GDP of the USA is sensitive to weather variables. (National Oceanic and Atmospheric Administration, 2018)

Extreme rains, long droughts, floods and heat waves impact business processes and consumer decisions. (Benth & Benth, 2013) (Müller & Grandi, 2000) However, it is essential to draw the attention upon the fact that weather risk does not solely arise from the consequences of extreme weather events like hurricanes, tropic storms and extreme droughts or floods. Weather risk comprises all deviations from “a normal pattern”. (Allianz Global Corporate & Specialty, 2016)

Whereas a relatively warm winter can cause sales drops of gas providers, a relatively mild summer will withhold people from consuming ice-cream, having BBQ’s and drinking beers. “Weather risk comprises uncommon, unseasonal and unexpected weather and can severely impact cash flows”. (Allianz Global Corporate & Specialty, 2016)

According to Barrieu & Scaillet (2010), a weather risk can be defined as:

“A risk that is part of everyday life, having limited economic consequences on an everyday basis, but with huge potential consequences in its accumulation or repetition.” (Barrieu & Scaillet, 2010)

2.1 Impact Classification

The impact of weather variables on the economy can be analysed from two perspectives. In 2.1.1, the revenue is the central subject of change, triggered by the weather variables. (Müller & Grandi, 2000) In 2.1.2, the focus is on three different channels through which the weather can have an impact on the company’s financials. (Allianz Global Corporate & Specialty, 2016)

2.1.1 Impact on Volume

In economic terms, weather exposure can be defined as: 훿푅 푊푒푎푡ℎ푒푟 퐸푥푝표푠푢푟푒 = (1) 훿푊

With 훿푅 = 퐶ℎ푎푛푔푒 𝑖푛 푟푒푣푒푛푢푒 훿푊 = 퐶ℎ푎푛푔푒 𝑖푛 푤푒푎푡ℎ푒푟 푣푎푟𝑖푎푏푙푒

The definition in equation 1 implies that a change in revenues is triggered by a change in a weather variable. (Brockett, Wang, & Yang, 2005) (Müller & Grandi, 2000) The following example can serve as in illustration here: if the temperature rises with 3°C in the UK, the demand for beer increases with 10%. (Lloyd Insurance, 1999).

Since 푅푒푣푒푛푢푒s = 푃푟𝑖푐푒 ∗ 푄푢푎푛푡𝑖푡푦 (2) a change in weather variables will either trigger a change in the revenues through the price and/or the quantity.

According to Brockett et al. (2012), Perez-Gonzalez & Yun (2013) and Müller & Grandi (2000) weather exposure is principally regarded as a volume or quantity risk, since it is doubtful that the price of the weather-sensitive goods will change with weather indices. We illustrate the argument with the example of the ice-cream industry. Ice-cream will not be offered at lower prices in a cold and wet summer compared with a relatively hot summer. (Müller & Grandi, 2000) Consequently, the drop in revenues of the ice-cream industry is the result of the sales contraction, triggered by adverse weather conditions and not the result of a price reduction.

However, KPMG (2017) and Müller & Grandi (2000) draw the attention upon the fact that there are also industries like the energy and power supply sector in which the market mechanisms are more efficient. In relatively mild winters, there is empirical evidence that the price of electricity drops in response to the decrease in demand. Similar, in case of cool summers, the demand for oil can increase and as a result the price rises as well. (Müller & Grandi, 2000) To find the adequate weather risk management tool, it is crucial to determine if the industry in question suffers from either volumetric risk or price risk induced by the weather variables. Price risk in the energy sector can be hedged using conventional commodity derivatives, whereas volumetric risk can be mitigated with weather derivatives. However, if the company suffers from both price risk and volumetric risk, a cross-hedge is the most adequate tool. (Müller & Grandi, 2000) In section 4.1, we will elaborate further on the distinction between price and volumetric risk.

2.1.2 Impact on Supply, Demand and Operational Exposure

Another way to look at the impact of weather conditions on the financial performance of an industry is not to determine whether the weather variables impact either the price or the sales volume of the industry, but rather to determine if the weather is affecting the company’s financials from either the supply side (2.1.2.1), the demand side (2.1.2.2) or through operational exposures (2.1.2.3). (Allianz Global Corporate & Specialty, 2016) While volatile weather conditions can cause a drop in sales volume due to volumetric weather exposure, the channel through which the sales volume is impacted remains ambiguous. The distinction between the different channels through which the weather can have an impact allows us to categorize industries according to their characteristics.

2.1.2.1 Supply

Utility companies and more specifically green electricity suppliers will primarily have a supply- side weather exposure. (Allianz Global Corporate & Specialty, 2016) The absence or a lack of wind/sun will impair the green electricity generation negatively and result in volatility in the electricity supply of the utility company. According to Allianz, annual wind power generation can deviate with 20% from the long-term average due to the weather conditions in the given year. (Allianz Global Corporate & Specialty, 2016)

2.1.2.2 Demand

The demand for certain goods and services is highly correlated to weather variables. The automotive industry will sell relatively fewer batteries in mild winters compared with cold winters and the beverage industry will sell less beer and soda during mild summers. (Allianz Global Corporate & Specialty, 2016)

2.1.2.3 Operational Exposure

Weather variables can also impact the business processes of specific industries. The airline industry has a significant operational exposure to the weather variables snow and ice. Ice formation on the airport apron can severely disturb the operations of the airline hub, resulting in lower punctuality and perturbed flight schedules. (Allianz Global Corporate & Specialty, 2016) Construction companies also face an operational exposure to the frost. Later in this paper, frost contracts will be introduced. The Chicago Mercantile Exchange has developed specific weather derivatives based on the variable “frost days” for construction companies in the Netherlands. (CME Group, 2011)

2.2 Weather Risk Management

Although it is impossible for companies to control the weather, companies can use weather risk management tools to mitigate the negative impact on their financial performance. Allianz defines weather risk management as follows:

“Weather risk management is the management of financial risks that are directly or indirectly linked to the occurrence of an observable weather event or variability in a measurable weather index. Crucially, no physical damage is required for a payment to be made, unlike with traditional insurance products. Such products focus on the use of weather data – measurable weather variables such as temperature, precipitation, sunshine, snowfall and wind – as the basis for risk indices. Protection is based around the accurate recording of independent weather data.” (Allianz Global Corporate & Specialty, 2016)

2.3 Recent Trend

There is an unequivocal trend noticeable in the weather variables around the globe. In recent years, the weather variables have become more volatile and more extreme. (National Aeronautics and Space Administration, 2018) The Royal Netherlands Meteorological Institute, for example, warns for extremer weather in the 21st century. The experts claim that computer calculations show that the climate has become more extreme this century. (Koninklijk Nederlands Meteorologisch Instituut, 2018) Furthermore, The World Economic Forum publishes its Global Risk Report every year, in collaboration with Warton Risk Management, the University of Oxford and the National University of Singapore. (World Economic Forum, 2018) This Risk Report draws on the annual Global Risks Perceptions Survey which is completed by 1,000 experts and decision-makers. The trend in the Global Risk Report is clear: Every year, environmental risks have grown tremendously in prominence. In the latest Annual Risk Report of 2018, extreme weather events are the No. 1 global risk with the highest likelihood and the No. 2 global risk in terms of impact. (World Economic Forum, 2018) The perception of extreme weather events as the most prominent global risk is the result of the extreme and volatile weather conditions of 2017. There were four impactful Atlantic hurricanes in 2017, i.e. Harvey, Irma and Maria and José. (World Economic Forum, 2018) According to Moody’s Analytics, “Hurricanes Harvey and Irma have cost the US economy between 150 and 200 billion in combined property damage, making 2017 the second costliest hurricane year in more than hundred years”. (Time Magazine, 2017)

In figure 1, the weather-related billion-dollar disasters in the US are presented. Especially severe storms and freezes have contributed to the significant upward trend.

Figure 1: Weather-related billion-dollar disasters. Source: World Economic Forum, 2018

There are more sources that find the same evolution towards increasingly costly weather events over the last decades. The ACE index1, for example, indicates that September 2017 was the most intense storm month ever measured in the USA. (World Economic Forum, 2018) What’s more, Chile had more than eight times the long run average of wildfires and Portugal faced more than 100 deaths after extremely long-lasting wildfires. (World Economic Forum, 2018) According to Allianz Global Corporate & Specialty (2016), on average $15bn per year was paid out in insured losses for such extreme weather events around the globe in the period 1980- 1989. “This has risen every decade to hit $40bn a year on average between 2000 and 2009. Most recently in the three years from 2010 to 2013 alone, $70bn in damages from these weather events has been paid out annually.”

(Allianz Global Corporate & Specialty, 2016)

1 The Accumulated Cyclone Energy index measures the intensity and duration of Atlantic storms

In 2014, NASA published a study using a blend of weather data and climate models. This study summarizes the evolution as follows: “Many types of extreme events are expected to increase in frequency and magnitude in the future and pose hazards to NASA’s mission, infrastructure and workforce.” (National Aeronautics and Space Administration, 2018)

In 2016, an article was published by Harvard Professor in Economics Kenneth Rogoff in which the author claims that there is a significant relationship between extreme weather events and short-term macroeconomic statistics in the US. He concludes that “extreme weather can add or subtract 100.000 jobs to monthly US employment.” (Rogoff, 2016) (Boldin & Wright, 2015)

We can conclude this section with the statement that today an active weather risk management is more than ever crucial in modern business management. That is why weather exposed industries need to adopt weather risk management tools as an integral part of their risk management strategy.

2.4 Integral Part of Risk Management

Until recently, weather was regarded as an external, uncontrollable variable which impacts sales volume or business processes. (Allianz Global Corporate & Specialty, 2016) Nevertheless, companies can mitigate the effects of adverse weather conditions on their business performance using the adequate weather risk management tool. Stockholders have become more conscious of the opportunities to mitigate the impact of weather variables on the business performance and as a result “bad weather is no longer a justification for poor quarterly financial reports.” (Allianz Global Corporate & Specialty, 2016) Weather risk management is now becoming an integral part of a company’s risk management strategy. Weather risk management tools like insurance and weather derivatives should complement the interest rate, foreign exchange and commodity price risk management tools. (Harvard Business Review, 2006)

The airline industry and Ryanair more specifically can serve as an example. Airlines like Ryanair face a significant commodity price risk, interest rate risk and currency exchange rate risk. (Ryanair, 2009) The commodity price risk arises from the volatility of the kerosene price. To alleviate the impact of jet fuel price volatility, Ryanair enters into forward commodity contracts. Second, foreign currency forward contracts are used to minimize the exposure to

12 foreign currency movements. (Ryanair, 2009) Thirdly, Ryanair also hedges its interest rate exposure.

However, Ryanair’s reports fail to address its weather exposure. (Ryanair, 2009) To illustrate the significant weather exposure of airlines, we refer to the consequences of “Snowmageddon” in the beginning of 2010 in the United States. In those days, “more than 24.000 flights were cancelled.” (Allianz Global Corporate & Specialty, 2016). It is essential to emphasize that weather variables have a decisive impact on the operations of the airline industry and should be considered as a full-fledged facet of risk management.

One option for a company to minimize weather exposure is using weather derivatives. In what follows, we will elaborate briefly on conventional derivatives to build the liaison and draw the parallels with the features and applications of weather derivatives.

Chapter 3. Conventional Derivatives

3.1 Overview/Description

The European Market Infrastructure Regulation defines derivatives as follows:

“A derivative is a financial contract linked to the fluctuation in the price of an underlying asset or a basket of assets. Common examples of assets on which a derivative contract can be written are interest rates instruments, equities or commodities.”

(The European Market Infrastructure Regulation, 2017)

Derivatives appear in many forms. The most common products are Futures, Forwards, Options and Swaps.

3.1.1 Futures and Forwards

“A forward or futures contract is an agreement to buy or sell a specified quantity of an asset at a specified price with delivery at a specified date in the future.” (Bank of international settlements, 2012) The buyer of the future or forward contract pays a fixed price and receives the underlying asset at maturity. The seller of the contract agrees to deliver the asset at maturity or settle the difference between the contract price (the futures price) and the actual price. (Damodaran, 1998)

3.1.2 Options

A second category of derivatives includes options. “Call option contracts give the right, not the obligation to buy a specified quantity of a commodity or financial asset at a particular price on or before a certain future date. (the expiration date) (Bank of international settlements, 2012) The purchaser of the call option will pay a fixed cost (the premium) to obtain the right to exercise the option and buy the underlying asset. (Bank of international settlements, 2012) The holder of the option is not obliged to exercise this right. In the scenario in which the holder does not exercise the option, the holder of the option only loses his premium. (Hull J. C., 2012) In our case study, we will work with option contracts to hedge the weather risk.

