The Risks and Rewards of Selling Volatility
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Implied Volatility Modeling
Implied Volatility Modeling Sarves Verma, Gunhan Mehmet Ertosun, Wei Wang, Benjamin Ambruster, Kay Giesecke I Introduction Although Black-Scholes formula is very popular among market practitioners, when applied to call and put options, it often reduces to a means of quoting options in terms of another parameter, the implied volatility. Further, the function σ BS TK ),(: ⎯⎯→ σ BS TK ),( t t ………………………………(1) is called the implied volatility surface. Two significant features of the surface is worth mentioning”: a) the non-flat profile of the surface which is often called the ‘smile’or the ‘skew’ suggests that the Black-Scholes formula is inefficient to price options b) the level of implied volatilities changes with time thus deforming it continuously. Since, the black- scholes model fails to model volatility, modeling implied volatility has become an active area of research. At present, volatility is modeled in primarily four different ways which are : a) The stochastic volatility model which assumes a stochastic nature of volatility [1]. The problem with this approach often lies in finding the market price of volatility risk which can’t be observed in the market. b) The deterministic volatility function (DVF) which assumes that volatility is a function of time alone and is completely deterministic [2,3]. This fails because as mentioned before the implied volatility surface changes with time continuously and is unpredictable at a given point of time. Ergo, the lattice model [2] & the Dupire approach [3] often fail[4] c) a factor based approach which assumes that implied volatility can be constructed by forming basis vectors. Further, one can use implied volatility as a mean reverting Ornstein-Ulhenbeck process for estimating implied volatility[5]. -
Managed Futures and Long Volatility by Anders Kulp, Daniel Djupsjöbacka, Martin Estlander, Er Capital Management Research
Disclaimer: This article appeared in the AIMA Journal (Feb 2005), which is published by The Alternative Investment Management Association Limited (AIMA). No quotation or reproduction is permitted without the express written permission of The Alternative Investment Management Association Limited (AIMA) and the author. The content of this article does not necessarily reflect the opinions of the AIMA Membership and AIMA does not accept responsibility for any statements herein. Managed Futures and Long Volatility By Anders Kulp, Daniel Djupsjöbacka, Martin Estlander, er Capital Management Research Introduction and background The buyer of an option straddle pays the implied volatility to get exposure to realized volatility during the lifetime of the option straddle. Fung and Hsieh (1997b) found that the return of trend following strategies show option like characteristics, because the returns tend to be large and positive during the best and worst performing months of the world equity markets. Fung and Hsieh used lookback straddles to replicate a trend following strategy. However, the standard way to obtain exposure to volatility is to buy an at-the-money straddle. Here, we will use standard option straddles with a dynamic updating methodology and flexible maturity rollover rules to match the characteristics of a trend follower. The lookback straddle captures only one trend during its maturity, but with an effective systematic updating method the simple option straddle captures more than one trend. It is generally accepted that when implementing an options strategy, it is crucial to minimize trading, because the liquidity in the options markets are far from perfect. Compared to the lookback straddle model, the trading frequency for the simple straddle model is lower. -
White Paper Volatility: Instruments and Strategies
White Paper Volatility: Instruments and Strategies Clemens H. Glaffig Panathea Capital Partners GmbH & Co. KG, Freiburg, Germany July 30, 2019 This paper is for informational purposes only and is not intended to constitute an offer, advice, or recommendation in any way. Panathea assumes no responsibility for the content or completeness of the information contained in this document. Table of Contents 0. Introduction ......................................................................................................................................... 1 1. Ihe VIX Index .................................................................................................................................... 2 1.1 General Comments and Performance ......................................................................................... 2 What Does it mean to have a VIX of 20% .......................................................................... 2 A nerdy side note ................................................................................................................. 2 1.2 The Calculation of the VIX Index ............................................................................................. 4 1.3 Mathematical formalism: How to derive the VIX valuation formula ...................................... 5 1.4 VIX Futures .............................................................................................................................. 6 The Pricing of VIX Futures ................................................................................................ -
