IIIS Discussion Paper

No.433 / August 2013

Fuel Hedging, Operational Hedging and Risk Exposure– Evidence from the Global Airline Industry

August 2013 Britta Berghöfer School of Business, Trinity College Dublin 2, Ireland Lufthansa Aviation Center, Airportring, 60546 Frankfurt / Main, Germany [email protected]

Brian Lucey (Corresponding Author) School of Business Trinity College Dublin 2 Ireland Institute for International Integration Studies (IIIS), The Sutherland Centre, Level 6, Arts Building, Trinity College Dublin 2 Ireland Glasgow Business School, Glasgow Caledonian University, Cowcaddens Rd, Glasgow, Lanarkshire G4 0BA, United Kingdom Faculty of Economics University of Ljubljana Kardeljeva ploscad 17 Ljubljana, 1000 , Slovenia [email protected]

IIIS Discussion Paper No. 433

Fuel Hedging, Operational Hedging and Risk Exposure– Evidence from the Global Airline Industry

August 2013 Britta Berghöfer School of Business, Trinity College Dublin 2, Ireland Lufthansa Aviation Center, Airportring, 60546 Frankfurt / Main, Germany [email protected]

Disclaimer Any opinions expressed here are those of the author(s) and not those of the IIIS. All works posted here are owned and copyrighted by the author(s). Papers may only be downloaded for personal use only. Fuel Hedging, Operational Hedging and Risk Exposure– Evidence from the Global Airline Industry

August 2013

Britta Berghöfer School of Business, Trinity College Dublin 2, Ireland Lufthansa Aviation Center, Airportring, 60546 Frankfurt / Main, Germany [email protected]

Keywords: Airline, hedging, operational hedging, financial hedging JEL Codes: G32, L93

ABSTRACT

The aviation industry is characterized by low profit margins and a constant struggle with skyrocketing fuel costs. Financial and operational hedging strategies serve aviation managers as a tool to counteract high and volatile fuel prices. While most research on fuel hedging has concentrated on the U.S. airline market, this paper is the first study to include airlines from Asia and Europe. We analyze 64 airlines over 10 years and find that Asian carriers are more negatively exposed than European airlines but less exposed than North American airlines. In contrast to Treanor, Simkins, Rogers and Carter (2012), this study finds less significant negative exposure coefficients among U.S. carriers. Using a fixed effects model we reject the hypothesis that financial hedging decreases risk exposure. One possibility is that the decreased volatility in jet fuel prices over the past few years has perhaps made airlines less exposed to fuel prices and hence, financial hedging less effective. However, operational hedging, defined by two proxies for fleet diversity, reduces exposure significantly. A one percent increase in fleet diversity, calculated with a dispersion index using different aircraft types, reduces the risk exposure coefficient by 2.99 percent. On the other hand, fleet diversity, calculated with different aircraft families, reduces exposure by 1.45 percent. Thus, aviation managers have to balance the fleet diversity between operational flexibility and entailed costs.

1. INTRODUCTION

The airline industry has always been characterized by low profit margins. Decreasing airfares due to increasing competition have made air travel a commodity market (Button, Costa, Costa and Cruz, 2011; Carter, Rogers and Simkins, 2004). Deregulation in the world airline industry has led to greater competition since the 1980s (Oum and Yu, 1998). Especially the deregulation of the aviation sector in Europe has supported the intense growth of low-cost carriers (LCC). Traditional airlines in particular suffer from losing market shares to LCCs (Civil Aviation Authority, 2006).

Besides increased competition, high and volatile fuel prices challenge airlines further. Fuel accounted for 33 percent of average operating costs in 2012, for 22 percent in 2005 and for 13 percent in 2001 (IATA1, 2012a). The overall fuel bill amounted to 177 billion U.S. dollars (USD) in 2011 (IATA, 2012b). Delta Air Lines, for example, reports fuel expenses of 36 percent of total operating costs in 2012 (Delta Air Lines, 2013). Even the golf-carrier Emirates with its supposed easy access to oil declared fuel costs of 34 percent (Emirates, 2012). In addition to the cost level, fuel price volatility and a large crack spread with the underlying commodity crude oil add to the airlines’ fuel problems (IATA, 2012b). However, the exposure to market pressure prevents airlines from raising ticket fares in response to the high kerosene prices (Carter et al., 2004). Button et al. (2011), analyzing the Portuguese airline market show that full cost recovery is impossible in the current competitive situation. As airlines are unable to increase ticket prices they put their efforts on hedging activities.2

Airlines started to employ fuel hedging as a risk management strategy in the late 1980s. Before, mostly currency derivatives to counteract exchange rate fluctuations were used (Morrell and Swan, 2006). Under the assumption of the famous Modigliani and Miller Proposition 1 (1958) debt policy and consequently risk management are extraneous for investors under perfect market conditions as investors can diversify on their own. Nevertheless, due to existing market imperfections (Deshmukh and Vogt, 2005) research results suggest hedging does make sense under different conditions and in fact, might add to firm value. Companies can hedge by either using financial derivatives or by altering real option decisions (operational hedging) (Smith and Stulz, 1985). Firms generally use financial and operational hedging complementary (e.g. Kim, Mathur and Nam, 2006; Treanor, 2008). Real options may include fleet diversity, fleet fuel-efficiency and optimized fleet assignment (Morrell and Swan, 2006; Naumann, Suhl and Friedemann, 2012; Treanor, 2008; Treanor, 2012; Treanor, Rogers, Carter and Simkins, 2012a).

The purpose of this paper is to uncover the determinants of airline commodity exposure. Exposure, in general, can be defined as the sensitivity of firm value to changes of the underlying financial risk (Jorion, 1990). Although various research papers have been published regarding the use of derivatives within the aviation industry, the author suggests that a further study could add to the existing body of knowledge. There are certain gaps in previous research which can serve as a valuable starting point for further research. Prior

1 IATA stands for International Air Transport Association, the largest trade association representing 240 airlines worldwide. 2 Obviously, not only passenger airlines are exposed to fuel prices and use financial derivatives but also cargo carriers and the military aviation industry encounter commodity exposure. Nevertheless, the limited information available on military operations inhibits a more detailed analysis of this sector. Cargo airlines are mostly part of a larger airline group, e.g. Deutsche Lufthansa, and are thus included in the analysis. Atlas Air is the only pure cargo carrier analyzed in this paper.

1 empirical research on jet fuel hedging has concentrated on the U.S. market. While the author acknowledges the difficulty of obtaining data outside the U.S., she suggests that it is important to examine data from other countries. In 1999, Rao points out that European airlines hedge more actively than American airlines. While analyzing the cost competitiveness of 22 major worldwide airlines, Oum and Yu (1995, 1998) uncover that Asian carriers are more cost competitive than American and European airlines. American airlines in turn have a better cost position than European carriers. Cobs and Wolf (2004) also point to differing hedging strategies among LCC and value carriers. Therefore, the author will focus on differences in commodity exposure between Asian, European and North American carriers3 as well as differences between LCC and premium airlines in the period of 2002-2012. So far, to our knowedge, no empirical research has dealt with regional or business model differences. A fixed effects regression model using panel data should estimate the effectiveness of operational and financial hedging. Lastly, based on statistically established results, this paper will provide managerial implications on how to reduce airline risk exposure.4

2. PREVIOUS RESEARCH

2.1. Airline fuel and fuel contracts

Bessembinder (1991, p. 519) defines hedges as “contracts that reduce an agent's risk”. Therefore, hedging is part of the overall corporate risk management strategy (Batt, 2009; Nance, Smith and Smithson, 1993). Hentschel and Kothari (2001) further distinguish between hedging, which reduces return volatility, and speculation, which increases return volatility. Regarding fuel hedging, an airline as the commercial consumer takes the long position, the airline "is long" by purchasing future or forward contracts. On the other side of the contract, the short position, there may be a trader, an oil or fuel company. Any positive cash flow arising in the long position constitutes a negative cash flow in the short position and vice versa (Dybvig and Marshall, 1997; Tokic, 2012).

Within an organization, risk management is normally managed centrally. Commodity hedging is the exemption as it is generally organized in a decentralized structure (Bodnar, Hayt and Marston, 1998). However, to underline the importance of fuel hedging within the aviation industry, the treasury department generally decides centrally about the fuel hedging strategy (Carter et al., 2004).