3.1.3 Swaps

In swap contracts, two parties agree to exchange (to swap) cash flows of different financial instruments. Swap contracts are created to benefit from competitive advantages of different economic agents. (Vander Vennet, 2017). (Bank Of International Settlements, 2003)

3.2 Hedging using Conventional Derivatives

Derivatives are used by three types of traders with different motives: the speculators, the arbitrageurs and the hedgers. (Hull J. C., 2012) The speculator takes a risky position on the derivative market and endeavours a profit. He bets on the future direction of an underlying element, e.g., stock index, interest rate, currency. (Hull J. C., 2012) The other market participants that trade derivatives are the hedgers. The main objective of the hedger is not to make a profit, but rather to protect the value of an underlying asset and consequently to neutralize fluctuations in the price of the underlying due to an unwanted/unexpected event. (Vander Vennet, 2017). Arbitrageurs are the third party. The arbitrageurs take no risk at all. They merely take an offsetting position in an instrument to secure a profit. (Hull J. C., 2012) (Hull, Treepongkaruna, Colwell, Heaney, & Pitt, 2014) In 4.3, we will zoom deeper in on the no-arbitrage concept.

In what follows, we will dive deeper into the speculators and the hedgers. “Speculators use derivatives to bet on the future direction of a market variable, e.g., Stock, interest rate, commodity.” (Hull, Treepongkaruna, Colwell, Heaney, & Pitt, 2014) (Hull J. C., 2012) Contrary to hedgers who want to avoid exposure to price fluctuation, speculators will take a specific position, triggered by the belief a specific market movement (up, down or stable) will take place. (Hull J. C., 2012)

In the aftermath of the financial crisis, derivatives and more specifically Credit Default Swaps were widely criticized for their speculatory and risky characteristics. (Matos, 2017) However, it is important to underline the economic benefits of derivatives. Derivatives can help economic agents to manage risks and neutralize price shocks of their assets. (Bank of international settlements, 2012). Because of the risk management applications of these contracts, the market for derivatives has experienced impressive growth. (Bank of international settlements, 2012) To put in perspective: In June 2017, the notional value of outstanding over-the-counter derivatives was $542 trillion. (Bank Of International Settlements, 2017)

Chapter 4. Weather Derivatives

4.1 Overview/Description

We start this section by recapitulating the definition of a conventional derivative.

“A derivative is a financial contract linked to the fluctuation in the price of an underlying asset or a basket of assets.” (The European Market Infrastructure Regulation, 2017)

The underlying asset on which the derivative is written can be a stock index, a commodity index, or other securities. The value of the derivative is consequently linked with the fluctuation of the underlying index. Weather derivatives are a specific type of derivatives which are written over weather indices like temperature, heat, cold, frost, snow and precipitation. One can create a weather derivative contract on basically every weather variable.

In this section, we will explain the technical details about weather derivatives, but briefly it works as follows: Weather derivatives are contracts which accumulate the “differences between the actual values of a weather index and the agreed values in the contract.” (CME Group, 2011)

Conventional derivatives are based on tradable financial assets like stocks, bonds or currencies and help to hedge exposure to unwanted/unexpected price fluctuations of the underlying asset. Weather derivatives, on the other hand, are based on data/indices of non- tradable variables like temperature, sunshine, precipitation and frost. (Müller & Grandi, 2000) Although the weather cannot be traded, the volume of goods and services depending on the weather conditions can. It is exactly this volume risk, rather than a price risk, that companies want to hedge by using weather derivatives. This volume risk is “the risk that results from a change in the demand for goods due to a change in the weather.” (Müller & Grandi, 2000) In table 1, the differences are summarized.

Conventional Derivatives Weather Derivatives

Risk Type Price Risk Volume Risk

Commodity, Stock index, Weather Index (e.g. temperature, The Underlying currency,… rainfall, sunshine hours) Physical Delivery or Both Cash Settlement Cash Settlement Table 1: Comparison between Conventional and Weather Derivatives. Source: Müller & Grandi, 2000

4.1.1 Brief History

Aquila Energy was the first organization to close a weather derivative contract in 1997. (CME Group) Aquila Energy, an American energy producer, closed a contract in 1997 to sell electricity to ConEd. This purchase contract included a weather clause. Aquila would offer the contractor a discount on its electricity bill if August temperatures would be lower than expected. (Garcia & Sturzenegger, 2001))

The rationale behind it was as follows: if August temperatures were milder and lower than average August temperatures, the demand for cooling (air conditioning) would drop. ConEd would suffer from this decrease in demand and wanted to hedge its revenues against this weather risk.

The rationale of hedging against unusual weather gained significant interest during the mild winter of 1997-1998, also known as El Niño. “El Niño refers to the period of hot sea temperatures in the east of the Pacific Ocean” (Royal Netherlands Meteorological Institute, 2018) which happens “at irregular intervals of two to seven years”. (Wikipedia, 2017) American firms realized that their earnings faced a significant weather risk caused by the mild winter. Consequently, the demand for weather hedging products skyrocketed that winter.

Starting in 1999, the first exchange-traded weather derivatve contracts were launched by the Chicago Mercantile Exchange. In the beginning, CME only traded two weather products: Heating Degree Days (HDD) and Cooling Degree Days (CDD) for ten cities in the USA. (CME Group, 2011) In the beginning, Enron was a major player in this developing weather derivatives market.

In 2003, CME expanded its weather derivatives to six European cities and two cities around the Pacific Rim (CME Group, 2011). CME also launched a new weather derivative, the Cumulative Average Temperature (CAT). Later, the number of weather stations on which weather derivative contracts were written, also increased.

The French utility company SOCCRAM was the first European company to use weather derivatives in 1999. (Müller & Grandi, 2000) SOCCRAM designs, builds and operates utility networks as part of a public service delegation. (Soccram, 2018). The company wanted to hedge its earnings against warm winters in which the demand for electricity is lower. (Müller & Grandi, 2000)

According to the Weather Risk Management Association, the total market for weather derivatives (OTC and exchange-traded contracts) was almost $11,8 billion by the end of 2011. (Benth & Benth, 2013). Compared to the $542 trillion notional value of the OTC derivative market, the value of the weather derivative market fades away. In a paper “why haven’t weather derivatives been more successful”, the author emphasizes that the potential of the weather derivative market is significant though. (Til, 2014) The author draws the attention upon the fact that especially OTC contracts will continue to grow, because “reinsurance companies and funds, using customised OTC derivatives, may be the best suited for taking on and managing weather risk. (Til, 2014)

4.1.2 Different Exchange-Traded Weather Derivatives Contracts

Analogous to conventional derivatives, weather derivative contracts exist in two different forms: OTC and exchange-traded contracts. OTC weather contracts can be written over every weather variable, as this is bilaterally arranged. (Hull J. C., 2012) In 4.2.4, we will go deeper into the differences between OTC and exchange-traded contracts.

This section will guide the reader through the most common, liquid and exchange-traded weather derivative contracts. All the contracts in this section are exchange-traded on the CME.

The most liquid exchange-traded weather derivatives are based on temperature. There are three indices on which a temperature derivative is based. • Cooling degree-day (CDD) • Heating degree-day (HDD) • Cumulative average temperature (CAT).

4.1.2.1 Cooling Degree Day (CDD)

The CDD and HDD indices, traded at the CME are calculated against a reference value of 18°C2. (Benth & Benth, 2013) Suppose, we want to calculate the CDD for one day (푡). The CDD index will be calculated as follows:

2 18°C = 65° Fahrenheit

퐶퐷퐷(푡) = 푚푎푥(푇(푡) − 18,0) (3)

(Benth & Benth, 2013) With 푇(푡) = 푎푣푒푟푎푔푒 푡푒푚푝푒푟푎푡푢푟푒 표푛 푑푎푦 푡

An average temperature on day 푡1 of 20°C results in a 퐶퐷퐷(푡1) = 2

An average temperature on day 푡2 of 14°C results in a 퐶퐷퐷(푡2) = 0

The index interprets a temperature under 18°C as a day in which no demand for cooling (air conditioning for example) is required and as a result, the index is 0 in this case. A temperature above 18°C is interpreted as a day in which there is demand for cooling and the index calculates the difference between the average temperature on that specific day and 18°C. (Benth & Benth, 2013)

For a specific period (푡1, 푡2), the CDD index is measured as follows:

푡2

퐶퐷퐷(푡1, 푡2) = ∑ 푚푎푥(푇(푡) − 18,0) (4)

푡=푡1

As the index only measures temperatures above 18°C, it is clear that this CDD contract is conceptually created to measure the demand for cooling. (Benth & Benth, 2013) As a result, CDD contracts are only available in the summer months, when temperatures are most likely to be above 18°C. In the USA, CME offers CDD contracts in May, June, July, August and September. (Benth & Benth, 2013) Since temperatures in parts of Europe are hardly above 18°C in summer, CDD contracts are not offered in Europe. Instead, CME offers European CAT contracts in summer. More information about CAT can be found in 4.1.2.3.

4.1.2.2 Heating Degree Day (HDD)

“The HDD index, on the other hand, measures the demand for heating.” (Benth & Benth, 2013) For a specific day 푡, we can define the HDD index mathematically as:

퐻퐷퐷(푡) = 푚푎푥(18 − 푇(푡), 0) (5)

(Benth & Benth, 2013)

With 푇(푡) = 푎푣푒푟푎푔푒 푡푒푚푝푒푟푎푡푢푟푒 표푛 푑푎푦 푡

When we calculate the HDD Index over a specific period, we define it as follows:

푡2

퐻퐷퐷(푡1, 푡2) = ∑ 푚푎푥(18 − 푇(푡), 0) (6)

푡=푡1

During the period of the contract (푡1, 푡2), the HDD index only obtains values when temperatures under 18°C are measured. It originates from the idea that temperatures under 18°C ask for heating. Therefore, these contracts are only available in the winter months. In the USA, Japan and Europe, the CME offers HDD contracts in November, December, January, February and March. (Benth & Benth, 2013) In figure 2, the relationship is shown between the average temperature and the index. It is important to underline that figure 2 is only valid if the average daily temperature is under 18°C.

Figure 2: Relationship between average temperature (<18°C) and HDD Index. Source: Hoornaert, 2018

For the sake of clarity, it is essential to draw the attention upon the fact that the relationship described above between average temperatures on the one hand and HDDs on the other hand is not valid in the case when temperatures are above 18°C. This is a common mistake in some master’s dissertations. We will illustrate the argument by an example using HDDs, since CDDs are not traded in Europe. It is important to emphasize that the HDD index measures demand for heating. The index only obtains value with temperatures under 18°C. If temperatures above 18°C, the relationship between the HDD index and the average temperature can result in anomalies. Table 2 will illustrate the argument. Assume the daily temperatures in the first two weeks of November3 were measured and written down in table 2. The HDD index in week 1 would equal 4 and the HDD index in week 2 would equal 0. One would expect the average weekly temperature in week 1 to be lower, because of the higher demand for heating (higher HDD index). However, this is a false reasoning. The average weekly temperature is higher in week 1. The HDD index only measures demand for heating (not temperature) and in week 1, the demand for heating

3 CDDs are only available in the period November-March.

20 is higher, because on the first day, the temperature drops to 14°C, which results in a HDD index of 4.

Average HDD Week 푇(푡1) 푇(푡2) 푇(푡3) 푇(푡4) 푇(푡5) weekly Index Temperature

1 4 14°C 22°C 22°C 22°C 22°C 20,4°C 2 0 18°C 18°C 18°C 18°C 18°C 18°C

Table 2: Practical example relationship average temperature and HDD index. Source: Hoornaert, 2018

We want to draw the attention upon the fact that the example in table 2 is an exception. In our case study, all temperature measurements in the data sample are under 18°C in the period November-March. As a result, we can always claim that a higher HDD index coincides with a higher demand for heating and as a consequence a lower temperature. So, in our case study, figure 2 will always provide guidance.

4.1.2.3 Other Types of Exchange-Traded Weather Derivatives

There used to be plenty of other weather derivative contracts traded on the Chicago Mercantile Exchange. However, a lot of CME’s weather products have disappeared in silence. It is essential to draw the attention upon the fact that a significant amount of papers and literature studies is not up to date anymore. The experts in weather derivatives Benth & Benth (2013) mention different exchange-traded weather contracts and different weather stations that are not available anymore today. (Benth & Benth, 2013) Indeed, if we compare a document of CME (regarding the offer of weather derivatives and weather stations) in 2011 and a document published on the CME website in 2018, we can mainly determine two significant changes. (CME Group, 2011) (CME Group, 2015) (CME Group, 2018)

• The weather stations in Europe have reduced from eleven in 2011 to two in 2015. • A significant amount of contracts have disappeared.

First of all, there used to be weather derivative contracts in Europe on eleven locations: London, Paris Amsterdam, Berlin, Essen, Stockholm, Barcelona, Rome, Madrid, Oslo and Prague. (CME Group, 2011) Today, only weather derivative contracts on the weather stations of London Heathrow and Amsterdam Schiphol are available. (CME Group, 2018) Even Wikipedia has it wrong today. (Wikipedia, 2018) The website still mentions 11 European cities.