What Drives Index Options Exposures?* Timothy Johnson1, Mo Liang2, and Yun Liu1
Review of Finance, 2016, 1–33 doi: 10.1093/rof/rfw061 What Drives Index Options Exposures?* Timothy Johnson1, Mo Liang2, and Yun Liu1 1University of Illinois at Urbana-Champaign and 2School of Finance, Renmin University of China Abstract 5 This paper documents the history of aggregate positions in US index options and in- vestigates the driving factors behind use of this class of derivatives. We construct several measures of the magnitude of the market and characterize their level, trend, and covariates. Measured in terms of volatility exposure, the market is economically small, but it embeds a significant latent exposure to large price changes. Out-of-the- 10 money puts are the dominant component of open positions. Variation in options use is well described by a stochastic trend driven by equity market activity and a sig- nificant negative response to increases in risk. Using a rich collection of uncertainty proxies, we distinguish distinct responses to exogenous macroeconomic risk, risk aversion, differences of opinion, and disaster risk. The results are consistent with 15 the view that the primary function of index options is the transfer of unspanned crash risk. JEL classification: G12, N22 Keywords: Index options, Quantities, Derivatives risk 20 1. Introduction Options on the market portfolio play a key role in capturing investor perception of sys- tematic risk. As such, these instruments are the object of extensive study by both aca- demics and practitioners. An enormous literature is concerned with modeling the prices and returns of these options and analyzing their implications for aggregate risk, prefer- 25 ences, and beliefs. Yet, perhaps surprisingly, almost no literature has investigated basic facts about quantities in this market. -
Derivative Securities
2. DERIVATIVE SECURITIES Objectives: After reading this chapter, you will 1. Understand the reason for trading options. 2. Know the basic terminology of options. 2.1 Derivative Securities A derivative security is a financial instrument whose value depends upon the value of another asset. The main types of derivatives are futures, forwards, options, and swaps. An example of a derivative security is a convertible bond. Such a bond, at the discretion of the bondholder, may be converted into a fixed number of shares of the stock of the issuing corporation. The value of a convertible bond depends upon the value of the underlying stock, and thus, it is a derivative security. An investor would like to buy such a bond because he can make money if the stock market rises. The stock price, and hence the bond value, will rise. If the stock market falls, he can still make money by earning interest on the convertible bond. Another derivative security is a forward contract. Suppose you have decided to buy an ounce of gold for investment purposes. The price of gold for immediate delivery is, say, $345 an ounce. You would like to hold this gold for a year and then sell it at the prevailing rates. One possibility is to pay $345 to a seller and get immediate physical possession of the gold, hold it for a year, and then sell it. If the price of gold a year from now is $370 an ounce, you have clearly made a profit of $25. That is not the only way to invest in gold. -
Futures & Options on the VIX® Index
Futures & Options on the VIX® Index Turn Volatility to Your Advantage U.S. Futures and Options The Cboe Volatility Index® (VIX® Index) is a leading measure of market expectations of near-term volatility conveyed by S&P 500 Index® (SPX) option prices. Since its introduction in 1993, the VIX® Index has been considered by many to be the world’s premier barometer of investor sentiment and market volatility. To learn more, visit cboe.com/VIX. VIX Futures and Options Strategies VIX futures and options have unique characteristics and behave differently than other financial-based commodity or equity products. Understanding these traits and their implications is important. VIX options and futures enable investors to trade volatility independent of the direction or the level of stock prices. Whether an investor’s outlook on the market is bullish, bearish or somewhere in-between, VIX futures and options can provide the ability to diversify a portfolio as well as hedge, mitigate or capitalize on broad market volatility. Portfolio Hedging One of the biggest risks to an equity portfolio is a broad market decline. The VIX Index has had a historically strong inverse relationship with the S&P 500® Index. Consequently, a long exposure to volatility may offset an adverse impact of falling stock prices. Market participants should consider the time frame and characteristics associated with VIX futures and options to determine the utility of such a hedge. Risk Premium Yield Over long periods, index options have tended to price in slightly more uncertainty than the market ultimately realizes. Specifically, the expected volatility implied by SPX option prices tends to trade at a premium relative to subsequent realized volatility in the S&P 500 Index. -