On the organized exchange-traded futures market aviation fuel is usually not traded. Except for a small Japanese market, aviation fuel forward contracts must be arranged OTC. Although airlines value OTC contracts for their customizability, these contracts bear counterparty risks, such as the danger of bankruptcy of the airline or the trading partner. (Carter et al., 2004; Cobs and Wolf, 2004; Morrell and Swan, 2006). Furthermore, OTC markets are highly illiquid which make OTC derivatives more expensive and also less available for higher amounts of fuel (Adams and Gerner, 2012; Carter et al., 2004; Cobs and Wolf, 2004). Illiquidity premia increase for longer hedge horizons (Adams and Gerner, 2012; Bertus, Godbey and Hilliard, 2009).

To overcome the disadvantages of OTC markets, airlines commonly ‘cross hedge’ their fuel demand with similar commodities futures contracts on commodity exchanges (Bessembinder, 1991). Futures are mainly traded via the New York Mercantile Exchange (NYMEX) or the IntercontinentalExchange (ICE) in London. Less than one percent of futures actually result in a physical delivery of the traded commodity (Morrell and Swan, 2006).

3 The inclusion of different countries in the research provides an international focus. 4 Berghoffer’s profession as a co-pilot of Lufthansa German Airlines is helpful to provide further insight into the topic, especially flight procedure related aspects.

2 Commodities exhibiting similar characteristics as kerosene are crude oil, heating oil and gasoil (Adams and Gerner, 2012). Similar characteristics stem from the nature of refining.5 Typically, crude oil6, heating oil and gasoil7 futures prices are cointegrated with kerosene prices and can be used for cross hedging jet fuel demand purposes (Adams and Gerner, 2012; Mirantes, Población and Serna, 2008). If we examine the correlation between jet fuel spot prices and crude oil spot prices over the past decade we find a correlation factor is 0.63.

Adams and Gerner (2012) find that for physical and refining reasons gasoil shows the closest relation with jet fuel although crude and heating oil are more often used by airlines. Higher output of one product can only be accomplished by reducing the output of the other product. A reduction in the crude oil due to rationing, political or military actions increases jet fuel prices. In addition, a high oil price normally means lower confidence and lower economic activity. Airlines consequently suffer from higher input prices and lower revenue. However, profits from crude oil forwards can alleviate the pressure on airline revenues (Morrell and Swan, 2006). Nevertheless, the risk of cross hedging of uncorrelated kerosene spot prices with crude oil spot prices remains (Adams and Gerner, 2012). This ‘basis risk’8 arises when the price of the underlying cross hedge instrument is not perfectly correlated with the price of the purchased asset (Bertus et al., 2009; Carter et al., 2004; Cobs and Wolf, 2004). Thus, if an airline wants to evade the disadvantages of the OTC market it has to face the basis risk of exchange-traded futures (Adam-Müller and Nolte, 2011). The difference between crude oil spot prices and jet fuel spot prices is called ‘crack spread’. The crack spread, which tends to widen during higher volatility in crude oil prices (Morrell and Swan, 2006), remained at a high level of 15 percent in 2011. Figure 1 clearly shows the increasing and volatile crack spread between 2002 and 2011. The distortion of WTI crude oil is especially apparent at the end of 2009 (IATA, 2012b).

5 The primary commodity is crude oil, which is refined to the following products: the ‘top of the barrel’ gasoline (44 percent), the ‘middle of the barrel’ heating oil, diesel (both 22 percent) and kerosene (9 percent) and the ‘bottom of the barrel’ fuel oil (4 percent). Each barrel displays similar specialties, which make the prices of the products in one barrel related (Adams and Gerner, 2012; Carter et al., 2004; Mirantes et al., 2008). The remaining 21 percent of refined products comprise, among others, liquefied gas and asphalt (Mirantes et al., 2008). 6 Crude oil is available as West Texas Intermediate (WTI) in North America or Brent in Europe. 7 Gasoil is the European term for heating oil and diesel. 8 Carter et al. (2004) further distinguish between product, time and locational basis risk.

3 Figure 1 – Spot prices of jet fuel, crude oil and the crack spread

Own figure using daily U.S. Gulf Coast Kerosene-Type Jet Fuel Spot Prices and Cushing, OK WTI Spot Prices. Data taken from EIA.org for the period 02 Jan 2002 - 31 Dez 2012. 210

160

110

60 Spot price in USD per gallon 10

-40 01/2002 07/2002 01/2003 07/2003 01/2004 07/2004 01/2005 07/2005 01/2006 07/2006 01/2007 07/2007 01/2008 07/2008 01/2009 07/2009 01/2010 07/2010 01/2011 07/2011 01/2012 07/2012

Crack Spread Jet Fuel Spot Price Crude Oil WTI Spot Price

With a dynamic hedging strategy the department adjust the instruments over the oil spot price cycle (Cobs and Wolf, 2004). However, the question then becomes how well the oil price cycle can be forecasted. The better airlines can forecast future prices the more successful they will be with their hedging activities. Sturm (2009) estimates, the US airline industry could gain more than 500 million USD by selectively hedging seasonal trends in the oil cycle. Besides the timing of hedging decisions, the airline has to consider the number of futures or forwards per unit of kerosene needed: the hedge ratio. A simple method to calculate the optimal hedge ratio is to use the slope coefficient of an ordinary least squares (OLS) regression of spot and futures returns (Clark, Lesourd and Thiéblemont, 2001). Yet, this simple calculation bears the risk of suboptimal outcomes in times of high price volatility (Brooks, Cerny and Miffre, 2012).

We do not discuss in detail here the rationales for hedging as these are well known. The main rationales encompass the alleviation of the underinvestment problem and the associated aspects of external financing and financial distress. In addition, managerial incentives, accounting policies and informational effects influence hedging activities. Taxation schemes and more behavioral elements are further incentives for a firm to use derivatives. However, even successful hedging imposes costs and other disadvantages for corporations. One of the main disadvantages associated with hedging is the additionally induced costs (Aabo and Simkins, 2005). Rao (1999) estimates the cost of hedging to be one percent of an airline's fuel bill. There is also counterparty risk ; Airlines typically receive data used for hedging decisions from oil or fuel suppliers directly (Mercatus, 2012), which involves counterparty risk in form of opportunistic behavior by the other party. If the airline cannot reduce this risk by adjusting e.g. contracting terms the benefits of hedging cannot be fully exploited. Rod Eddington, former CEO of British Airways, expresses the disadvantage in this way: "When you hedge all you do is bet against the experts of the oil market and pay the middle man […]" (Davey, 2004). In 2008, many airlines experienced losses due to their hedging strategy, even the

4 formerly successful fuel hedger Southwest Airlines had to write off large sums of capital (Tokic, 2012). Once incorrectly applied, hedging can turn from a profitable business strategy into a loss-making one. However, this alleged disadvantage can be used by well-hedging airlines as a real competitive advantage over their peers. The ability of an airline to employ successful hedging strategies will influence largely research results regarding benefits of hedging.

2.2. Does hedging add value?

Generally, firms are exposed to fluctuating values in exchange rates, interest rates or commodity prices. Adler and Dumas (1984) emphasize the difference between risk and exposure. While risk is defined by the probability of occurrence, exposure identifies “what one has at risk” (p. 41). Hedging as part of the risk management program should reduce the exposure in comparison to speculation, which increases the exposure. The gold mining study by Tufano (1998) on shows that gold exposure is negatively related to gold price volatility. Hedged firms are less exposed to gold price risk. Jin and Jorion (2006) on U.S. oil and gas producers for the period 1998-20019 show that hedging can reduce a firm’s exposure to commodity price risk. Allayannis, Ihrig, and Weston (2001) include 265 firms over a three year period in their paper, testing for the effectiveness of operational10 and financial hedging. While currency hedging reduces exchange rate exposure they reject that operational hedging reduces exposure as well. However, Hentschel and Kothari (2001) do not find evidence that interest rate or currency hedges reduce risk exposure.11 In an international sample of 1,150 manufacturing firms from 16 countries Bartram, Brown, and Minton (2010) conclude that operational hedging reduces exchange rate exposure by 10-15 percent and financial hedging by approximately 40 percent in contrast to a hypothetical non-hedging firm.

According to the underinvestment theory, as long as hedging can reduce cash flow volatility firm value should be positively affected. Allayannis and Weston (2001) is an early study on that question. They find evidence that firms using currency derivatives exhibit an increase in firm value, measured with Tobin’s Q, of almost 5 percent.12 However, Smithson and Simkins (2005) and Jin and Jorion (2006) challenge the hedge premium of 5 percent and question whether this value derives solely from financial hedging. The success of the overall corporate risk management such as operational hedging might contribute to the premium. Moreover, the selection of firms from different industries might induce the importance of other variables not accounted for in the study (Jin and Jorion, 2006). Nelson, Moffitt and Affleck-Graves (2005) extend their research on large and small U.S. firms between 1995 and 1999, using the same model as Allayannis and Weston (2001). While they cannot find increased firm value with small firms, they arrive at the same conclusion as Allayannis and Weston for large firms i.e. a firm value increase of 4.3 percent.