Secondly, many contracts have disappeared since 2011. There used to be weather derivative contracts based on snowfall, hurricane, rainfall, frost and wind speed indices. (CME Group, 2011) (Benth & Benth, 2013) However today, only the CAT, HDD and CDD contracts are offered at the CME. (CME Group, 2018)

4.1.2.1 Cumulative Amount of Temperature (CAT)

The Cumulative Amount of Temperature measures the cumulative amount of temperature over a vast period. This period can either be seasonal or monthly. (Benth & Benth, 2013)

푡2

퐶퐴푇(푡1, 푡2) = ∑ 푇(푡) (7)

푡=푡1

The CAT index is primarily created as the substitute for the CDD index in regions around the world in which the temperatures are rarely above 18°C. The CDD index has no use in these regions, because its index does not obtain values. (Benth & Benth, 2013) That is why CME offers CAT contracts for the summer months in Europe. In both Europe and the USA, HDD contracts in winter are available.

4.2 Hedging using Weather Derivatives

In this section, we elaborate on the most common weather hedging strategies and discuss the different industries with significant weather exposure. Moreover, we elucidate the empirical results of an important topic in corporate finance whether active risk management, including hedging using weather derivatives can add value to a firm. (Perez-Gonzalez & Yun, 2013). Thirdly, we compare the OTC and exchange-traded weather derivatives. This distinction has an impact on the magnitude of “location basis risk”. (Meteodat GmbH, 2004) We end this section by providing empirical results on weather hedging and showing two practical examples of weather hedging.

4.2.1 Weather Hedging Strategies using Options

4.2.1.1 General Strategies

In general, there are four main weather hedging strategies using weather options traded at the CME. An overview of the strategies can be found in table 3. It is important to recapitulate that the CDD index and temperature are positively related when temperatures are above 18°C, while the HDD index and temperature are negatively related when the temperatures are under 18°C (Cf. figure 2). HDD contracts are available from November through March. The CDD index in the USA and the CAT in Europe are available from May through September.

Hedge against… Option Strategy Industry application

Cold winter months: Call HDD Construction company November-March

Warm winter months: Utility company (electricity, Put HDD November-March gas)

Cold summer months: Beverage and ice-cream Put CDD or Put CAT May-September industry

Warm summer months: Call CDD or Call CAT Agriculture May-September

Table 3: Hedging Strategies. Source: Müller & Grandi, 2000

A comprehensive illustration of a long put CDD option to hedge against a cold summer is shown below in 4.2.1.2.

4.2.1.2 Comprehensive illustration

We opt for the following example to illustrate a weather hedging strategy (OTC) using weather options. In a cold summer, there is less demand for air-conditioning. Imagine an American utility company with air-conditioning as its core-business that wants to hedge itself against a drop in revenue due to a relatively cold summer. Unambiguously, the company has a volume risk due to the weather circumstances. It can hedge itself by buying weather derivatives which are written on weather indices, analogous to financial derivatives written over stock indices.

A put option can be bought by a trader who believes the index of the underlying element will go down under the agreed strike value. (Hull J. C., 2012) (The option's guide, 2017) The company can buy an American CDD put option for the period May-September. (Müller & Grandi, 2000). We assume the agreed strike index of the contract is 400 CDDs4 for the 5 month period. An illustration of the example can be consulted in figure 3.

Figure 3: Profit/Loss Diagram Weather Put Option. Source: Müller & Grandi, 2000.

A lower CDD index would imply that the demand for cooling is limited. As a result, the demand for air-conditioning would be lower. If the actual CDDs would now be under 400 CDDs, the weather option is in the money. (Hull J. C., 2012) (Investopedia, 2018) However, this does not mean there will be a profit. Therefore, we should look at the premium of the option and calculate the break-even index.

If the total cost of the option premium payments is $500.000, the tick size is 1 CDD and the index value per tick size is $10.000, then the break-even index would be calculated as follows: (Müller & Grandi, 2000)

퐼푛푑푒푥 푉푎푙푢푒 ∗ (퐶퐷퐷푠푡푟푖푘푒 푖푛푑푒푥 – 퐶퐷퐷푏푟푒푎푘푒푣푒푛) – 푝푟푒푚𝑖푢푚 = 0

Here this would be:

4 CDDs in this contract are measured in Degrees Celcius

$10.000 ∗ (400 – 퐶퐷퐷푏푟푒푎푘푒푣푒푛) – $500.000 = 0

퐶퐷퐷푏푟푒푎푘푒푣푒푛 = 350

If the CDD index over the period would be under 350, then the company makes a profit on the weather derivative. Assume now a summer with an exceptionally low CDD index of 180 over the period. How much would the profit be for the utility company? This would result in a profit of:

$10.000 ∗ (400 − 180) – $500.000 = $1.700.000

4.2.2 Weather-Exposed Industries

The incentives for hedging using conventional derivatives also apply to hedging using weather derivatives. However, the industries experiencing a weather exposure have distinctive characteristics. (Müller & Grandi, 2000) Weather derivatives may prove fruitful for hedging weather exposure for outdoor amusement parks, open-air concerts, soft drink producers, utility companies and agriculture. In what follows, we will elaborate an extensive overview of the industries that have an exposure to volume volatility, caused by weather variables.

Heineken, Coca-Cola and The Oktoberfest are three examples of companies/organizations who use weather derivatives to hedge against their weather exposure. (The Economist, 2003)

4.2.2.1 Outdoor Amusement Parks/Events

Open air amusement parks like Legoland and Disneyland are very dependent on the weather circumstances and nature events. In 1999, Disneyland Japan issued a 200 million “catastrophe” bond to protect the park against earthquakes in the region. (Financial Times, 2006) In the paper “The effect of rain on the decision to visit a theme park”, the researchers found that fewer people visit Korean theme parks when it is a rainy or cold day. (Kang & Moon, 2012) They also concluded that the weather forecasts have a significant impact on the number of

25 visitors. This example proves that outdoor amusement parks are dependent on the following weather variables: rain and temperature.

Another example of an amusement event that can expect a loss of revenues due to rainy or cold days is The Oktoberfest. According to The Economist, The Oktoberfest hedges against “unusual temperature swings” using weather derivatives. (The Economist, 2003)

4.2.2.2 Ski Resorts

Ski resorts face a massive weather risk. (Sileo, 2012) A crucial determinant in the revenues of ski-resorts is the amount of snow in the ski-area. As a result, the revenues are linked with the weather conditions, more specifically with the snow quantity. (Sileo, 2012) A winter with an insufficient level of snow can cause a drop in: • winter sports visitors for hotels • rental of skiing equipment • sales of ski-passes (Sileo, 2012)

The paper “Managing risk of ski resorts with snow options” can serve as an example of hedging weather risk using weather derivatives. (Sileo, 2012) The author emphasizes the fact that scarcity of snow can lead to a catch-22: if revenues in a given year are insufficient to finance new investments, these investments are postponed and consequently will result in a loss of competitiveness and quality of the accommodation. As a result, this loss of competitiveness can lead to fewer tourists and again a decrease in revenue. In this paper, the author suggests a strategy to hedge this weather risk to neutralize the financial effects of an insufficient level of snow. They apply their strategy on the ski lift operator Paganella Spa, located in Andalo, which is an Italian ski resort. Andalo was specifically chosen, because the area had been affected by a scarcity of snow in the recent years. Similar to conventional hedging, an OTC put option is bought to hedge against a downward trend on the underlying index. In this specific case, the underlying index is the cumulative snow level in the given region. If the index passes under the strike level, the buyer will receive payoffs. The payoff was calculated as the difference between the contracted cumulative snow strike level and the actual cumulative snow index. (Sileo, 2012)

4.2.2.3 Electricity Utilities

Staffell & Pfenninger (2017) found that as the energy sector is moving towards renewable energy production, the supply and demand of electricity has become more dependent on

26 weather variables. The shift from fossil fuels towards wind and solar power has increased the volatility of energy production in the UK. To quote the researchers: “Wind and solar power are dependent on weather and thus variable (or intermittent), fluctuating at timescales ranging from minutes to hours to multiple days, as well as across years and decades.” (Staffell & Pfenninger, 2017)

However, not only the supply of electricity is affected by weather variables, but also demand by households and industrial firms. The French company SOCCRAM, as the first European company to use weather derivatives in March 1999, (Gide Loyrette Nouel, 2001) (Müller & Grandi, 2000) can serve as an example of a firm that hedged itself against a warm winter. The aim was to safeguard the company against a drop in the demand for electricity as a consequence of excessively warm weather in the winter. (Müller & Grandi, 2000) (Blom, 2009)

4.2.2.4 Construction & Manufacturing

The following two examples will illustrate the impact of adverse weather conditions in the construction industry.

In 2005, CME launched a new contract specifically for the Netherlands. (CME Group, 2011) In order to hedge the revenues of Dutch construction companies, CME launched frost contracts. The rationale behind was as follows: if there would be serious frost in the Netherlands, the construction workers would be contractually exempted from work on those days. As a consequence, the construction companies face a frost risk. (CME Group, 2011).

More recently, an article in Bloomberg described the impact of the disruption of the “Beast from the East” storm on the construction activity in the UK. (Bloomberg, 2018) According to Financial Times (6 March 2018), this Beast from the East caused the burst of water pipes in the UK. Consequently, the manufacturing plants of Jaguar Land Rover and Cadbury were halted for three days in a row. (Financial Times, 2018)

It is clear that the operations of construction and manufacturing firms have a significant exposure to weather risk.

4.2.2.5 Agriculture

As an illustration of the agriculture’s weather exposure, we will elaborate specifically on the Champagne Industry. In 2016, 268 million bottles of Champagne were produced. (The Comité Champagne, 2018) The most critical and costly element of Champagne are the grapes. The weather risk originates precisely from these grapes. The quality of the vintage of the grapes significantly influences the quality and the quantity of the wine production in a given year. In a paper by Yandell (2012), the author determines whether a weather hedging strategy can reduce the grape harvest risk in the Champagne region. Frost, extreme precipitation and temperature can have adverse effects on a vintage. (Yandell, 2012) The weather risk in this specific case (the direct relationship between weather circumstances and vintage values in a given year) can be hedged using weather derivatives. The author analyzed the link between the historical grape harvest data and the corresponding historical temperatures. To minimize the harvest value variance, a protective collar hedging strategy was applied. (Yandell, 2012) The outcome of the weather hedging strategy was a reduction of the average harvest value variance of 31,32% across all years of the sample. What’s more, the protective collar hedging strategy also increased the mean harvest value with 7,86%. However, in this paper, the author did not include the cost of the hedging strategy. It is possible that the transaction costs of the strategy would offset this significant decrease in harvest value variance. The author explains this choice by pointing out that there is no consensus in the literature about a pricing model for weather derivatives. (Yandell, 2012)

The abovementioned paper illustrates the significant weather risk of the wine farmers and more generally the agriculture. Moreover, even nations with a very homogenous climate can face a significant weather risk in its agricultural sector.

The Republic of Malawi can serve as an example. According to the World Bank, 38% of the GDP of Malawi is dependent on rainfall. (World Bank, 2012) However, the rainfall has been extremely volatile in the past. In 2005, there has been a severe drought in the country, leading to millions of Malawian farmers in need of food aid. (World Bank, 2012) In the aftermath of the crisis of 2005, the World Bank supported the Government of Malawi in 2008 with setting up a “weather derivative to shift a portion of the risk of adverse weather conditions to the international financial markets. The World Bank acted as an intermediary between Malawi and reinsurance companies or investment banks for the transaction.” (World Bank, 2012) The underlying index of the contract was a rainfall index, which reflected the link with the maize

28 production. The trigger in the contract was a 10% lower rainfall than the historical average. In this case, there would be a payout to the government of the Republic of Malawi with a maximum of $4,4 million. (World Bank, 2012)

4.2.2.6 FMCG

In the entire value chain of the FMCG industry, sales are influenced by the weather. (Jan Erik Blom). We already know that good weather has a significant effect on stock exchange returns (Hirshleifer & Shumway, 2003). Moreover, the weather circumstances can trigger the human mood (Wei & Cao, Stock market returns: A note, 2005). In a paper by K.B. Murray et al. researchers found that sunlight reduces the negative mood and consequently has a positive effect on willingness to pay and ultimately on consumption. (Murray, Muro, Finn, & Leszczyc, 2010)

The British Meteorological office has calculated that if daily temperature increases with 3°C, the daily beer consumption increases with 10% (Lloyd Insurance, 1999) In June 2006, the world’s biggest supermarket chain, Wal Mart, decreased its sales forecasts due to adverse weather conditions. An abnormal cold summer negatively impacted Wal Mart’s sales of air conditioning systems and outdoor swimming pools. (Murray, Muro, Finn, & Leszczyc, 2010) In what follows, we will cite two financial statements of large FMCG companies that have recently reported about unexpected weather conditions.