Carter et al. (2006) research the airline industry exposure and thus focus on hedged input goods instead of output goods. Carter et al. as well as Treanor (2008) come to the same conclusion as Tufano (1998) : While the airline industry is exposed to high fuel prices, operational and financial hedges can reduce this exposure. Carter et al ( 2006) suggest a firm value premium of between 5-10% for the 28 US airlines examined. Treanor et al. (2012a) take up on previous research about the U.S. airline industry. This time, they focus on the period of

9 Jin and Jorion (2006) base their paper on a research conducted by Haushalter (2000). 10 They estimate operational hedging using four proxies including the number of countries where the firms operate and a geographic dispersion index. 11 Hentschel and Kothari (2001) use 929 firm years from the period of 1991-1993. They justify their findings with the assumption that firms hedge to reduce short-term contract risk. As short-term contracts only constitute a small portion of firm value, these contracts do not have a great impact on risk reduction. 12 However, the premium is only significant in periods of currency appreciation.

5 1994-2008 and also include selective hedging13 methods of airlines in their research model. They conclude that airlines use more derivatives when fuel prices are high or when risk exposure is greater. Furthermore, fuel price exposure has increased during the analyzed period. Treanor, Simkins, Rogers and Carter (2012b) look at the relation between the real options of fleet diversity and aircraft age and risk exposure. For the same time period, both operational and financial hedging reduces airlines’ risk exposure. Moreover, operational hedging tends to be more effective in reducing risk exposure than financial hedging. We can however question the research results regarding fleet diversity. While a diverse fleet might reduce fuel price exposure but the additional costs with operating a diverse fleet, e.g. maintenance cost, type rating, training of crews, may outweigh the benefits. We suggest below a second method for calculating operational diversity which may mitigate some of these problems.

However, Guay and Kothari (2003) question the risk premia derived by Allayannis and Weston and Carter et al. by arguing that other risk management parts such as operational hedges might have influenced the outcome. In their study they concentrate on a three standard deviation change in the underlying commodity price. The resulting sensitivity of cash flow and market value is much lower than the risk premia derived previously. They conclude that the mixed evidence in literature stems from the irrelevance of derivative usage within the overall risk management strategy. A similar result is obtained by Jin and Jorion (2006) in their study on U.S. oil and gas producers.14 While the positive effect of hedging on volatility was already discussed they do not find higher market value for hedged firms.

Treanor (2008) refines Carter et al. (2006) by including operational hedging variables such as fleet diversity and leased aircraft. Apart from small exceptions he agrees with prior research that financial fuel hedging increases firm value.15 In a more recent study conducted by Treanor et al. (2012a), the authors use data between 1994 and 2008 and still find a hedge premium for U.S. airlines. However this hedge premium has decreased over the evaluation period. In addition, Treanor et al. conclude that investors prefer airlines which implement a stable hedging strategy rather than adapting the percentage of fuel requirements hedged related to different levels of risk exposure.

In this paper we concentrate on risk exposure only. The main reason for preferring risk exposure is the possibility of a direct estimation of exposure coefficients with stock data. In contrast, firm value is usually calculated indirectly using Tobin’s Q.

3. DATA AND METHODOLOGY

3.1. Data and variables

We examine a set of airlines from around the globe, and believe we are the first paper to do so. The selection of North American airlines is based on Securities and Exchange Commission (SEC) filings with the SEC code 4512 – scheduled air transportation, as well as one cargo airline with SEC 4522. The sample of Asian and European airlines is based on multiple selection methods. The member list of the IATA is used as an initial filtering mechanism. In addition, online search engines and personal experience are employed for

13 Treanor et al. (2012a) use the level of changes in hedging activity, measured with the standard deviation of percentage fuel hedged, as a proxy for selective hedging. 14 A negative relation between derivative usage and Tobin’s Q is also observed by Fauver and Naranjo (2010). In a sample of 1746 U.S. firms (1991-2000) they find that hedged firms with greater agency and monitoring issues show a lower Tobin’s Q of as much as -8.4 percent. 15 Treanor (2008) includes fuel pass-through agreements in the model. These agreements have a negative impact on Tobin’s Q. Treanor suggests, as mainly regional airlines use fuel pass-through agreements the results could be distorted.

6 finding more airlines. The airlines must be publicly listed and their stock traded on international exchanges. After the initial filtering, the airlines’ annual reports were retrieved from SEC-filings, companies’ websites, Thomson Reuters Eikon, by e-mail or in paper version per mail. Only those airlines with an adeqate number of annual reports are included. The cutoff date for the annual report 2012 is June, 11th 2013. Airlines, part of a tour operator (e.g. Condor and Thomas Cook Group) or a conglomerate (e.g. Asiana Airlines and Kumho Asiana Group), are not included due to limited amount of airline level information. This selection process results in 20 Asian, 20 European and 24 North American airlines as exhibitedTable 1. Fourteen of the 64 airlines can be defined as low-cost carriers.16 The period of analysis is 2002 until 2012.

Table 1 Overview of airlines analyzed in this paper Asia Europe North America TOTAL LCC Airlines 20 20 24 64 14 Periods 197 178 211 586 114 Average periods per airline 9.85 8.90 8.79 8.14 9.16

For the estimation of yearly exposure coefficients, weekly equity and market index stock data, as well as U.S. Gulf Coast and Singapore jet kerosene spot prices are retrieved from Datastream. The trade weighted U.S. dollar index is collected from the website research.stlouisfed.org. In Figure 2 the airlines’ average monthly stock returns are averaged to a regional return. The graphic reveals the return volatility of North American airlines. The maximum annualized standard deviation in daily returns occurred in 2008 with North American airlines. Interestingly, the maximum SD for U.S. Gulf Coast jet fuel prices in 2008 is 1.007 while Singapore jet fuel demonstrates an SD of 0.415 in the same period.

16 The low-cost carriers analyzed in this paper comprise AirAsia, Tiger Airways, Virgin Australia, Airberlin, easyjet, Norwegian Airshuttle, Ryanair, Vueling, airTran, Allegiant Travel Company, Frontier Airlines, JetBlue Airways, Southwest Airlines and Spirit Airlines. In most cases, these airlines classify themselves as low-cost carriers. Some airlines such as Airberlin do not see themselves as a typical LCC and their allocation to one of the two categories is more ambiguous. In general, a LCC is characterized by air fares and cost structures which are lower than that of value carriers. LCCs achieve lower costs by an undiversified fleet, short turnaround times, low service offering and underutilized airports (Windle and Dresner, 1999).

7 Figure 1 Monthly stock returns for each region

Own figure using monthly ln changes in airline stock prices averaged to regional stock return. Data taken from Datastream for the period 02 Jan 2002 - 31 Dez 2012. 0.5 0.4 0.3 0.2 0.1 0 -0.1

Monthly ln ln changes Monthly -0.2 -0.3 -0.4 01/2002 07/2002 01/2003 07/2003 01/2004 07/2004 01/2005 07/2005 01/2006 07/2006 01/2007 07/2007 01/2008 07/2008 01/2009 07/2009 01/2010 07/2010 01/2011 07/2011 01/2012 07/2012 01/2013

Asia Europe North America

Financial and operational fuel hedging data was collected manually from the airlines’ annual reports and 10-K filings. The disclosure of the data, however, varies over the regions (detailed data available on request). While Asian airlines disclose 72 percent of relevant fuel derivative information, European airlines disclose 88 percent and North American carriers 99 percent. Nevertheless, sufficient data were extracted. Other variables such as load factor, distance or business model are gathered manually from annual reports. Long-term debt and total asset data are collected from Datastream.

An airline’s financial hedging strategy is defined by the variables HDGPER, the percentage of next year’s fuel requirements hedged, as well as HDGMAT, the maximum hedging maturity in months. The inhomogeneous disclosure of hedging information of European and Asian airlines poses a problem. Sometimes, airlines disclose their hedge contracts but do not state the maturity. Hence, next year’s fuel hedging cannot be calculated. Similarly, if neither the percentage nor the notional amount is published no information is usable for that period. In cases where the notional amount hedged is published in barrels, metric tons or U.S. gallon, this value is divided by the actual fuel consumption of the following year.

Based on the Hirschman-Herfindahl concentration index employed by Allayannis et al. (2001) for geographic diversification we calculate an aircraft dispersion index for estimating the degree of an airline’s fleet diversification. A diverse fleet can be used as an operational hedge. In times of high fuel prices and simultaneous weaker economic environment, smaller aircraft can be deployed to counteract lower demand. Thus, the important performance indicator load factor can be maintained at a high level. The fleet information is obtained from annual reports or airlines’ websites. If an airline omits the reporting of the fleet size in one period, the nearest period’s information is used instead. The ADI_1 is calculated as follows:

. ADI_1 1 . (1) where N is the total number of different aircraft models in airline i’s fleet. In contrast to previous research, the author regards each aircraft model type as a different aircraft model. A Boeing 737-700, for example, has 149 seats in 1-class configuration while the 737-600

8 contains 132 seats. Each of these Boeing 737 consequently serves as a valuable operational hedge to moderate fluctuating demand conditions. Hence, each aircraft model type is considered as one model.

However, holding available a diverse fleet presents enormous costs. Not only do different aircraft types require different type ratings, but also spare parts, technology or ancillary equipment vary. To overcome the methodological issue of a diverse fleet, a second index is calculated. ADI_2 treats all aircraft models of one aircraft family with the same type rating requirements as one aircraft. Aircraft families combine a certain number of aircraft types with the same basic configurations but with different seat numbers. Examples of aircraft families are the Airbus 320 family17 or the Boeing 777 family.

. ADI_2 1 . (2)

Exceptions from the above rule include for example the aircraft types Airbus 330 and 340. Although the same pilots can fly both types with a so-called double type rating, their specifications are so different, the number of engines in particular, as to meaningfully analyze them as one aircraft type. Also, freighter and passenger versions of the same aircraft type are still included as two different types because they cannot be operated interchangeably. Airplanes of a subsidiary are added to the number of the same core brand aircraft type. One could argue that different contractual crew arrangements would inhibit the operational compatibility. Nevertheless, economies of scale increase in e.g. maintenance or aircraft procurement with a higher number of equivalent aircraft types in the subsidiary airlines.1819

Table 2 summarizes the results for variables ADI_1 and ADI_2 as described above.

17 The A320 family consists of the A318, A319, A320 and A321. The Boeing 777 family comprises the 777-200, 777-200ER, 777-200LR, 777-300. 777-300ER and 777 Freighter. 18 Aircraft in operation under Capacity Purchase Agreements (CPA) or fixed-fee contracts, for example JAZZ aircraft in the Air Canada fleet, are excluded from the mainland’s fleet. Mostly, these contracts remain in force for five years or more. Consequently, CPA aircraft are not as flexibly deployable as owned aircraft. 19 Treanor et al. (2012b) note that the fleet reported in the annual reports represents the aircraft in operation and not the entire available aircraft fleet, including parked aircraft. However, in our view it is not necessary to adjust the ADI. First, the number of parked aircraft usually accounts for a small percentage of the complete fleet. Second, the risk exposure is estimated for current periods and thus reflects the operating fleet better than the fleet including parked and grounded airplanes.

Table 2 Aircraft diversity indices, 2002-2012 The table reports the results for Equations (1) and (2). The regional diversity indices include averaged annual airline diversity indices. Data are obtained from annual reports and airline homepages. If the airline does not report their fleet in one period, the author uses the fleet information of an adjacent year.

ADI_1 ADI_2 Year Asia Europe NA Total Asia Europe NA Total ADI_1 - ADI_2 2002 0.791 0.778 0.629 0.724 0.780 0.572 0.504 0.594 0.130 2003 0.775 0.739 0.586 0.690 0.783 0.584 0.459 0.574 0.116 2004 0.783 0.728 0.598 0.688 0.803 0.582 0.419 0.554 0.134 2005 0.778 0.749 0.606 0.687 0.815 0.633 0.429 0.571 0.117 2006 0.788 0.719 0.627 0.698 0.823 0.582 0.452 0.568 0.130 2007 0.793 0.654 0.612 0.673 0.824 0.549 0.470 0.567 0.107 2008 0.816 0.672 0.613 0.685 0.821 0.541 0.474 0.572 0.113 2009 0.819 0.647 0.591 0.672 0.822 0.490 0.458 0.554 0.118 2010 0.769 0.627 0.598 0.659 0.818 0.451 0.511 0.549 0.110 2011 0.759 0.603 0.654 0.671 0.813 0.417 0.489 0.531 0.140 2012 0.760 0.598 0.648 0.669 0.814 0.412 0.479 0.530 0.138

Average 0.785 0.683 0.615 0.683 0.811 0.528 0.468 0.560 0.123

3.2. Empirical model and hypotheses

We utilise a two-step procedure to estimate how airlines can reduce their fuel price risk exposure. Exposure, in general, can be defined as the sensitivity of firm value to changes of the underlying financial risk. Based on Jorion (1990) risk exposure formula, Equation 3 initially estimates each airline’s yearly exposure coefficient.

, ,, ,, , , (3) Where:

, is airline i’s weekly ln stock price return for week w,

, is the ln return for the corresponding market index for week w,

, is the weekly ln change in jet kerosene prices for week w,

, is the ln change in the trade weighted U.S. dollar index for week w,

, is the market risk factor for airline i for year y, and

, is the jet fuel risk factor for airline i for year y.

In the second step of the analysis, the yearly risk exposure coefficients are regressed against financial and operational hedging variables as well as control variables. Equation 4 represents the second regression using a panel dataset.

10 , ,,

1, (4) ,

The exposure coefficient γ is calculated for each airline i for each year y. The hedging variables HDGPER, HDGMAT and ADI_1 are discussed above.

The variables LNTA and LTDA are included to control for firm size and leverage. One of the most researched determinants of hedging usage is the size of a firm. Haushalter’s (2000) study on 100 oil and gas producers in the mid 1990s provides evidence that larger firms, defined by total assets, are more likely to hedge than smaller firms. Geczy, Minton and Schrand (1997) observe similar results among 370 U.S. non-financial firms in 1990. They conclude that due to economies of scales in hedging costs firms using commodity or interest rate hedges are more likely to employ exchange rate futures. Larger companies have more resources available to set up and manage hedging programs. Moreover, larger firms may encounter lower cost of external financing and lower information asymmetry (Deshmukh and Vogt, 2005). Further research with consistent results on firm size and derivatives usage include e.g. Carter et al. (2006), Guay and Kothari (2003), Nelson et al. (2005) and Pramborg (2004).

Another determinant for hedging is a firm’s debt ratio. However, empirical evidence on this is mixed. Whereas Haushalter (2000) and Graham and Rogers (2002)20 find that the more financial leverage a firms exhibits the more derivatives it uses, Carter et al. (2006) provide the contrary result. The airlines with the lowest debt ratio use more derivatives than airlines with higher leverage. Carter et al. explain their findings with the relatively higher financial distress costs of larger airlines. The investment growth possibilities in times of high fuel prices must not be put at stake by airlines with a high percentage of leverage. To preserve cash flows in times of distress successful airlines hedge and maintain their leverage at a low level. The variable long-term debt to assets (LTDA) is included to factor in any potential effect of leverage to risk exposure (Getzy et al., 1997; Nance et al., 1993; Tufano, 1996). With higher firm leverage compulsory interest payments increase. The inability to pay the interest rates will lead a firm into financial distress. The variable LTDA therefore takes financial distress into account (Dionne and Garand, 2003).

For airlines length of average flight is an important operational issue. The natural logarithm of average flight distance (LNDIS) allows us to control for several issue. First, the longer the average sector length is, the lower is the possibility for an airline to have an undiversified fleet. Ryanair, for example, cannot offer long distance flights with the current seat configuration of its Boeing 737-800 fleet. Air France-KLM, on the other hand, can operate domestic, short-distance as well as transatlantic flights with its currently 26 different aircraft models. Second, if the fuel situation at a destination airport renders it either inefficient or impossible to refuel the aircraft for the return flight airlines harness the method of tankering. Tankering refers to carrying a certain amount of fuel for the return flight on the outbound flight. Reasons for tankering include fuel shortage, fuel savings due to higher fuel prices or handling problems at the destination airport.21 The longer the outbound flight the higher is the required amount of fuel to be carried for the outbound flight and the less return fuel can be carried on board. The same principle applies to load factor. The higher the number of passengers on board, the lower is the capacity for return fuel due to aircraft weight restrictions. Third, the rule ‘The more you get the more you pay’ does not apply to the aviation industry. Flight tickets for domestic business flights are generally more expensive

20 Graham and Rogers (2002) use a sample of 442 non-financial firms in 1994/1995 which use currency or interest rate derivatives. 21 Rarely, business reasons such as breaches of contract or ongoing contract negotiations bring about tankering flights.

11 than longer distance charter flights. Also, marketing campaigns such as “Europe for 99 Euros” lead to diminishing marginal yield the longer the flight distance becomes. The average distance is either reported directly by an airline or the author calculates the variable below

/ ln ) (5)

All distances are subsequently converted into kilometer values. If no data is available for a certain period the distance from the adjacent year is used.