• “Sports Directs has warned that annual profits in 2016 will be up to £40m lower than expected because of fierce high street competition and dreadful weather conditions in the run-up to Christmas.” (Financial Times, 2016) • CEO Unilever Paul Polman: “Growth in the third quarter was adversely affected by poorer weather in Europe compared with last year and natural disasters in the Americas.” (Financial Times, 2017) (Unilever, 2017)

4.2.2.7 Banks and Insurance Companies

The biggest insurance company in Switzerland, Zurich Insurance reported a profit warning on January 2016. They warned for a $275m loss due to winter storms and high rainfall in the British Isles (Financial Times, 2016) It is clear that also banks and insurance companies cope with profit fluctuation due to weather circumstances. Banks, for example, can have investments in a wind-mill park. If those banks

29 are dependent on the sensitive cash flows of the wind-mill park, the banks also face an indirect weather risk. (Blom, 2009). Lastly, Geman & Leonardi (2005) add that AIG, Swiss Re, ING Insurance are the most active insurance companies in the weather derivative industry, just as the financial institutions Goldman Sachs, Merill Lynch and Société Générale. (Geman & Leonardi, 2005)

4.2.3 OTC vs Exchange-Traded Weather Derivatives

Weather derivatives can either be bought OTC or exchange-traded. The first weather derivatives were primarily OTC contracts. Only in 1999, the first exchange-traded weather derivative contracts were available on the CME. From that point, the contracts became standardized and the market started booming. (Blom, 2009) The advantage of the exchange- traded weather derivatives is that “a clearinghouse substitutes itself as central counterparty to all transactions that its members agree to submit for clearing. The use of a clearinghouse has the potential to mitigate each of the types of counterparty risk associated with OTC derivatives.” (Bank Of International Settlements, 1998)

Moreover, since most OTC derivatives are executed via telephone and as a result, bilateral negotiated, the hedging strategy in the case study will make use of exchange-traded weather derivatives, traded on the Chicago Mercantile Exchange. (Bank Of International Settlements, 1998) (Hull J. C., 2012) This offers the advantage that the hedging strategy is not dependent on another market participant. However, there is one noticeable drawback from using the exchange-traded contracts of CME. That is the existence of location basis risk. Professor John C. Hull (2012) describes basis risk as “the difference between the spot price and the futures price.” He states that “hedging is often not quite as straightforward. Some of the reasons are as follows: 1. The asset whose price is to be hedged May not be exactly the same as the asset underlying the futures contract. 2. The hedger May be uncertain as to the exact date when the asset will be bought or sold. 3. The hedge May require the futures contract to be closed out well before its expiration date.” (Hull J. C., 2012)

The three aforementioned reasons are crucial in the formation of basis risk in the case of weather derivatives. However, weather derivatives give the first reason a slightly other dimension. CME’s weather derivatives are only written over a few cities in the US and over Amsterdam and London in Europe. As a consequence, the underlying temperature index of an exchange-traded contract will not correspond and correlate perfectly with the temperature index of the targeted weather-exposed region. This increases the basis risk. In the context of weather derivatives, we can refer to this specific type of basis risk as “location basis risk.” (Meteodat GmbH, 2004) (University of Warschau, 2016)

For example, a French grape farmer located in Epernay wants to hedge against a mild summer using weather derivatives. He has two options: He can close an OTC weather derivative contract with a French insurer or buy an exchange-traded contract on the weather station of either London or Amsterdam. In the OTC contract, the two parties agree that the temperature in Epernay is the underlying variable of the contract. In this case, the “location basis risk” is eliminated entirely. However, if the French farmer chooses a standardized weather derivative of CME, he faces a location basis risk. In this case, he needs to choose between either London or Amsterdam as the location of measurement of temperature. Here, the correlation between the temperature in London or Amsterdam, on the one hand, and the temperature in Epernay on the other hand will be decisive for the magnitude of the location basis risk. Although the magnitude of the location basis risk is to be minimized, it is crucial to draw the attention upon the fact that “there is often a trade-off between basis risk and the price of the weather hedge.” (Blom, 2009) Big cities often coincide with popular weather derivatives markets. Cities like New York and London are traded more frequently like cities as Amsterdam, Sacramento or Dallas. As a result, the contracts in metropolises are more liquid.

In the case study, London is chosen as the weather station of the weather derivative contract. This will be discussed in chapter five.

4.2.4 Empirical Results on Hedging using Weather Derivatives

Perez-Gonzalez & Yun (2013) examine if weather risk management, using weather derivatives can lead to an increase in firm value. The authors apply a data analysis on a sample of 203 generators and distributors of electricity and natural gas, since the energy sector is one of the most weather-sensitive sectors. (National Research Council, 2003) The demand for electricity and gas is highly related to weather conditions. In a mild summer, there will be less demand

31 for air conditioning. In a warm winter, there will be less demand for heating. In this respect, adverse weather conditions can have a huge impact on the financial performance of utility providers. The results of the paper can be summarized into four main arguments. The first finding is about the capital structure and valuation. Firms with a significant exposure to weather risk have a remarkable lower valuation in the absence of weather hedging strategy. The difference is around 4% for firms that face the highest weather exposure. What’s more, firms that do not hedge their weather exposure use less leverage and pay fewer dividends than firms who do hedge their weather exposure. (Perez-Gonzalez & Yun, 2013)

The second finding is that firms with a high weather exposure before 1997 have a higher likelihood to use weather derivatives after 1997. In other words, historical weather exposure can predict the use of weather derivatives. (Perez-Gonzalez & Yun, 2013)

The third finding is the most relevant for this master’s dissertation. The authors conclude that the use of weather derivatives as a weather risk management tool results in higher valuations of the 203 energy companies in the sample. This hedging strategy increases the market-to- book ratios with a significant 6%. (Perez-Gonzalez & Yun, 2013)

The last finding is that the weather hedging strategy leads to higher investment levels. The authors suggest the idea that this might result from smoother cashflows which obviate credit. (Perez-Gonzalez & Yun, 2013)

4.2.5 Practical Examples of Hedging using Weather Derivatives

4.2.5.1 Example of an OTC Weather Derivative

To illustrate the details and mechanics of a weather derivative contract, we have included the example of KeySpan. In the annual report of KeySpan 2006, we can read the following:

“In 2006, we entered into heating-degree day put options to mitigate the effect of fluctuations from normal weather on KEDNE's financial position and cash flows for the 2006/2007 winter heating season - November 2006 through March 2007. These put options will pay KeySpan up to $37,500 per heating degree day when the actual temperature is below 4,159 heating degree days, or approximately 5% warmer than normal, based on the most recent 20-year average for normal weather. The maximum amount KeySpan will receive on these purchased put options is $15 million. The net premium cost for these options is $1.7 million and will be amortized over the heating season. Since weather was warmer than normal during the fourth

32 quarter of 2006, KeySpan recorded a $9.1 million benefit to earnings associated with the weather derivative.”

(Perez-Gonzalez & Yun, 2013) (KEYSPAN Corporation, 2006)

4.2.5.2 Example of an Exchange-Traded Weather Derivative

In table 4, the seasonal CME exchange-traded European HDD weather option is presented with its specific features and specifications. (Chicago Mercantile Exchange Group, 2018)

Table 4: Contract specifications of an exchange-traded weather option at CME.

Source: Chicago Mercantile Exchange Group, 2018.

The London CME European HDD weather option allocates a £20 value to the tick size of one HDD. This contract can be traded during the periods November-March or December- February.

4.3 Pricing Models for Weather Derivatives

The objective of this master’s dissertation is not to determine which pricing model is the most accurate and suitable for weather derivatives, but rather to give an overview of the existing literature on weather derivatives and to find out if a weather hedging strategy would have a positive impact on AB InBev’s financial performance.

The question on how to price weather derivatives is a severe polemic in the academic literature, without a straightforward conclusion. The objective of the lion’s share of the academic literature about weather derivatives is to determine an adequate pricing model. (Benth & Benth, 2013) (Brody, Joanna, & Zervos, 2002) (Broni-Mensah, 2012) (CME, Labuszewski, Nyhoff, Co, & Peterson, 2010) (Campbell & Diebold, 2005) (Jewson & Brix, 2005) (Wei & Cao, 2004)

Throughout the years, academia have developed a broad range of modelling and pricing tools. However, questions remain whether the underlying assumptions of these models are in line with the specific characteristics of weather derivatives. Campbell & Diebold (2005) draw the attention upon the fact that standard valuation methods, like the Black-Scholes formula, are not valid in the case of weather derivatives. The Black-Scholes theory includes the no-arbitrage assumption, which means that there are no-arbitrage opportunities in the market. The put-call parity in option pricing most commonly illustrates the concept of no-arbitrage.

The put-call parity for a tradable asset is given by equation 8.

퐶 − 푃 = 푆 − 푃푉(퐾) (8)

With

퐶 = 푃푟𝑖푐푒 표푓 푡ℎ푒 푐푎푙푙 표푝푡𝑖표푛 푃 = 푃푟𝑖푐푒 표푓 푡ℎ푒 푝푢푡 표푝푡𝑖표푛 푆 = 푆푝표푡 푝푟𝑖푐푒 표푓 푡ℎ푒 푎푠푠푒푡 퐾 = 푆푡푟𝑖푘푒 푝푟𝑖푐푒 표푓 푡ℎ푒 푎푠푠푒푡

If the parity in equation 8 would not exist, then there would be an arbitrage opportunity in the market. Suppose that

퐶 − 푃 > 푆 − 푃푉(퐾) (9)

Then 퐶 − 푃 − 푆 + 푃푉(퐾) > 0 (10)

The situation in equation 10 would imply that implementing the strategy mentioned underneath would result in an incoming cashflow today, without corresponding outgoing cashflow in the future. (Matos, 2017)

A trader can sell the call option (+C), buy the put option (-P), buy the underlying asset over which the call and put option are written (-S) and borrow the present value of the strike price/index of the asset (+PV(K)). For the trader, this implicates an incoming cashflow (>0) in the beginning of the contract. On maturity date, the trader does not have an outgoing cashflow, so this resembles precisely the arbitrage opportunity and consequently the presence of a “free lunch”. (Broni-Mensah, 2012)

However, the weather is neither an asset nor tradable. (Geman & Leonardi, 2005) (Brody, Joanna, & Zervos, 2002) As a result, the no-arbitrage concept as shown above is invalid, which implies that the Black-Scholes formula cannot be applied on the pricing of weather derivatives. (Benth & Benth, 2013) As a result, the single approach to price weather derivatives is via statistical methods that can model the underlying variable. (Campbell & Diebold, 2005) (Wei & Cao, 2004)

In the case study, two valuation methods that are frequently used in the weather derivative literature will be applied: Burn analysis and Monte Carlo Simulation. (Benth & Benth, 2013) (Jewson & Brix, 2005) (Blom, 2009)

4.3.1 Burn Analysis

The first pricing method is the burn (rate) analysis5. Burn analysis is based on the concept of the rational expectation hypothesis. (Benth & Benth, 2013) This method simply calculates the expected payoff of a weather option as the average of the payoffs in the past. (Jewson & Brix, 2005) We assume continuous compounding interest. (Hull J. C., 2012)

푛 1 퐸푥푝푒푐푡푒푑 푃푎푦표푓푓 = ∑ 푝 (11) 푛 i i=1

(Benth & Benth, 2013)

5 The standard reference for this method in the context of weather derivatives is Jewson & Brix (2005)

With 푝푖 = 푝푎푦표푓푓 𝑖푛 푦푒푎푟 𝑖

푝푖 = 푀푎푥(푆푖 − 퐾; 0) 푓표푟 푎 푐푎푙푙 표푝푡𝑖표푛

푝푖 = 푀푎푥(퐾 − 푆푖; 0) 푓표푟 푎 푝푢푡 푂푝푡𝑖표푛

The symbol 푆푖 refers to the spot index in year i. The spot index can be the HDD index, the CAT index or the CDD index for example. The symbol 퐾 is the strike index.

Since,

푂푝푡𝑖표푛 푃푟푒푚𝑖푢푚 = 푃푉(퐸푥푝푒푐푡푒푑 푃푎푦표푓푓푠)

The option premium calculated via Burn Analysis can be defined by equation 12:

푛 1 푂푝푡𝑖표푛 푃푟푒푚𝑖푢푚 = 푒−푟푇 ∑ 푝 (12) 푛 푖 푖=1

(Matos, 2017)

With

푟 = 푟𝑖푠푘 푓푟푒푒 푟푎푡푒 푇 = 푇𝑖푚푒 푡표 푚푎푡푢푟𝑖푡푦

It needs to be said that the burn analysis as described in equation 12 is a simple and straightforward method which does not yield exact prices of weather derivatives, but instead delivers an estimation of the “magnitude of the price of the instrument under analysis.” (Campbell & Diebold, 2005) (Benth & Benth, 2013)

4.3.2 Monte Carlo Simulation

The second method that is frequently used in the pricing of weather derivatives is the Monte Carlo simulation. Monte Carlo simulation can be defined as:

“The imitation of a real-world process by using random number generation and probability distributions to randomly generate events that occur in the system. Running the simulation model results in a statistical observation of the performance of the system.”

(Maenhout, 2018)

First, the probability distribution of the index is to be determined. An extensive database is of vital importance to have a significant distribution model. When the distribution model is tested

36 and found significant, the Monte Carlo simulation is executed to calculate different spot indices using random number generation. This spot index can either be the HDD index, the CAT index or the CDD index.