The variable passenger load factor (LF) describes which percentage of available seat kilometers are actually sold. With this important key performance indicator airlines compete globally. In times of high fuel prices it is of particular importance that as many as possible seats are sold to cover operating costs that arise regardless of the number of passengers flown, e.g. flight crew or maintenance costs. The continuous rise in load factor reflects the operational importance of well-booked flights. Despite the economic downturns over the last few years operational decisions by airline managers have led to higher load factors.

Figure 2 Load factor

The load factor per region is the averaged load factor of each airline of that region. Data is collected manually from the airlines' annual reports and 10-K filings. 82.0 80.0 78.0 76.0 74.0

LF in in LF % 72.0 70.0 68.0 66.0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Total Asia Europe North America

we test a number of hypotheses:

H1: Airlines are equally exposed to jet fuel prices regardless of their home base (γASIA,y= γEU,y= γNA,y). Due to the globally traded commodity jet fuel the author expects the same or similar levels of exposure independent of the origin of the airline.

H2: Financial hedging reduces an airline’s fuel price risk exposure (α1<0). Financial hedging is generally applied to reduce a firm’s exposure.

H3: The higher an airline’s fleet diversity the greater the reduction in exposure (α3<0). In times of high fuel prices and low demand airlines can operate aircraft models with lower seat numbers and vice versa.

H4: The longer the average flight distance the greater an airline is exposed to jet fuel prices (α6>0). The incoherent law of ticket prices and flight distance may make longer distance flights less efficient. The longer a flight the more fuel must be carried on board and the lower the opportunity for tankering.

12 H5: With increasing load factor risk exposure can be reduced (α7<0). Certain costs arise regardless of the number of seats sold on a flight, e.g. flight crew expenses. The greater the number of passengers on board the lower will be the level of risk exposure.

4. DATA, FINDINGS AND ANALYSIS

4.1. Descriptive results

Figure 3 displays the increase in fuel costs as a percentage of operating costs over the last 11 years. From 2002 to 2012 the total percentage of fuel costs doubled from 15.25 percent annually to 33.34 percent annually, an increase of 119 percent22. Asian airlines show the highest percentage of fuel costs, most likely due to their overall lower cost base especially in staff expenses.

Table 3 Fuel cost in percentage of operating cost, 2002 - 2012

Own figure. Fuel cost in percentage of operating cost are either reported by airlines directly in their annual reports or calculated by the researcher, dividing fuel expenses by total operating expenses. 40.00

35.00

30.00

25.00

20.00

15.00 Fuel cost in in cost Fuel % costoperating of 10.00 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Asia Europe North America TOTAL

Fuel costs account for a higher percentage of operating costs with low-cost carriers than with value carriers. With their business model LCCs can influence costs such as staff, catering, cleaning and airport fees. However, with the globally traded commodity jet fuel low-cost airlines have to obey the market and pay the same price as value carriers. Consequently, fuel costs are relatively higher in comparison to other operating costs with low-cost carriers.

22 A detailed summary of each airline’s cost structure is available on request

13 Figure 3 Comparison of fuel costs of LCCs versus value carriers

Own figure. Low-cost carriers comprise AirAsia, Tiger Airways, Virgin Australia, Airberlin, easyjet, Norwegian Airshuttle, Ryanair, Vueling, airTran, Allegiant Travel Company, Frontier Airlines, JetBlue Airways, Southwest Airlines and Spirit Airlines. 40.00

35.00

30.00

25.00

20.00

15.00

Fuel cost in % operating expense operating in % cost Fuel 10.00 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

LCC Value carrier

The overall average percentage of next year’s fuel requirements hedged is 24.29 percent. The average hedging maturity is 18.68 months (of those airlines that disclose maturity data) with a median of 12 months. Hence, some airlines make use of very long hedging horizons.23 Of the 586 financial reports, 93 percent enclose the type of financial instrument used. The majority of airlines use options, with 153 options (without further details), 176 call options and 13 put options followed by 220 swaps. Ninety-seven annual reports report the usage of collars24. Some airlines only report the employment of future or forward contracts while other airlines do not report the instrument at all.

Figure 4 Underlying commodity in financial hedging contracts

Data manually collected from airlines' annual reports and 10-K filings. Of the 586 reports, 434 annual reports contain information about the underlying commodity. Diesel oil, 1.61% Gas oil, 7.60%

Heating oil, 15.21% Jet fuel, 42.40%

Crude oil, 33.18%

Some country specific information can aid us in understanding hedging strategies used by international airliners. China Southern Airlines (2013), for example, reports that domestic regulations instruct local airlines to purchase all fuel requirements on the Chinese spot market, thus inhibiting any opportunities for financial hedging. Japanese accounting standards do not request Japanese firms to disclose any contract amounts or estimated fair values of open

23 Southwest uses the longest hedge maturity in 2006 and 2008 with 72 months. 24 Collars are derivatives that combine a put and a call option.

14 derivative positions (JAL Group, 2008). Jet Airways (2013) explains its non-hedged position with a still low maturity level in contrast to “more established carriers such as Lufthansa, [and] Singapore Airlines” (p.119). Moreover, airlines with liquidity bottlenecks report their inabilities of entering financial contracts, supporting the theoretical disadvantage of hedging in financially tight situation. Examples include Swiss International Air Lines in 2004 and United Air Lines in 2003.

4.2. Risk exposure coefficients

Table 4 reports the summary of jet fuel exposure coefficients There are a total of 555 observations, with 200 North American, 183 Asian and 172 European exposure coefficients. Airline-years with less than 26 observations are disregarded in the analysis. The overall mean (median) exposure coefficient is -0.131 (-0.091). Approximately 68 percent of all coefficients are negative and 28 percent are significantly less than 0 at the 10 percent level using a one- sided t-test. North America exhibits the highest percentage of significant observations with 41.5 percent, followed by Asian airlines with 24.6 percent and Europe with 15.7 percent.

Table 4 Summary statistics of jet fuel exposure coefficients TOTAL ASIA EUROPE NA PREMIUM LCC

Number of Observations 555 183 172 200 445 110 Mean γ -0.131 -0.104 -0.019 -0.247 -0.147 -0.074 Median γ -0.091 -0.085 -0.019 -0.191 -0.098 -0.041 Standard error γ 0.223 0.189 0.187 0.282 0.228 0.214 Minimum γ -1.794 -0.726 -0.718 -1.794 -1.794 -1.222 Max γ 1.621 0.671 1.228 1.621 1.621 0.840 % Negative γ 67.86% 68.76% 56.98% 76.19% 68.09% 68.18% Percent significant at 10% 32.88% 27.15% 22.67% 46.16% 30.17% 42.52% Airlines in highest Quartile 16 4 1 11 13 3

Number γ significantly different from 0 10% level 127 32 27 66 5% level 73 16 11 44 Number γ significantly less than 0 10% level 155 45 27 83 5% level 98 44 18 57 Number γ significantly greater than 0 10% level 30 9156 5% level 16 574

In contrast, Treanor et al. (2012b) find that more than 70 percent of U.S. airlines show significantly negative exposure coefficients between 1994 and 2008. Either, U.S. airlines have been less exposed over the last four years or their statistical methods and data25 differ so drastically that the differences can be explained. The decrease in exposure coefficients after the record year in terms of fuel prices 2008 becomes apparent in Figure 5 Mean and median exposure coefficients and jet fuel volatility, 2002 - 2012. The strong gap between mean and

25 While we for example use weekly stock data, Treanor et al. (2012b) use daily stock data in their analysis. Moreover, the airlines analyzed in their research differ slightly.

15 median values in 2008 is a sign for a few, strongly negatively exposed airlines. US Airways is the highest exposed airline in the entire period of analysis, with an exposure coefficient of - 1.794 (significant at the 0.01 percent level) in 2008. The red line represents the jet fuel volatility over the same period. The correlation coefficient for the median (mean) exposure coefficient and the 10-day volatility is -1.18 (-1.61). Therefore, as the volatility has decreased by almost 80 percent between 2008 and 2012 so has the risk exposure.

Figure 5 Mean and median exposure coefficients and jet fuel volatility, 2002 - 2012

The fuel price exposure is calculated with weekly airline, market index and jet fuel returns. The 10-da y jet fuel vola tility is ca lcula ted using da ily U.S. Gulf Coa st returns. Data are obtained from Datastream, research.stlouis.org and EIA.gov. 0 1.200 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 -0.05 1.000 -0.1 0.800 -0.15

-0.2 0.600 Exposure Volatility -0.25 0.400 -0.3 0.200 -0.35

-0.4 0.000

Mean exposure Median exposure 10-da y jet fuel vola tility

Eleven of the 16 airlines in the highest quartile of exposure are North American airlines. The quartiles are calculated using the median value of each airline calculated with the yearly exposure coefficients. We examine the regional annual exposure coefficients using a two sided t-test. Table 5 clearly shows significant differences in the annual exposure coefficients between Asian, European and North American airlines.

Table 5 Results of univariate analysis of regional exposure differences

T-statistic between Difference in regional exposure coefficients regions (2-sided)

Asia - Europe -3.204 *** Asia - North America 4.604 *** Europe - North America 6.661 ***

Significance levels ***1%, **5%, *10%. Consistent with the results of the number of statistically significant negative exposure coefficients, Asian carriers are more negatively exposed than European airlines but less exposed than North American airlines. Consequently, we rejects H1 that airlines are equally exposed to jet fuel prices regardless of their home base. A hypothetical reason for the higher exposure of North American airlines could be the higher volatility of U.S. Gulf Coast

16 kerosene prices in comparison to Singapore jet fuel prices. However, that assumption omits the fact that the European exposure coefficients are also estimated using U.S. jet fuel prices. Hence, there must be other reasons why North American airlines are more exposed to fuel prices than the other regions. Carter and Simkins (2004) examine the impact the terroristic attacks of 9/11 had on stock returns of major U.S. and international airlines. They find similar results that U.S. airlines were hit much more severely in terms of decreased stock returns than the seven international carriers. They assume market participants expected the U.S. airline industry to be hit much harder by the terrorist attacks than international airlines. Similarly, the author suggests that the statistically proven greater risk exposure of North American than European or Asian carriers could spring from investor concerns. If fuel price induced market expectations lead to changes in airline stock prices the exposure coefficients are affected simultaneously.

Table 6 contains a detailed summary of each airline individually. Analogous to the above mentioned regional differences in significant exposure coefficients, the percentage of negative exposure period is highest among North American firms (76.19 percent), followed by Asian carriers (68.76 percent) and European airlines (56.98 percent). Interestingly, the reverse order can be observed in the percentage of years hedged. European airlines used financial instruments in almost 83 percent of the reported periods, Asian airlines 74 percent and North American airlines 56 percent. The low number of years, in which financial instruments were employed in North America, could be explained by the number of capacity purchase agreements (CPAs) in force in the U.S. With a CPA or fixed-fee agreement the “major airline purchases the regional airline’s flying capacity by paying pre-determined rates for specified flying, regardless of the number of passengers on board” (Pinnacle Airlines Corp. 2012, p.9). Consequently, the regional airline is not directly impacted by fuel price risk as fuel costs are generally reimbursed by the purchaser. To control for CPA in the second step of the analyses we add the dummy variable REG if a regional carrier avails of CPA or fixed-fee agreements.26

26 Six of the 24 North American airlines can be classified as REG. These include: Air Transport Services, Expressjet, Mesa Air Group, Pinnacle Airlines, Republic Airways, and SkyWest.

17 Table 6 Detailed statistic of airline fuel exposure and financial hedging activity Percent Percent of next significant at years fuel hedged 10% level (only the years are (one-sided included where Airline MEAN MEDIAN SE MIN MAX % NEG test) data available) AirAsia -0.175 -0.231 0.162 -0.410 0.143 75.00% 62.50% 85.71% Air China -0.280 -0.248 0.311 -0.534 -0.008 100.00% 16.67% 66.67% Air New Zealand 0.027 0.053 0.173 -0.255 0.215 27.27% 9.09% 100.00% All Nippon Airways 0.030 -0.036 0.109 -0.099 0.410 72.73% 18.18% 100.00% Cathay Pacific Airways -0.120 -0.061 0.124 -0.325 0.021 81.82% 27.27% 100.00% China Airlines -0.180 -0.083 0.144 -0.684 0.078 90.91% 36.36% 90.00% China Eastern -0.123 -0.093 0.233 -0.601 0.239 63.64% 18.18% 54.55% China Southern -0.199 -0.163 0.264 -0.496 0.141 88.89% 18.18% 18.18% Eva Air -0.191 -0.197 0.159 -0.659 0.099 72.73% 45.45% 90.00% Garuda Indonesia -0.172 -0.172 0.262 -0.214 -0.130 100.00% 0.00% 60.00% JAL -0.003 -0.026 0.107 -0.136 0.177 50.00% 12.50% 100.00% Jet Airways -0.131 -0.016 0.241 -0.645 0.259 57.14% 28.57% 0.00% Korean Air -0.245 -0.228 0.199 -0.725 0.139 90.91% 45.45% 72.73% Malaysia Airlines -0.110 -0.102 0.157 -0.482 0.391 63.64% 36.36% 100.00% Pakistan International 0.013 -0.086 0.243 -0.269 0.561 54.55% 27.27% 9.09% Qantas -0.040 -0.040 0.149 -0.165 0.094 63.64% 0.00% 100.00% Singapore Airlines 0.017 0.011 0.123 -0.208 0.260 45.45% 36.36% 100.00% Thai Airways -0.171 -0.167 0.165 -0.584 0.172 72.73% 54.55% 71.43% Tiger Airways -0.014 -0.001 0.191 -0.110 0.068 66.67% 0.00% 100.00% Virgin Australia -0.020 0.107 0.261 -0.726 0.671 37.50% 50.00% 63.64% Subtotal Asia -0.104 -0.085 0.189 -0.726 0.671 68.76% 27.15% 74.10% Aegean Airlines 0.048 0.025 0.174 -0.112 0.159 20.00% 0.00% 55.56% Aer Lingus -0.065 -0.053 0.211 -0.200 0.058 66.67% 0.00% 100.00% Aeroflot 0.038 0.042 0.139 -0.180 0.271 36.36% 9.09% 20.00% Airberlin -0.021 0.063 0.209 -0.415 0.163 42.86% 14.29% 100.00% Air France-KLM -0.074 -0.102 0.150 -0.436 0.279 72.73% 36.36% 100.00% Austrian Airlines -0.076 -0.088 0.150 -0.183 0.072 75.00% 0.00% 71.43% British Airways -0.050 -0.075 0.140 -0.235 0.272 77.78% 50.00% 100.00% Cyprus Airways 0.202 0.189 0.419 -0.718 1.228 25.00% 37.50% 80.00% Deutsche Lufthansa -0.113 -0.103 0.106 -0.412 0.096 72.73% 36.36% 100.00% easyJet -0.031 -0.034 0.182 -0.182 0.254 63.64% 9.09% 90.91% Finnair 0.077 0.041 0.129 -0.226 0.440 36.36% 54.55% 100.00% IAG -0.222 -0.222 0.252 -0.266 -0.178 100.00% 0.00% 100.00% Iberia 0.001 -0.014 0.124 -0.412 0.393 55.56% 44.44% 88.89% Icelandair -0.100 -0.016 0.165 -0.663 0.047 63.64% 9.09% 55.56% Norwegian Airshuttle -0.106 -0.049 0.227 -0.398 0.129 55.56% 22.22% 70.00% Ryanair -0.009 -0.013 0.142 -0.226 0.169 54.55% 18.18% 90.91% SAS -0.181 -0.166 0.206 -0.524 0.002 90.91% 27.27% 100.00% Swiss Int. Air Lines 0.027 0.035 0.224 -0.103 0.142 50.00% 0.00% 100.00% Turkish Airlines -0.067 -0.023 0.148 -0.505 0.187 63.64% 18.18% 36.36% Vueling 0.337 0.400 0.248 -0.152 0.840 16.67% 66.67% 100.00% Subtotal Europe -0.019 -0.019 0.187 -0.718 1.228 56.98% 22.67% 82.98%