The randomized spot index to calculate the payoff is mathematically defined in equation 13.

푥(푡) = µ + 휎 ∗ 푤푡 (13)

The variables µ and 휎 in the equation 13 result from the probability distribution of the index. 푤푡 is a random variable from the adequate probability distribution.

The expected payoff is then:

푛 1 퐸[푔(푥(푡)] = ∑ 푔 (푥(푡)) (14) n t=1

The function 푔( ) in equation 14 represents the payoff expression. The payoff expression can be defined as:

푔(푥(푡)) = 푀푎푥(푥(푡) − 퐾; 0) 푓표푟 푎 푐푎푙푙 표푝푡𝑖표푛

푔(푥(푡)) = 푀푎푥(퐾 − 푥(푡); 0) 푓표푟 푎 푝푢푡 표푝푡𝑖표푛

With

푥(푡) = 푅푎푛푑표푚𝑖푧푒푑 푆푝표푡 퐼푛푑푒푥

퐾 = 푆푡푟𝑖푘푒 퐼푛푑푒푥

With this random variable 푤푡, equation 13 can be run for a large number of times, until the average of 푔(푥(푡)) is relatively stable.

Again, since the option premium equals the present value of the expected payoff, then the option premium equals:

푂푝푡𝑖표푛 푃푟푒푚𝑖푢푚 = 푃푉(퐸푥푝푒푐푡푒푑 푃푎푦표푓푓)

푛 1 푂푝푡𝑖표푛푃푟푒푚𝑖푢푚 = 푒−푟푇 ∑ 푔 (푥(푡)) (15) 푁 t=1

4.4 Alternatives for Weather Derivatives

Two other instruments can be identified for hedging against a weather exposure. The two alternative weather risk management tools have the same objective: minimizing the overall impact of adverse weather conditions. However, if we zoom in on the features of the alternatives, we can determine important distinctions.

4.4.1 Insurance

Traditional insurance contracts are used to mitigate the impact of “rare weather events such as extreme cold, heat, hurricanes or floods.” (Kent Business School, 2015) These events are rare, but when they appear, they result in significant impact on the financial performance of a company. Moreover, in the case of traditional insurance, the insured has to prove that there has been physical damage to his crops, harvests, real estate or production plant. (Allianz Global Corporate & Specialty, 2016) This is a time consuming and subjective activity. (Kent Business School, 2015)

With a hedging strategy using weather derivatives, it is not necessary to show any damage, significant higher costs or loss in turnover. These contracts cover low-risk and high-probability events. (Kent Business School, 2015) A weather variable functions as the underlying element in this contract. (Müller & Grandi, 2000) Moreover, it allows businesses to hedge volumes which cannot be insured. A farmer can send pictures of his damaged crops to the insurance company on the one hand, but the beverage industry cannot send pictures of empty terraces on rainy days to the insurer on the other hand.

As a final argument, the settlement of weather derivatives is objective. The payment is based on the objective observation of the underlying weather variable of the contract and consequently, the pay-out to the buyer of the weather contract will happen fast and efficient.

4.4.2 Natural Hedging

The second alternative constitutes operational decisions. Natural hedging refers to the reduction in risk exposure through strategic operational changes and adjustments, rather than using derivatives. (Matos, 2017) These strategic operational changes can include “diversification across currency zones, operational matching of revenues and expenditure.” (European Commission, 2008)

In the Harvard Business Case of January 2013, the foreign currency hedging policy of Airbus is discussed. (Harvard Business School Case Collection, 2013). In this case, the author explains that Airbus is pursuing a natural hedging strategy. The dollar is the central currency of commercial aircraft transactions, since the aeroplane industry bills their customers in dollars. As a consequence, Airbus’ revenues are expressed in dollars. However, Airbus, a European company, has a significant portion of its cost reported in euros in their financial statements. (Hoornaert, 2017) These European roots are historical and a result of the “Franco-German-Spanish majority shareholding.” (Harvard Business School Case Collection, 2013) As a result, Airbus has a significant dollar/euro exchange rate exposure. To hedge against this exchange rate exposure, Airbus trades in conventional foreign exchange derivatives. However, they also apply a natural hedging strategy, which is described in the next paragraph. (Harvard Business School Case Collection, 2013)

Airbus is planning to have non-European sourcing be 40% of its total sourcing by 2020. This process of alignment (currency-wise) between costs and revenues is called “natural hedging.” (Harvard Business School Case Collection, 2013)

The case of Airbus is an excellent illustration of the origins and the implementation of a natural hedging strategy. This natural hedging strategy can also be applied to hedge against the weather. If a local Belgian beer producer has a significant weather exposure, it can decide to diversify his area of distribution. Suppose the beer producer suffers from low sales in winters. With a decent “natural weather hedging” strategy, the company will enter new markets with a complementary climate. Consequently, the company will sell beer all year round and will neutralize its weather risk.

Sometimes managers underestimate the power of natural hedging. The cost is sometimes significantly lower than financial hedges. (McKinsey, 2010)

Chapter 5. Case Study AB InBev

5.1 Objective of the Case Study

The objective of the case study is to investigate whether there is a significant relationship between the beer sales volume of AB InBev’s brands in West-Europe on the one hand and weather variables on the other hand. Subsequently, we design a hedging strategy using exchange-traded weather derivatives of the CME to minimize the weather exposure.

In 5.2, the company AB InBev is presented. The data-analysis starts in 5.3. In the first section of 5.3, we remove a downward trend from the beer sales. This downward trend in the data corresponds to the European phenomenon of a declining beer consumption. (European Commission, 2018) In 5.3.2, we will choose either Amsterdam or London as the weather station of our hedging strategy. Ultimately, in 5.3.3, three different weather variables are examined on their relationship with AB InBev’s quarterly beer sales6 in West-Europe. Lastly, in 5.4, we calculate the option premium payments for our weather hedging strategy and present the results.

Our specific interest in AB InBev for setting up a weather hedging strategy originates from two primary motivations. First, the literature indicates that the FMCG-industry and more specifically, the beverage industry has a significant weather exposure. (Allianz Global Corporate & Specialty, 2016) (Bloomberg, 2018) (The Economist, 2003) (Lisson, Brunhart, & Gasteige, 2013) (Blom, 2009) (Müller & Grandi, 2000) Secondly, there is a panoply of statements in the quarterly reports of AB InBev regarding the impact of weather variables. These statements indicate that the financial performance was (either positively or negatively) affected by weather variables like temperature and rainfall.

In what follows, we will quote some of the statements from the quarterly reports of AB InBev, mentioning the weather as an explanatory variable of their financial performance:

“We estimate the beer industry grew by 0.5% in the quarter, driven by improved weather and some easing in macro-economic headwinds.” (Quarterly report 2013 AB InBev, second quarter)

6 In litres

“In the United States, we estimate that industry selling day adjusted STRs declined by 2.8% in both 2Q13 and HY13, with the decline in 2Q13 due to poor weather.” (Quarterly Report AB InBev 2013, second quarter)

“Industry volumes in 2Q11 were impacted by poor weather, especially in the center of the country.” (Quarterly Report AB InBev 2011, third quarter)

“Own beer volumes in Belgium grew 3.2% in 2Q11 on the back of good weather and positive industry dynamics.” (Quarterly Report AB InBev 2011, second quarter)

“We expect a difficult first quarter as a result of an earlier Carnival and poor weather given lower temperatures and high rainfall.” (Quarterly Report AB InBev 2018, first quarter)

“Revenue per hl grew by 2.1% in the quarter, with the benefit of favorable brand mix being partly offset by unfavorable regional mix driven by poor weather.” (Quarterly Report AB InBev 2016, first quarter)

“Beer volumes benefitted from favorable weather.” (Quarterly Report AB InBev 2015, third quarter)

“In Brazil, beer volumes grew 1.1% as consumer spend continued to be under pressure from food inflation and September saw poor weather in key regions.” (Quarterly Report AB InBev 2008, third quarter)

5.2 Company Presentation

5.2.1 History of the company

The company Anheuser-Busch InBev is formed through the merger of two international brewing giants in 2008: InBev (Belgium-based), and Anheuser-Busch (US-based). (AB InBev, 2017) (Wikipedia, 2018). The structure of the firm is visionally supported by figure 4. Subsequently, InBev itself was formed through a merger of Ambev and Interbrew. These beer groups resulted also from the process of various consecutive mergers and acquisitions. In 1988, when Brouwerij Artois merged with the Walloon-based brewer Piedboeuf, Interbrew was formed. (Wikipedia, 2017) The brewery Artois bought in 1717 the Den Hoorn brewery. The Den Hoorn Brewery is known for its world-famous beer Stella Artois, which already existed in 1366. (AB InBev, 2018) Interbrew began expanding internationally with the acquisition of the notable Canadian brand Labatt. Labatt, at the time, was not much smaller than Interbrew. After the merger in 2004, the company Interbrew was considered a multinational, with both Canadian and Belgian roots. Ambev was the result of the merger in 1988 of two Brazilian

41 companies: Brahma and Antarctica. Later on, in respectively 2006 and 2012, the group Ambev acquired Cerveza Ouilmes and Cerveria Nacional Dominicana. The corporate structure of the brewery Anheuser Busch InBev prior to the merger with SAB Miller in 2015 is presented in the Figure 4.

Anheuser- Busch Inbev

Anheuser Inbev Busch

Anheuser- Ambev Interbrew Harbin Busch

Antarctica Brahma Piedboeuf Labatt Artois

Figure 4: The M&A history of Anheuser-Busch Inbev, pre-2015. Source: AB InBev 2018.

In 2014, AB InBev already conquered the position of the world’s largest brewer as measured by market share. At that time, the market share was 20,7%, followed by SAB Miller with 9,7%. (Wall Street Journal, 2015) In October 2015, AB InBev announced a successful bid to acquire the second biggest brewery SAB Miller for $107 billion. (Bloomberg, 2015) Shareholders from both companies approved the merger on 28 September 2016. The deal closed on 10 October 2016. (Wikipedia, 2018)

5.2.2 Core Business

Today, Anheuser-Busch InBev NV/SA is world’s largest brewer. (Financial Times, 2018) It is a Belgium-based company engaged in the brewing industry with a portfolio of over 500 beer brands. (Financial Times, 2018) The most famous global brands are Stella Artois, Budweiser, Corona, Leffe, Beck’s, Hoegaarden. Their portfolio also includes “local champions”, as there are: Bud Light, Skol, Brahma, Quilmes, Modelo Especial, Sedrin, Klinskove, Cass and Jupiler. (Thomas Reuters, 2018) (AB InBev, 2017) What’s more, the company also has a soft drink department with both own production and agreements with PepsiCo regarding the bottling and the distribution of its various subsidiaries (e.g. Ambev). Pepsi, 7UP and Gatorade are examples of brands which are distributed through these agreements. (Thomas Reuters, 2018) AB InBev has operations in over 50 markets and sell their beers in over 150 countries. Their operations are focused on 9 geographical zones. Africa, APAC North, APAC South, Europe, Latin America COPEC, Latin America North, Latin America South, Middle Americas, North

America. (AB InBev, 2017) In the data analysis, the focus will be on Europe and more specifically on data from West-Europe.

5.2.3 Financial Performance

AB InBev is the Belgian company with the largest market capitalization. As of 5 April 2018, the market capitalization was $165 billion. (Thomas Reuters, 2018) However, the stock price is at its lowest point in four years. If we look back at 18 October 2017, the market capitalization was more than $216 billion. (Thomas Reuters, 2018) AB InBev has been suffering the last six months financially due to the increasing competition of craft beers combined with slow growth in the US beer market in general. Sales in the US of popular brands like Budweiser and Bud Light have declined, “while craft and lower-alcohol beer sales are on the rise.” (Bloomberg, 2018) (Financial Times, 2018)

5.3 Data-Analysis

5.3.1 Beer Sales

Since we do not have daily, weekly or monthly sales data to our disposal, the beer sales data used in this case study are retrieved from the quarterly financial statements of Anheuser-Busch InBev NV/SA. The choice for West-Europe over Europe as a whole was incentivized by the objective to minimize the location basis risk.

According to the Encyclopedia Britannica, a clear contrast exists between the climate configuration of West-Europe and East-Europe. “The division is made by a line linking the base of the peninsula of Jutland (covering most parts of Denmark) with the head of the Adriatic Sea.” (Encyclopedia Britannica, s.d.) This corresponds largely to AB InBev’s interpretation of their core market West-Europe, which includes Belgium, UK, The Netherlands, France, Germany, Italy, Luxembourg, Portugal and Spain. (AB InBev, 2017).