18 Percent Percent of next significant at years fuel hedged 10% level (only the years are (one-sided included where Airline MEAN MEDIAN SE MIN MAX % NEG test) data is avbl) Air Canada -0.104 -0.119 0.397 -0.901 0.723 66.67% 22.22% 81.82% Air Transport Services 0.249 0.091 0.325 -0.274 1.621 30.00% 20.00% 0.00% airTran -0.337 -0.156 0.219 -1.222 -0.018 100.00% 30.00% 100.00% Alaska Air Group -0.297 -0.178 0.153 -0.930 0.074 90.91% 45.45% 100.00% Allegiant Travel -0.259 -0.309 0.203 -0.651 0.209 83.33% 66.67% 28.57% American Airlines -0.383 -0.392 0.369 -1.000 0.271 81.82% 63.64% 100.00% Atlas Air 0.054 0.042 0.191 -0.439 0.490 25.00% 37.50% 0.00% Continental Airlines -0.441 -0.381 0.223 -1.073 -0.003 100.00% 66.67% 66.67% Delta Air Lines -0.449 -0.392 0.291 -1.040 -0.097 100.00% 50.00% 81.82% Expressjet -0.305 -0.267 0.284 -1.096 0.599 66.67% 44.44% 12.50% Frontier Airlines -0.341 -0.296 0.340 -1.133 0.133 75.00% 37.50% 62.50% Great Lakes Aviation 0.007 0.039 0.501 -0.642 0.497 27.27% 9.09% 0.00% Hawaiian Holdings -0.358 -0.373 0.263 -0.716 0.069 90.91% 54.55% 100.00% JetBlue Airways -0.282 -0.190 0.201 -0.720 -0.010 100.00% 54.55% 100.00% Mesa Air Group -0.549 -0.215 0.439 -1.762 -0.050 100.00% 44.44% 0.00% Midwest Air Group -0.035 -0.017 0.270 -0.268 0.179 50.00% 0.00% 80.00% Northwest Airlines -0.414 -0.414 0.370 -0.668 -0.161 100.00% 50.00% 71.43% Pinnacle Airlines -0.122 -0.139 0.266 -0.478 0.365 75.00% 25.00% 11.11% Republic Airways -0.175 -0.162 0.237 -0.562 0.221 75.00% 25.00% 22.22% SkyWest -0.233 -0.193 0.166 -0.732 0.200 90.91% 54.55% 0.00% Southwest Airlines -0.180 -0.144 0.113 -0.578 0.000 100.00% 63.64% 100.00% Spirit Airlines 0.401 0.401 0.296 0.382 0.420 0.00% 100.00% 100.00% United Continental -0.659 -0.539 0.312 -1.317 -0.216 100.00% 85.71% 72.73% US Airways -0.705 -0.432 0.345 -1.794 -0.141 100.00% 57.14% 54.55% Subtotal North America -0.247 -0.191 0.282 -1.794 1.621 76.19% 46.16% 56.08%

TOTAL -0.131 -0.091 0.223 -1.794 1.621 67.86% 32.88% 70.12%

In a univariate analysis of low versus high exposed airlines we test for statistically significant differences in the regression variables used High-exposed (the more negatively exposed) airlines are classified as airlines with a median exposure coefficient in the highest 25 percent. Low-exposed airlines (the more positively exposed) exhibit a median value in the lowest 25 percent of all median coefficients. The variable HDG, percentage of periods hedged, is included in the cross-sectional dataset. The results reveal that high-exposed airlines have higher leverage and are greater in size. They also show a higher level of fleet diversification and a higher load factor than low-exposed airlines. Contrary to Treanor et al. (2012b), the two categories do not differ significantly in their percentage fuel hedged or percentage of periods hedged.

Table 7 Summary statistics for high/medium/low exposed airlines Low- Medium- High- T-statistic Variable exposed exposed exposed low/high airline airline airline (2-sided)

LTDA (long-term debt to assets ratio) 0.244 0.333 0.329 5.986 ***

LNTA (ln of total assets) 14.03 15.36 15.30 6.120 ***

ADI_1 (aircraft dispersion index 1) 0.613 0.700 0.725 4.9203 ***

ADI_2 (aircraft dispersion index 2) 0.427 0.600 0.614 7.784 ***

HDG (percentage of periods hedged) 0.633 0.709 0.688 0.935

HDGPER (percentage of next year's fuel 0.206 0.287 0.181 -0.734 requirements hedged) HDGMAT (maximum maturity of fuel 16.66 22.03 16.57 -0.076 derivatives in months)

DIST (average flight distance in km) 1863.13 1752.57 1887.07 0.399

LF (passenger load factor) 0.724 0.748 0.769 3.353 ***

` Significance levels ***1%, **5%, *10%

4.3. The effects of financial and operational hedges on risk exposure

We show in Table 8 the results of a regression analysis on the dependent variable annual fuel price risk exposure coefficient for each airline against the independent variables discussed earlier. A Hausman test suggests that a fixed effects model is appropriate . The models include year dummy variables which are not reported. Model 1 and 2 report the results using ADI_1 variable representing fleet diversity. On the other hand, Model 3 and 4 control for fleet diversity with variable ADI_2. Model 2 and 4 are clustered on airline ID, controlling for heteroscedasticity and autocorrelation27.

27 The cluster option in Stata has almost the same effect as using the robust standard error function with fixed effect models (Torres-Reyna, 2013).

20 Table 8 The effectiveness of financial and operational hedging variables

This table discloses the results of.

, , , 1, ,

Model 1 and 2 include ADI_1 as an estimator for fleet diversity, whereas Model 3 and 4 use ADI_2 as fleet diversity variable. Model 1 and 3 report the results of a fixed effect model, Model 2 and 4 control for a clustered sample (clustered for airline ID). All models include year dummy variables not reported. The t-statistic is presented in parentheses. The significance levels are * p < 0.10, ** p < 0.05, *** p < 0.01.

Model 1 Model 2 Model 3 Model 4 Fixed effects Fixed effects / Fixed effects Fixed effects / cluster cluster

HDGPER -0.00749 -0.00749 0.00786 0.00786 (-0.08) (-0.06) (0.08) (0.07)

HDGMAT 0.000785 0.000785 0.00148 0.00148 (0.29) (0.30) (0.55) (0.61)

ADI_1 -0.574** -0.574** (-2.39) (-2.52)

ADI_2 -0.340* -0.340** (-1.93) (-2.07)

LNTA 0.0569** 0.0569*** 0.0559** 0.0559*** (2.02) (2.98) (2.00) (2.79)

LTDA -0.297 -0.297 -0.289 -0.289 (-1.05) (-0.88) (-1.04) (-0.84)

LNDIS 0.120 0.120 0.0836 0.0836 (0.54) (0.75) (0.38) (0.49)

LF -0.0437 -0.0437 -0.174 -0.174 (-0.06) (-0.05) (-0.24) (-0.19)

Constant -1.273 -1.273 -1.121 -1.121 (-0.72) (-0.94) (-0.65) (-0.75) No. of observations 195 195 197 197 Rho .6920 .6920 .6272 .6272 F-statistic 0.0088 0.0000 0.0139 0.0000

The results of Model 1 and 2 reject H2 that financial hedging reduces an airline’s fuel price risk exposure. We also analyses each region individually and comes to the same conclusion that financial hedging does not reduce risk exposure. Even when using North American observations only, the coefficients of the variables HDGPER and HDHMAT are not significant. These results are in contrast to prior research where most researchers find that the use of financial derivatives reduces risk exposure. Different estimation periods could cause the diverging results. While Treanor et al. (2012b) analyze the period 1994-2008, this paper

21 comprises the years 2002-2012. After 2008, the volatility in jet fuel prices has decreased enormously. In 2012, the annualized standard deviation calculated with daily stock return of U.S. Gulf Coast (USG) and Singapore jet fuel prices (SIN) has reached the lowest level over the last decade. The goal of hedging is to reduce uncertainty. If uncertainty in jet fuel prices has been decreasing over the last few years, consequently, the use of financial hedges is less effective than in the past.

Table 9 Annualized standard deviation calculated with daily stock returns Airlines Jet fuel ASIA EUROPE NORTH AMERICA USG SIN 2002 0.183 0.249 0.452 0.338 0.278 2003 0.183 0.232 0.503 0.452 0.387 2004 0.146 0.168 0.305 0.432 0.311 2005 0.106 0.132 0.242 0.578 0.369 2006 0.119 0.142 0.260 0.352 0.254 2007 0.165 0.186 0.247 0.262 0.205 2008 0.290 0.316 0.675 1.007 0.415 2009 0.203 0.218 0.488 0.448 0.400 2010 0.162 0.184 0.292 0.273 0.276 2011 0.179 0.184 0.295 0.244 0.253 2012 0.112 0.131 0.231 0.202 0.162

MIN 0.106 0.131 0.231 0.202 0.162 MAX 0.290 0.316 0.675 1.007 0.415 MEAN 0.168 0.195 0.363 0.417 0.301 MEDIAN 0.165 0.184 0.295 0.352 0.278

Figure 6 shows the U.S. Gulf Coast volatility over a longer time horizon. A linear trendline shows a tiny negative slope (-6E-06), indicating a static or barely decreased jet fuel spot price volatility over the last 23 years.