It is vital to focus on a geographical area with coherent and similar climate characteristics because of the fact that we have to link the beer sales volume to the weather variables, measured at one specific weather station. Only in the case of coherent and similar climate characteristics, we can minimize the location basis risk. (cf. 4.2.3) The magnitude of the location basis risk can be minimized by matching the weather station where the underlying index is measured with the location where the hedging company executes economical

43 activities or performs its business processes. (Meteodat GmbH, 2004) (University of Warschau, 2016)

In the case study, we will use quarterly beer sales data starting from the third quarter in 2005 until the first quarter in 2013. The choice for this period is logic since this specific period gives us quarterly reports about West-Europe separately. Starting from the second quarter in 2013, AB InBev started to report its financial numbers for Europe as a whole, including Ukraine and Russia. Furthermore, we stripped the data from any influences of mergers, acquisitions and divestments the company went through in that period. This is crucial to facilitate a thorough understanding of the company’s underlying performance. The quarterly reports of AB InBev allowed us to eliminate the impact of changes in currencies as well as the impact of acquisitions and divestitures. As a result, the beer sales volume does not swing due to acquisitions or divestitures.

In figure 5, AB InBev’s quarterly sales (in litres) of West-Europe are plotted. The numbers are to be interpreted in 1.000 hectolitres. The numbers cover the period starting from the third quarter of 2005 until the first quarter of 2013.

Figure 5: AB InBev’s quarterly sales (in litres) of West-Europe. Source: AB InBev Quarterly Reports 2005-2013.

We can break down this time series into two different components: a trend and seasonality. On the one hand, we see a clear seasonal pattern in the sales volume (in 1.000 hl). Furthermore, a downward trend in the time series can be determined. The linear trend in our data can be found via the following linear regression model:

̂ ̂ 푦̂푖 = 훽0 + 훽1푋푖 (Vyncke, 2016)

Using the Least Squares Method, we have obtained the linear regression model

푦̂푖 = 10386,954 − 105,874푋푖

With 훽̂0 = 10386,954 and 훽̂1 = −105,874

The downward trend determined by linear regression is plotted in figure 6. The linear regression has an 푅2 of 0,47. This means that 47% of the variance in the dependent variable quarterly beer sales is predictable through the variable “Quarter.” The downward linear trend is significant on the 1% significance level. The regression equation and the 푅2 are displayed in table 5.

Figure 6: The downward trend in AB InBev’s sales (West-Europe). Source: AB InBev Quarterly Reports 2005-2013

̂ ̂ Equation R Square Sign. 훽0 훽1 Linear7 0,470 0,000 10386,954 -105,874 Table 5: Linear regression of the downward trend

7 The independent variable is Quarter

What is the origin of this downward trend in beer sales? We found that this downward trend corresponds to a worldwide trend of declining beer consumption. In a report from the European Commission, we can read that there has been a decrease over the years in the alcohol consumption of Germany, Portugal, France, Greece and Italy. (European Commission, 2018) The Economist published an article in 2017 in which they claim that worldwide beer consumption shrinks by 1,8% per year. (The Economist, 2017) Moreover, the Financial Times published in June 2017 an article in which they state that in Europe “beer sales fell 1,8%, compared with a five-year average decline of 0,6%.” (Financial Times, 2017) Furthermore, the Financial Times adds that the decline of beer sales is caused by the revival of gin and the increasing impetus of craft beer. This “craft beer tsunami” has been eating into AB InBev’s sales in the recent years. (BBC Business, 2015) (Bloomberg, 2018) The conclusion is that the decline in volume sales coincides with the European trend of decreasing beer consumption. To make an adequate statistical analysis of the data, it is crucial to strip the underlying declining trend from the quarterly sales.

We have obtained the following linear regression model, using the Least Squares Method:

푦̂푖 = 10386,954 − 105,874푋푖

With 훽̂0 = 10386,954 and 훽̂1 = −105,874

A detrended data series is of the form:

퐷푒푡푟푒푛푑푒푑 푑푎푡푎푠푒푟𝑖푒푠 = 푦푖 − 훽̂1푋푖 = 훽̂0 + 휀 (16)

Since,

푦̂푖 = 훽̂0 + 훽̂1푋푖

훽̂1푋푖 = 푦̂푖 − 훽̂0

This results in:

푦푖 − 훽̂1푋푖 = 푦푖 − ( 푦̂푖 − 훽̂0) = 훽̂0 + 휀

Now we have the detrended time series.

훽̂0 + 휀 = 10386,954 + 휀

The detrended time series is plotted in figure 7. Since the trend is completely removed from the time series, it is evident that we can model a regression equation with 푅2 = 0 and with

푆푙표푝푒 = 훽̂1 = 0. The corresponding regression equation with 푆푙표푝푒 = 훽̂1 = 0 is plotted in table 6.

̂ ̂ Equation R Square Sign. 훽0 훽1 Linear8 0,000 1,000 10386,954 0,000

Table 6: Linear Regression of Detrended Sales

Figure 7: Detrended time series

5.3.2 Choice of Weather Station

In 4.2.3, we elaborated on the difference between OTC and exchange-traded contracts. In this case study, a hedging strategy using exchange-traded weather derivative contracts from CME will be used. OTC contracts are ideal to decrease the credit risk and the dependence on another market party to close the contract. (Bank of international settlements, 2012) On an exchange, different strike prices can be chosen in a time efficient manner. CME used to offer contracts from 11 European cities.

8 The independent variable is Quarter

However, today CME only offers weather derivatives contracts for two European cities: Amsterdam Schiphol and London Heathrow. (Chicago Mercantile Exchange, 2018).

In this case study, the further development of the weather hedging strategy will be based on data from London Heathrow. This choice has its origin in the following four reasons:

• The correlation between the London temperatures and the Amsterdam temperatures is 0,994. As a result, it is not vital to run the analysis twice since the data are very similar. • The Pearson correlation coefficient for the two locations and the detrended sales are both significant. Both correlation coefficients are significant at the 0,01 level. • As we have discussed in 4.2.3, weather derivatives based on the weather station of London Heathrow are traded more often. Consequently, these contracts are relatively more liquid. (Blom, 2009) • The British Meteorological Office has a comprehensive database dating back to 1720. This reliable database provides information about daily temperature averages, sunshine hours and rainfall. We do not have a similar reliable database available for Amsterdam Schiphol.

5.3.3 Testing Variables

We ended last section mentioning four incentives to choose London over Amsterdam as weather station for the weather hedging strategy. In this section, we are going to test three variables on their relationship with quarterly beer sales volume: temperature, sunshine hours and rainfall.

5.3.3.1 Temperature

The temperature on a given day can be measured in three different ways. Minimum temperature, average temperature or maximum temperature. (Blom, 2009) In this case study, the objective is to find a relationship between beer sales and the temperature. Since most of the beer consumption happens throughout the day, it is evident to use maximum temperature as input variables. (Blom, 2009) In figure 8, the quarterly average of daily maximum temperatures in London Heathrow is presented.

It is essential to emphasize that the quarterly beer sales volume cannot be linked to the CDD, HDD or CAT index, because these indices are designed for a specific period only and not for a year as a whole.

Figure 8: Quarterly average of daily maximum temperatures in London Heathrow. Source: British Met Office

The relationship between the detrended quarterly sales volume (in 1.000 hl) and the quarterly average of daily maximum temperatures in London Heathrow is presented in figure 9.

Figure 9: Detrended Sales versus London Temperatures.

Source: British Met Office and quarterly reports AB InBev 2005-2013

The time series is filtered for the downward beer consumption trend. As a result, an adequate statistical analysis of the relationship between temperature and beer sales (in 1.000 hl) is possible. In figure 10, the relationship between the quarterly average of daily maximum temperatures in London Heathrow and AB InBev’s detrended quarterly sales (in 1.000 hl) of

West-Europe is plotted in a boxplot. There is a clear pattern visible for the naked eye. To support this preliminary hypothesis, we perform a correlation and regression analysis.

Figure 10: Boxplot of quarterly average of daily maximum London temperatures and Detrended Sales.

Source: British Met Office and quarterly reports AB InBev 2005-2013

In the correlation analysis, the Pearson correlation coefficient is calculated. The Pearson correlation coefficient is a measure of the linear relationship between two variables. (SPSS Tutorial, 2018) The Pearson correlation coefficient indicates a number between -1 and 1 with a value of 1 implying a perfect linear relationship between the values of X and Y. A value of 0 indicates the absence of a linear correlation between X and Y. In Table 7, the correlation between AB InBev’s detrended quarterly sales (in litres) of West-Europe on the one hand and the quarterly average of daily maximum temperatures in London Heathrow on the other hand is presented. We find a significant correlation of 0,792 between the detrended sales and the quarterly average of daily maximum temperatures measured at London Heathrow.

Correlation Number of data Sign. Pearson Correlation

TempLondon and 31 0,000 0,792 Detrended Sales

Table 7: Correlation between Detrended Sales and London Temperatures

Via curve estimation, we screen the data to analyse how temperature and sales are related. This can be either linearly, inversely, exponentially, quadratically or another type of regression model. In table 8, the results of the different regression analyses are presented. The corresponding curve estimations can be found in figure 11.

9 ̂ ̂ ̂ Equation R Square Sign. 훽0 훽1 훽2

Linear 0,627 0,000 8093,771 149,105

Inverse 0,682 0,000 12529,183 -28720,936

Quadratic 0,730 0,000 4812,095 629,187 -15,473

Growth 0,617 0,000 9,015 0,015

Exponential 0,617 0,000 8229,335 0,015

Table 8: Curve Estimation of Detrended Sales and London Temperatures

Figure 11: Curve Estimation of Detrended Sales and London Temperatures

9 The independent variable is Quarterly Average of Daily Maximum Temperatures in London

Although the fact that a quadratic regression model obtains the highest 푅2, it is important to determine if the model can be explained and clarified through common sense. (Vyncke, 2016) The regression model with the highest 푅2 does not necessarily coincide with the superior model.

The quadratic regression model results in the following equation:

̂ ̂ ̂ 2 푦̂푖 = 훽0 + 훽1푋푖 + 훽2푋푖 (17)

2 푦̂푖 = 4812,095 + 629,187푋푖 − 15,473푋푖

Equation 17 reaches its maximum at 20,33°C. It is hard to think of a rational explanation why beer sales gradually rise until 20,33°C and then suddenly start to drop.

Overfitting might be the origin of the problem. The data sample in this case study only comprises 31 data points, so it is possible that we are making an overcomplex model to give meaning to our data.

Linear and inverse regression models have better grounds to explain the relationship rationally. A linear regression model with an 푅2 = 0,627 is rationally understandable. As the temperature increases, the beer consumption increases to the same extent.

푦̂푖 = 훽̂0 + 훽̂1푋푖 (18)

푦̂푖 = 8093,771 + 149,105푋푖

the slope of the regression equation 18 equals 149,105.

푑푦̂푖 = 훽̂1 = 149,105 푑푋푖

This can be interpreted as follows: If temperature increases with one degree Celcius, the beer sales in West-Europe will go up with 149,105 hectolitres. This is the equivalent of almost 15 million litres beer.

An inverse regression model with an 푅2 = 0,682 can also be clarified using common sense. As the temperature is increasing, the beer sales gradually increase as well, but to a lesser extent. An inverse regression model has the following equation:

1 푦̂푖 = 훽̂0 + 훽̂1 (19) 푋푖 (Vyncke, 2016)

A sudden rise in temperature from 8°C to 13°C will result in relatively more beer sales than a rise in temperature from 13°C to 18°C. This can be explained by the reasoning that as the temperatures are becoming notably high, a proportion of beer consumption is shifting gradually towards water and soda consumption. (Forbes Magazine, 2017) (Blom, 2009)

Applied to our data sample, the inverse regression model results in an increase of 138 million litres beer when the quarterly average of daily maximum temperatures increases from 8°C to 13°C, compared with an increase of 61 million litres when temperature goes up from 13°C to 18°C.

5.3.3.2 Sunshine Hours

It is not unreasonable that sunshine hours impact beer sales. We already mentioned that sunny weather is associated with upbeat mood and subsequently influences stock returns. (Hirshleifer & Shumway, 2003) What’s more, sunshine also positively influences consumer spending. (Murray, Muro, Finn, & Leszczyc, 2010) In this section, the relationship between the detrended beer sales and the quarterly cumulative sunshine hours in London Heathrow is analysed. (Meteorogical Office, 2018) In figure 12, the relationship between quarterly accumulated sunshine hours in London Heathrow and quarterly beer sales (in 1.000 hl) is plotted. As the sunshine hours increase, the beer sales also go up.

Figure 12: : Boxplot of the Sunshine hours in London and Detrended Sales

In table 9, the results from different regression analyses are presented. In figure 13, the curve estimations of the different regression models are plotted. The 푅2 for the linear regression model is 0,543. However, the 푅2 here is lower than the 푅2 for the relationship between temperature and beer sales. As a result, we will exclude sunshine hours from the hedging strategy.

10 ̂ ̂ ̂ Equation R Square Sign. 훽0 훽1 훽2

Linear 0,543 0,000 8626,986 4,706

Inverse 0,473 0,000 11984,914 -492820,322

Quadratic 0,545 0,000 8947,241 2,731 0,003

Growth 0,518 0,000 9,071 0,000

Exponential 0,518 0,000 8700,711 0,000

Table 9: Curve Estimation between Detrended Sales and Sunshine hours.

Figure 13: Curve Estimation Detrended Sales and Sunshine Hours

10 The independent variable is Quarterly Average of Daily Maximum Temperatures in London

5.3.3.3 Rainfall

The last test variable is rainfall. From a rational perspective, we expect the relationship to be negative. On rainy days, people are less eager to organise BBQ’s or to spend their spare time on terraces. The data is once again obtained through the British Met Office. We have accumulated the rain (in mm) per quarter. The relationship between quarterly accumulated rainfall and beer sales is plotted in figure 14. Subsequently, we run a correlation and regression analysis for the relationship between the quarterly accumulated rainfall (in mm) and the detrended sales.

Figure 14: Boxplot of Accumulated Rainfall and Detrended Sales

In figure 14, a pattern is not directly determinable for the naked eye. To strengthen this preliminary hypothesis, a correlation analysis has been executed of which the results are presented in table 10. Although the correlation is negative, the relationship is not significant. Moreover, the results of the regression analysis are meaningless. Eleven regression analyses were insignificant on the 5% significance level. Moreover, the highest 푅2 was 0,011.

Correlation Number of data Sign. Pearson Correlation

Accumulated Rainfall 31 0,838 -0,038 and Detrended Sales

Table 10: Correlation between Rainfall and Detrended Sales

As a result, there is no significant relationship between AB InBev’s beer sales and the quarterly accumulated rainfall. We will not include rainfall into the hedging strategy.

5.4 Weather Hedging Strategy: Hedging against relatively cold winter

Last section resulted in the choice of temperature as the most appropriate weather variable to include in the hedging strategy. The quarterly accumulated sunshine hours was also related to beer sales, but both the linear and the inverse regression model of temperature had a higher coefficient of determination. This section is about building a weather hedging strategy to mitigate the weather exposure of AB InBev.

General weather hedging strategies were illustrated in 4.2.1.1. For our case study, we need to find out which weather hedging strategy is the most suitable. The weather hedging strategy will implement exchange-traded weather derivatives from the CME. However, these contracts have some specific features. At the CME, the HDD contracts are only offered from November to March and the CAT and CDD contracts are only offered from May to September. The beer sales data is presented per quarter. This arises matching problems.

Since beer sales and temperature are positively related, it is evident that the hedging strategy should hedge against temperatures which are lower than the long-term averages. This objective can either be executed by a European CAT Put for the months November- March or a European HDD Call option in the months May-September. The strategies can be consulted in table 11.

Hedge against… Option Strategy Industry application

Cold winter months: November- Call HDD Construction company March

Cool summer months: May- Beverage and ice-cream Put CDD or Put CAT September industry Table 11: Weather Hedging Strategies applied to the case of AB InBev. Source: Müller & Grandi, 2000

Although both a call HDD and a put CAT option can be used to hedge against relatively cold temperatures, it can be wise to examine which quarters impact the volume sales the most. If one quarter appears to have a higher impact on beer sales volume than the other, it might be cost-efficient to hedge the revenues only in the most impactful quarter.

This rationale has its origin in the 푅2 of the inverse regression model of 5.3.3.1. The 푅2 of the inverse regression model was higher than the 푅2 of the linear regression. This implies that an increase in relatively high temperatures affects beer sales less than an increase in relatively low temperatures.

Since low temperatures are associated with winter and autumn (corresponding with the first and fourth quarter) the preliminary hypothesis is that temperature in winter and autumn impact the beer sales the most.

To strengthen this preliminary hypothesis with statistical results, the coefficient of determination is calculated between quarter 1 and 4 (representing winter and autumn) and quarter 2 and 3 (representing spring and summer) on the one hand and beer sales on the other hand.

The coefficient of determination in this case gives the percentage of the variance in the beer sales that is predictable from the variable quarterly average of daily maximum temperatures in London Heathrow. (Vyncke, 2016) In table 12, the results can be consulted. In the first and fourth quarter, almost 46% of the variance in beer sales can be explained by the variable temperature. In the second and third quarter, this is only 20,78%.

Quarters 푅2 of linear regression

Quarter 2 and 3 0,2078

Quarter 1 and 4 0,4578

Table 12: Coefficient of determination of linear regression between quarterly average of daily maximum temperatures and quarterly beer sales volume.

Moreover, we compare the standard deviation of the daily temperature of the first and fourth quarter with the standard deviation of the second and third quarter using the F-test. Both temperature measurements in quarter 1 and 4 on the one hand and quarter 2 and 3 on the other hand are significantly normally distributed on the 1% significance level. The standard deviation of quarter 2 and 3 results in a value of 3,24°C while the standard deviation of quarter 1 and 4 is 2,99°C. This can be consulted in table 13. To test if the standard deviation in the first and fourth quarter is significantly higher than the standard deviation of the second and third quarter, we use the F-test for variances. We can reject the null hypothesis on the 1% with the F-test for variances. As a result, the standard deviation of the first and fourth quarter is significantly higher than the standard deviation of the second and third quarter. The F-test for variances is presented in Table 14.

Quarters Standard Deviation

first and fourth quarter 3,24°C

second and third quarter 2,99°C

Table 13: Standard Deviation quarter 1 and 4 versus quarter 2 and 3

Test Sign.

F-test for variances 0,000

Table 14: F-Test for variances

Because of the fact that a higher percentage of the variance in beer sales can be explained by the variable temperature in the first and fourth quarter and the higher volatile temperature measurements in that period, the hedging strategy will be applied to the first and fourth quarter. However, CME does not offer contracts for these periods. The closest approximation is the months November through March. As a result, we will apply the weather hedging strategy on that specific period.

5.4.1 Call Option Premium Payments

In this case study, the weather hedging strategy will be designed using call HDD options. A weather hedging strategy to hedge against relatively cold temperatures in the months November-March can make use of different option strategies. The long call HDD option strategy, the bull spread using HDD call options or the butterfly spread using HDD call options. (Hull J. C., 2012) (Matos, 2017)

In any of these cases, the premiums needs to be determined. The premium payments of the HDD option contracts will be calculated using 50 years of daily temperature measurements, from 1968 until 2017. This corresponds to 18.265 data points. We have obtained the data from the British Met Office. (British Meteorological Office, 2018)

Our dataset of 18.265 data points includes a minor (but significant) upward trend. The trend in our data corresponds to the phenomenon of global warming and local urbanisation. (NASA, 2018) (Jewson & Brix, 2005) NASA emphasizes that “most climate scientists agree the main cause of the current global warming trend is human expansion of the greenhouse effect” (NASA, 2018)

Jewson & Brix (2005) underline that this underlying trend should be removed from the data series: “it is not important to distinguish between local and global trends because we are interested in removing the combined trend. The way the trend is incorporated is often linear or multiplicative in the mean” (Jewson & Brix, 2005)

Therefore, the trend is removed from the 18.265 data points.

For the calculations of the option premium payments, it is essential to clarify some terms and definitions. The hedging strategy is performed over the period November-March. We will refer to November-March as “season”. Every variable that refers to that period, will receive the subscript “s”. For example:

푝푠 = 푆푒푎푠표푛푎푙 푝푎푦표푓푓 = 푃푎푦표푓푓 표푣푒푟 푡ℎ푒 푝푒푟𝑖표푑 푛표푣푒푚푏푒푟 − 푚푎푟푐ℎ

푆푠 = 푆푒푎푠표푛푎푙 푆푝표푡 𝑖푛푑푒푥 = 푆푝표푡 𝑖푛푑푒푥 표푣푒푟 푡ℎ푒 푝푒푟𝑖표푑 푛표푣푒푚푏푒푟 − 푚푎푟푐ℎ

5.4.1.1 Burn Analysis

In this case study, the burn analysis calculates the seasonal option premium as the average of the seasonal payoffs over the last 50 years. (Benth & Benth, 2013) The season in this case study is the period November-March. We use 50 years of data from 1968 until 2017.

50 1 푆푒푎푠표푛푎푙푂푝푡𝑖표푛푃푟푒푚𝑖푢푚 = 푒−푟푇 ∑ 푝 (20) 50 푠 푠=1

With

푟 = 푟𝑖푠푘 푓푟푒푒 푟푎푡푒 푇 = 푇𝑖푚푒 푡표 푚푎푡푢푟𝑖푡푦 푁 = 50

We already know that

푝푠 = 푀푎푥(푆푠 − K ,0) 푓표푟 푎 푐푎푙푙 표푝푡𝑖표푛

With

푆푠 = 푠푒푎푠표푛푎푙 푠푝표푡 𝑖푛푑푒푥

K = 푠푡푟𝑖푘푒 𝑖푛푑푒푥

Since we build the hedging strategy only with HDD options, the seasonal spot index refers to the HDD spot index.

The seasonal HDD spot index simply calculates the HDD index over the days of the given season, i.e. November-March. In mathematical terms:

푡m

푆푠 = 퐻퐷퐷(푡1, 푡m) = ∑ 푚푎푥(18 − 푇(푡), 0) (21)

푡=푡1 With 푇(푡) = 퐴푣푒푟푎푔푒 푡푒푚푝푒푟푎푡푢푟푒 표푛 푑푎푦 푡

푡1 = 푓𝑖푟푠푡 푑푎푦 표푓 푡ℎ푒 푠푒푎푠표푛

푡푚 = 푙푎푠푡 푑푎푦 표푓 푡ℎ푒 푠푒푎푠표푛

Starting from 1968 until 2017, we have calculated the seasonal payoffs every year. We assume that AB InBev is willing to accept some risk and that is why we have simulated the premium payments at strike indices slightly above the 50-year average seasonal spot HDD index, which is 1990,54 HDDs. We present the option premium payments for option contracts starting at a strike index of 2050 in table 15.

We discount the expected payoffs with the monthly rate of the Treasury Bills of The Bank of England, which is 0,52%. (Moody's, 2018)

K = 2050 K = 2060 K =2070 K = 2080 K = 2090

50 1 ∑ Max(S − K; 0) 28,52 25,96 23,9 22,04 20,24 50 s s=1 Value of one HDD £20 £20 £20 £20 £20 index point

Expected Payoff £570,4 £519,2 £478 £440,8 £404,8

Option Premium £555,8 £505,9 £465,7 £429,5 £394,4 Table 15: Option Premium payments at different strike indices via Burn Analysis

Calculated via the burn analysis, a call option with a strike index of 2050 corresponds to a premium of £555,8. Evidently, a call option with a more extreme strike index of 2090 corresponds to a lower price of £394,4, since this latter event is less likely to take place.

5.4.1.2 Monte Carlo Simulation

According to the literature, the Monte Carlo Simulation is more valid than the burn analysis to calculate a weather option’s premium. (Jewson & Brix, 2005) (Benth & Benth, 2013).

First, we determine the probability distribution of the seasonal spot HDD indices, measured over the period of 5 months. A significant normal distribution is obtained after calculating the seasonal payoffs of 50-years of daily data.11 The histogram of seasonal HDD spot indices can be found in figure 16.

We test the normal distribution on our sample via the Pearson’s Chi-Square test and the Shapiro-Wilk test. (Vyncke, 2016). For both the tests, we cannot reject the null hypothesis, and as a result, the seasonal spot HDD index is normally distributed. The Shapiro-Wilk test together with the Pearson’s Chi Square test can be consulted in table 16. The 50 seasonal spot HDD indices have the following characteristics:

푠푒푎푠표푛푎푙 푠푝표푡 퐻퐷퐷 𝑖푛푑푒푥 = ~푁(µ, 휎2)

푠푒푎푠표푛푎푙 푠푝표푡 퐻퐷퐷 𝑖푛푑푒푥 = ~ 푁(1990.54 , 130.18)

Figure 15: Histogram of detrended HDDs London Heathrow

11 From 1968 until 2017

Normality Tests Sign.

Chi Square Test 1,000

Shapiro Wilk 0,187

Table 16: Normality tests for detrended quarterly HDDs London Heathrow

With the data obtained from the British Met Office, we can run Monte Carlo Simulations to calculate seasonal HDD spot indices using random number generation. The expected payoff for the call option is then: (Benth & Benth, 2013) (Jewson & Brix, 2005) (Maenhout, 2018)

푛 1 퐸[푔(푥(푡)] = ∑ 푔 (푥(푡)) (22) 푛 t=1

The function g( ) refers to the pay-off function of a call option.

푔(푥(푡) = 푀푎푥(푥(푡) − 퐾; 0)

With

푥(푡) = 푅푎푛푑표푚𝑖푧푒푑 푠푒푎푠표푛푎푙 퐻퐷퐷 푠푝표푡 𝑖푛푑푒푥

푥(푡) = µ + 휎 ∗ 푤푡 (23)

푥(푡) = 1990,54 + 130,18 ∗ 푤t

With this random variable 푤푡 , the equation can be run for a large number of times, until the payoff 푔(푥(푡)) is relatively stable. We ran this function 20.000 times.