Figure 6 – U.S. Gulf Coast jet fuel volatility, 1990-2012

Own figure calculated as 10-day volatility, using logarithmic changes of U.S. Gulf Coast Kerosene-Type jet fuel spot prices. Data taken from EIA.gov for the period 02 Jan 1990 - 31 Dez 2012. 2.5

1.5 SD 1

0.5

0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 10-da y vola tility Linear (10-day volatility)

22 Hentschel and Kothari (2001) do not find evidence that interest rate or currency hedges reduce risk exposure either. The authors justify their findings with the assumption that firms hedge to reduce short-term contract risk. As short-term contracts only constitute a small portion of firm value, these contracts do not have a great impact on risk reduction. Other reasons for the still large percentage of fuel requirements hedged among all international airlines could be the managerial or reputational incentives Also, the variables HDGPER and HDGMAT do not cover all aspects of an airline’s financial hedging strategy. The more sophisticated the strategy in terms of timing, hedge ratio and underlying commodity is, the larger will be the effectiveness of derivatives. Consequently, different variables estimating financial hedging strategies might yield different results in future research.

The results support H3, the higher an airline’s fleet diversity the greater the reduction in exposure. In both models, a more diverse fleet defined by variable ADI_1 reduces airline risk exposure. The results are significant at the 5 percent level. Similar to the calculations of Treanor et al. (2012b)28, a one percentage point increase in fleet diversity (ADI_1) would lead to a reduction of the risk exposure coefficient of 2.99 percent. Yet, as pointed out earlier some detriments arise with a more diverse fleet such as higher maintenance or purchasing costs. In fact, a diverse fleet could even reduce operational flexibility. Airlines generally use standby crews to cover for any potential irregularity in daily operations. Standby crews await their flight assignment at the airport in case of defect aircraft, delayed crews or other unexpected abnormities. If, however, the cockpit crew has a type rating for the Airbus 320 family and the only available spare aircraft is a Boeing 737, the crew cannot operate the replacement flight. In this case, greater fleet diversity in the same aircraft distance category reduces operational flexibility. To cover for these caveats we use fleet diversity variable ADI_2. The results of Model 3 and 4 support H3 even when using the more aggregated variable ADI_2 for fleet diversity. The results are significant at the 10 percent level in Model 3 and at the 5 percent level in Model 4, which clusters on airline ID. In this case, a one percent increase in fleet diversity would lead to a 1.45 percent reduction of risk exposure.29 These results can serve as a valuable tool for airline managers in choosing the optimal degree of fleet diversity. While ADI_1 occasions more operating costs it reduces fuel price exposure to a greater extent than the less cost intensive alternative of ADI_2.

28 The value of 2.99 percent is calculated with the average ADI_1 (0.683) multiplied by the coefficient of ADI_1 (0.574). The number is then multiplied by 0.01 and divided by the average risk exposure coefficient (0.131) 29 The value of 1.45 percent is calculated analogous with the average ADI_2 (0.56) multiplied by the coefficient of ADI_2 (0.34) multiplied again by 0.01 and divided by the average risk exposure coefficient (0.131).

23 Figure 7 – Comparison of fleet diversity variables ADI_1 and ADI_2, 2002-2012

ADI_1 and ADI_2 are calculated with Equation (1) and (2). The values are average values of each airline's yearly fleet diversity index. Data taken from annual reports and airline websites. 0.75 0.16 0.14 0.70 0.12

0.65 0.10 0.08 0.60 0.06 Fleet diversity Fleet 0.04 0.55 0.02 0.50 0.00 Difference between ADI_1 and ADI_2 and ADI_1 between Difference 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 ADI 1 - ADI 2 ADI 1 ADI 2

the evidence from Figure 7 illustrate the trend in the global aviation industry to less fleet diversification. European airlines, in particular, have reduced their fleet diversity by 23.12 percent (ADI_1) or 28.04 percent (ADI_2) between 2002 and 2012. Asian airlines have reduced their fleet diversity by 3.92 percent (ADI_1) or 4.36 percent (ADI_2). Although North American airlines’ fleet diversity has increased in terms of aircraft models by 3.04 percent (ADI_1), they managed to reduce the number of aircraft families by 5.02 percent (ADI_2).

The results lead to a rejection of H4 and H5. Neither a longer average flight distance nor a smaller load factor lead to a significant increase of airline risk exposure.30 The results indicate the tendency that, as expected, a longer flight distance increases exposure. The results are not significant, though, implying that other effects have to be regarded with flight distance. Although a longer flight distance diminishes for example the opportunity for tankering, longer flights are in general more fuel efficient than short-haul flights.31 On the one hand, long-haul aircraft can carry more passengers and hence lower the fuel consumption per passenger. On the other hand, take-off and approach phases of a flight consume the highest amount of the overall trip fuel. As short-haul flights comprise more of these phases with high fuel flow compared to long-haul flights, short-haul flights have an increasing effect on risk exposure. Similar to the implications of H4, the results imply that a higher load factor reduces risk exposure as expected. However, the results are not significant at the 10 percent level. While higher load factors reduce exposure due to more efficiently operated flights they also lead to higher aircraft take-off weights. The higher the weight of an aircraft the higher is the fuel consumption .

30 Stata omits the dummy variable REG due to collinearity. To control for CPAs regardless, we also run analyses where we exclude the six regional carriers. All results are essentially identical with the previous results. . 31 According to Lufthansa internal information, the most fuel efficient sector length of long range flights is six flight hours. For flights longer than six hours the fuel used for each additional flight hour increases because the fuel has to be carried over a long distance. Payload, e.g. passengers or freight, has to be reduced to clear space for fuel.

24 5. CONCLUSION AND IMPLICATIONS

The airline industry has been transitioning towards becoming a commoditized market. The continuously high fuel prices over the past few years have eroded already low profit margins. Thus, airline managers try to counteract this process with financial and operational hedging. In this study, 20 Asian, 20 European and 24 North American airlines are analyzed between 2002 and 2012.

We find lower exposure to fuel risk than in previous studies. We assume that the number of negative exposure coefficients has decreased in our study as the volatility of jet fuel prices has decreased in our sample as compared to previous. As median exposure coefficients are closely correlated with the 10-day jet fuel volatility (-1.18), a decrease in volatility leads to a reduction in airline exposure. Nevertheless, airlines are still exposed to jet fuel prices, especially North American airlines. We finds significant (one percent level) differences in exposure coefficients between the three regions. Asian carriers are more exposed than European airlines but less exposed than North American. High-exposed airlines have higher leverage and are larger in size. They also show a higher level of fleet diversification and a higher load factor than low-exposed airlines. They do not differ significantly in their percentage fuel hedged or percentage of periods hedged.

The results of a fixed effects panel analysis reject the hypothesis that financial hedging reduces exposure. Even analyzing each region in a separate model we do not find any significant reduction in exposure. In contrast to financial hedging, operational hedging defined by fleet diversity reduces risk exposure significantly. A once percentage point increase in fleet diversity (ADI_1) would lead to a reduction of the risk exposure coefficient of 2.99 percent. Yet, fleet diversity entails high costs such as maintenance and or crew costs. Therefore, we also use a second measure of fleet diversity (ADI_2), which covers aircraft families instead of aircraft types. The results hold, as with this variable, a one percent increase in fleet diversity would lead to a 1.45 percent reduction of risk exposure. Consequently, aviation managers have to weigh operational flexibility against higher costs related to fleet diversity. The two variables, ADI_1 and ADI_2, can serve as a starting point for the decision making process. In fact, airlines have reduced their fleet diversity by 7.70 percent (ADI_1) or 10.77 percent (ADI_2) between 2002 and 2012. The greatest reduction can be found among European airlines with 23.12 percent (28.04 percent). We do not find that longer flights or a smaller load factor increase airline risk exposure

In further research, a more comprehensive analysis of fleet diversity could lead to a better understanding of the optimum level of fleet diversity. The analysis should combine risk exposure aspects, associated costs and benefits of operating a diverse fleet. Different proxies for operational hedging such as alliances, route networks or flight procedures could enhance the knowledge of the effectiveness of operational hedging. Also, a closer look at current research and development projects of aircraft manufacturers might yield insight into the development of future aircraft fleets. Although a large percentage of international aviation market shares are covered with the examined airlines, a more comprehensive study would have included South American, African and Middle Easter airlines. However, Middle Eastern airlines in particular do not report essential financial data that could have been used in this analysis. The results of this paper have clearly demonstrated that further research is necessary in the field of hedging in the aviation industry. While only a few few studies exist about fuel hedging, their results are inconsistent and the data limited. the diverging findings regarding the effectiveness of financial hedging render it necessary to further implore in that direction to give consistent managerial implications for the aviation industry. On the basis of the current findings, financial hedging seems significantly less effective than operational hedging.

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29 Institute for International Integration Studies The Sutherland Centre, Trinity College Dublin, Dublin 2, Ireland