The premium is then the discounted expected value of the call option, which can be written as:

푂푝푡𝑖표푛 푃푟푒푚𝑖푢푚 = 푒−푟푇 퐸[푔(푥(푡)]

푛 1 푂푝푡𝑖표푛푃푟푒푚𝑖푢푚 = 푒−푟푇 퐸[푀푎푥(푥(푡) − 퐾; 0] = 푒−푟푇 ∑(푀푎푥(푥(푡) − 퐾; 0) (24) 푛 t=1

The option premium payments calculated by the Monte Carlo Simulation are presented in Table 17.

Ks = 2050 Ks = 2060 Ks = 2070 Ks = 2080 Ks = 2090 20000 1 ∑ 푀푎푥(x(t) − 퐾; 0) 29,7 26,6 23,7 21,0 18,6 20000 t=1 Value of one HDD index £20 £20 £20 £20 £20 point Expected Payoff £594,1 £531,1 £473,2 £420,2 £371,6 Option Premium £578,9 £517,4 £461,1 £409,4 £362,1

Table 17: Option premium payments at different strike indices via Monte Carlo Simulation

The option premium payments calculated via Monte Carlo simulation can be interpreted as follows. If the temperature continues to move around its seasonal HDD average with its historical standard deviation in a random manner, the payoffs of the option contracts will equal the option premium payments in the long run. As a consequence, the profit or loss equals to zero.

A comparison of the option premium payments calculated according to the two pricing models is presented in table 18 below.

Premium Ks = 1900 Ks = 1910 Ks = 1920 Ks= 1930 Ks = 1940 Burn £555,8 £505,9 £465,7 £429,5 £394,4 Analysis

Monte Carlo £578,9 £517,4 £461,1 £409,4 £362,1 Simulation

Table 18: Option Premium payments Monte Carlo Simulation versus Burn Analysis

5.4.2 Result from the Hedging Strategy

We have calculated the seasonal spot HDD indices every year from 1968 to 2017. Using different strike indices, the seasonal payoff can be calculated. In 5.4.1, the option premium payments were calculated at different strike indices. As a result, we are now able to calculate the profits and losses of the hedging strategy. We will use the premium payments calculated by the Monte Carlo Simulation, since the literature agrees that the burn analysis is a simple estimation of the price rather than a reliable pricing tool. (Campbell & Diebold, 2005) (Benth & Benth, 2013)

We have simulated the profit/loss situations for three different periods: 1999-2017, 2005- 2013 and 2011-2017. The first hedging period starts in 1999, since the first exchange-traded weather derivatives on the CME originate from that year. The second hedging period is the case study period. The choice for the third hedging period originates from the idea to determine the payoffs for the most recent period.

Three hedging strategies are considered: the long call strategy, the bull spread and the butterfly spread. The long call strategy is the most appropriate hedging strategy in our case study.

5.4.2.1 Long Call Strategy

A long call strategy can be implemented when the trader has the perception that the underlying index will go up significantly. (Damodaran, 1998) (The option's guide, 2017) (Hull J. C., 2012) In the case study, a higher seasonal HDD spot index equals higher demand for heating and therefore lower temperatures. Since the hedging strategy has the objective to hedge against lower temperatures, this long call strategy can benefit from a higher seasonal spot HDD index.

As an illustration, the profit/loss diagram for a call option in the year 2010 with strike index of 2050 HDDs is given in figure 17. The seasonal HDD spot index in that season was 2415 HDDs.

Figure 16: Profit/Loss Diagram Call option in the year 1981. Source: Hoornaert, 2018

Consequently, the profit for the holder of the call option equals:

퐼푛푑푒푥 푉푎푙푢푒 ∗ 푃푎푦표푓푓 – 푝푟푒푚𝑖푢푚 £20 ∗ (2415 – 2050) − £578,9 = £6721,1

For every year in the period 1999-2017, we have calculated this profits/losses on the call option contracts. In table 19, we have provided the accumulated profits/losses over 19 years. For the case study period (2005-2013), we found that a long call strategy results in a stable profit of around £5500-£5600; This is shown in table 20. Since the tick size of the contract is 1 HDD and the index value of one tick size is £20, the accumulated seasonal payoff will always be a multiple of £20. The profits/losses of the most recent 7 years can be consulted in table 21.

Strike Index 2050 2060 2070 2080

Accumulated Payoff £12280 £11340 £10540 £9880

Premium payments £10998,9 £9831,1 £8761,1 £7779,4

Profit/Loss £1281,1 £1508,9 £1778,9 £2100,6 Table 19: Profit/Loss of hedging strategy 1999-2017

Strike Index 2050 2060 2070 2080

Accumulated Payoff £10900 £10300 £9700 £9240

Premium payments £5210 £4656,8 £4150 £3685

Profit £5690 £5643,2 £5550 £5555 Table 20: Profit/Loss of hedging strategy during the case study

In the beginning of this section, the profit over the year 2010 was visually illustrated. This specific year was chosen with a purpose. 2010 is the year with the highest seasonal payoff of the entire dataset. The seasonal spot HDD index is 2415 that year, which is 365 HDDs higher than the strike index of 2050. This extreme cold winter result in a significant profit of £6721,1. As a result, the profit over a timespan considerably depends on the period over which the profits are calculated. Because of the fact that the case study includes the year 2010, the profit is very high.

However, if the profits/losses are measured over the last 7 years (2011-2017), the profits melt away. In table 21, the accumulated profits/losses over the years 2011-2017 is presented.

Strike Index 2050 2060 2070 2080

Accumulated Payoff £3280 £2940 £2740 £2540

Premium payments £4052,2 £3622 £3227,8 £2866,1

Profit -£772,2 -£682 -£487,8 -£326,1

Table 21: Profit/Loss of hedging strategy 2011-2017

As a consequence, the profits over the period considerably depend on the measurement period. The two most lucrative years were 2013 and 2010. Especially 2010 was an extremely cold winter. The British Met Office expressed it as follows: “Overall, the prolonged freezing conditions resulted in an exceptionally cold December across the UK: the coldest December in the last 100 years and the coldest across central England since 1890.” (British Meteorological Office, 2018)

There are other years when the pay-out equals zero. During the period 2002-2008, the holder of an option contract with a strike index of 2050 for example receives 0 payoffs. As a result, the loss on the hedging strategy during that period equals seven times the premium of £558,41.

We can conclude that the success of a long call hedging strategy is greatly dependent on the period. An investor who wants to apply this strategy needs a long-term vision in order to cover the premium payments.

This long call option strategy is one way to protect the revenues against a relatively cold winter.

5.4.2.2 Bull Spread and Butterfly Spread

A bull spread option strategy can be used by an investor perceiving a higher future index of the underlying. (Hull J. C., 2012) The bull spread is implemented using two call options. One call option is bought at a strike price/index (퐾1) below the strike price/index of another call option (퐾2) that is sold. The butterfly spread involves the sale of two call options with a strike index between the strike indices of two call options that are sold.

As a result, the initial premium payment cost for both the bull spread and the butterfly spread is significantly lower. However, with these strategies, we lose upside potential.

Although these two hedging strategies have the objective to benefit from an increase in the underlying index, these strategies are not suitable in the case study. AB InBev would not fully benefit from extreme low temperatures like in the years 2010 and 2013. Hedging against low temperatures was the main objective, though. As a result, the bull spread and butterfly spread are not appropriate in this case study.

5.5 Reflections on the Case Study

The objective of the case study was to minimize the weather exposure measured as the change in the revenues caused by a change in weather variables. Cf. formula 1.1

In the case study, a winter hedging strategy was created for AB InBev. The weather hedging strategy was implemented using HDD options on temperature. Rainfall and sunshine hours as explanatory variables were also tested. However, temperature appeared to have the highest explanatory power in the variance of the beer sales. The choice for London over Amsterdam as the main weather station for the temperature measurements was primarily incentivized by the extensive database of the British Meteorological Office. Using the Monte Carlo simulation, the option premiums were calculated for strike indices above the average seasonal HDD spot index. In 5.4, we computed the profits of the hedging strategy with data from 1999 until 2017. The hedging strategy over the period 1999-2017 and over the case study period were both significantly profitable. However, the high profitability should be interpreted with caution. The years 2010 and 2013 resulted both in an elevated HDD index, due to extremely cold weather conditions. For the most recent seven years, a loss was accumulated. This period excluded the most profitable years and as a result, the payoff fell significantly.

We can conclude that AB InBev faces a significant weather exposure. The sales volume of the world’s largest brewer is affected by sunshine hours on the one hand and temperature on the other hand. A change in the quarterly average of maximum temperatures from 8°C to 13°C can impact the beer sales with 138 million litres.

The company can decrease its weather exposure by using weather derivatives as a tool of its risk management strategy. However, it is essential to underline that weather hedging needs

68 a long-term horizon. For years in a row, the expected payoffs may equal zero, but in the case of extreme temperatures, the company will see its volumetric contraction compensated in a fast, objective and efficient manner.

Conclusion

This master’s dissertation has shed light on a relative new topic in risk management, i.e. weather risk management. More than ever, industries face a weather risk that impacts the financial performance via their supply, demand or business processes. In the latest Global Risk Report 2018, published by the World Economic Forum, extreme weather events were ranked number 1 global risk with the highest likelihood and number 2 risk in terms of impact. Extreme weather events are perceived to bear higher risks than cybersecurity, weapons of mass destruction, data fraud and involuntary immigration.

As a result, it is key to include weather risk management tools such as natural weather hedging, insurance and weather derivatives into the general risk management strategy.

Secondly, we have elucidated the weather derivative literature. We explained the origins and the history of the weather derivative market. The first contracts were OTC. However in 1999, the Chicago Mercantile Exchange started to offer exchange-traded weather derivatives in Europe and the US. Moreover, three different weather derivative contracts were illustrated. We also zoomed in on the design of a weather hedging strategy using options. Different weather- exposed industries could benefit from the weather hedging strategy, as there are: the beverage industry, outdoor amusement events, electric utilities, construction industry and agriculture.

In 4.2.4, the empirical results of a study were discussed in which the authors examined if weather risk management using weather derivatives could lead to an increase in firm value. The conclusion was that the use of weather derivative increased the market-to-book ratio with 6%.

Since the weather is neither an asset nor tradable, the Black-Scholes formula is invalid and as a result, alternative pricing models have been widely discussed in the literature. Today, there is still an academic polemic about the most adequate weather derivative valuation model. We have guided the reader through two pricing models for weather derivatives: the Burn Analysis and the Monte Carlo Simulation. In the last part of chapter four on weather derivatives, we have provided two alternative weather risk management tools: insurance and natural hedging. We can conclude that natural weather hedging is a rational strategic choice with a relatively low cost. Furthermore, weather derivatives have the advantage of an efficient, fast and objective cash settlement, whereas the financial compensation from insurance can be a long and effort-intense process. Moreover, it allows businesses to hedge volumes which cannot be insured.

In the last chapter of this master’s dissertation, a case study of AB InBev was implemented. In the financial statements of AB InBev, there is a panoply of statements regarding the impact of the weather on the financial performance. We have performed statistical analyses to lay bare the weather exposure of one of largest FMCG companies in the world. After filtering the beer sales volume for a declining trend in the West-European beer consumption, we have found an inverse regression model between West-European beer sales volume and quarterly average of daily maximum temperatures in London Heathrow. The inverse regression model had a coefficient of determination of 0,682. The inverse relationship can be explained through the reasoning that as temperatures are becoming notably high, a proportion of beer consumption is shifting gradually towards water and soda consumption. Quarterly accumulated sunshine hours was also significantly related with beer volume sales, but to a lesser extent as the quarterly average of daily maximum temperatures. Quarterly accumulated rainfall did not have a significant relationship with beer sales volume.

In order to mitigate AB InBev’s weather risk, a weather hedging strategy using a long European call option strategy with contracts traded at CME was designed. Using Monte Carlo Simulation, we have calculated the option premiums at different strike prices above the 50-year seasonal average HDD index measured in the period November through March. The results of the hedging strategy in the case study period are profitable. This is primarily due to the extreme cold winters of 2010 and 2013. Both years resulted in a significant payoff which surpassed the option premium payments. Nevertheless, it is crucial to emphasize that during the period 2002- 2008, the hedging strategy resulted in severe losses. As a result, the success of the weather hedging strategy is greatly dependent on the hedging period.

We can conclude that weather derivatives are an essential tool in weather risk management. These instruments provide efficient and objective cash settlement and furthermore they reduce the volatility of the financial performance triggered by weather variables. Our case study indicated that the significant payoffs in some years are vital to cover the premium payments of other years in which the payoffs equal zero. As a consequence, only a long-term weather hedging horizon will prove to mitigate the weather exposure.

Further research

Further research needs to be executed on the valuation methods of weather derivatives. Today, there is no standard pricing model like the Black-Scholes model in conventional derivatives.

Moreover, there is a need for comparing the different weather risk management tools empirically. Are weather derivatives in the long-run superior to insurance? Weather derivative settlement is objective and efficient, but can it add more value to a company than insurance can?

I hope sincerely that the answers on the aforementioned questions will be answered in the near future and that one day, weather risk management will be considered as a full-fledged facet of general risk management.

Signature author:

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