Option Prices Under Bayesian Learning: Implied Volatility Dynamics and Predictive Densities∗
Option Prices under Bayesian Learning: Implied Volatility Dynamics and Predictive Densities∗ Massimo Guidolin† Allan Timmermann University of Virginia University of California, San Diego JEL codes: G12, D83. Abstract This paper shows that many of the empirical biases of the Black and Scholes option pricing model can be explained by Bayesian learning effects. In the context of an equilibrium model where dividend news evolve on a binomial lattice with unknown but recursively updated probabilities we derive closed-form pricing formulas for European options. Learning is found to generate asymmetric skews in the implied volatility surface and systematic patterns in the term structure of option prices. Data on S&P 500 index option prices is used to back out the parameters of the underlying learning process and to predict the evolution in the cross-section of option prices. The proposed model leads to lower out-of- sample forecast errors and smaller hedging errors than a variety of alternative option pricing models, including Black-Scholes and a GARCH model. ∗We wish to thank four anonymous referees for their extensive and thoughtful comments that greatly improved the paper. We also thank Alexander David, Jos´e Campa, Bernard Dumas, Wake Epps, Stewart Hodges, Claudio Michelacci, Enrique Sentana and seminar participants at Bocconi University, CEMFI, University of Copenhagen, Econometric Society World Congress in Seattle, August 2000, the North American Summer meetings of the Econometric Society in College Park, June 2001, the European Finance Association meetings in Barcelona, August 2001, Federal Reserve Bank of St. Louis, INSEAD, McGill, UCSD, Universit´edeMontreal,andUniversity of Virginia for discussions and helpful comments. -
The Impact of Implied Volatility Fluctuations on Vertical Spread Option Strategies: the Case of WTI Crude Oil Market
energies Article The Impact of Implied Volatility Fluctuations on Vertical Spread Option Strategies: The Case of WTI Crude Oil Market Bartosz Łamasz * and Natalia Iwaszczuk Faculty of Management, AGH University of Science and Technology, 30-059 Cracow, Poland; [email protected] * Correspondence: [email protected]; Tel.: +48-696-668-417 Received: 31 July 2020; Accepted: 7 October 2020; Published: 13 October 2020 Abstract: This paper aims to analyze the impact of implied volatility on the costs, break-even points (BEPs), and the final results of the vertical spread option strategies (vertical spreads). We considered two main groups of vertical spreads: with limited and unlimited profits. The strategy with limited profits was divided into net credit spread and net debit spread. The analysis takes into account West Texas Intermediate (WTI) crude oil options listed on New York Mercantile Exchange (NYMEX) from 17 November 2008 to 15 April 2020. Our findings suggest that the unlimited vertical spreads were executed with profits less frequently than the limited vertical spreads in each of the considered categories of implied volatility. Nonetheless, the advantage of unlimited strategies was observed for substantial oil price movements (above 10%) when the rates of return on these strategies were higher than for limited strategies. With small price movements (lower than 5%), the net credit spread strategies were by far the best choice and generated profits in the widest price ranges in each category of implied volatility. This study bridges the gap between option strategies trading, implied volatility and WTI crude oil market. The obtained results may be a source of information in hedging against oil price fluctuations. -
Derivatives: a Twenty-First Century Understanding Timothy E
Loyola University Chicago Law Journal Volume 43 Article 3 Issue 1 Fall 2011 2011 Derivatives: A Twenty-First Century Understanding Timothy E. Lynch University of Missouri-Kansas City School of Law Follow this and additional works at: http://lawecommons.luc.edu/luclj Part of the Banking and Finance Law Commons Recommended Citation Timothy E. Lynch, Derivatives: A Twenty-First Century Understanding, 43 Loy. U. Chi. L. J. 1 (2011). Available at: http://lawecommons.luc.edu/luclj/vol43/iss1/3 This Article is brought to you for free and open access by LAW eCommons. It has been accepted for inclusion in Loyola University Chicago Law Journal by an authorized administrator of LAW eCommons. For more information, please contact [email protected]. Derivatives: A Twenty-First Century Understanding Timothy E. Lynch* Derivatives are commonly defined as some variationof the following: financial instruments whose value is derivedfrom the performance of a secondary source such as an underlying bond, commodity, or index. This definition is both over-inclusive and under-inclusive. Thus, not surprisingly, even many policy makers, regulators, and legal analysts misunderstand them. It is important for interested parties such as policy makers to understand derivatives because the types and uses of derivatives have exploded in the last few decades and because these financial instruments can provide both social benefits and cause social harms. This Article presents a framework for understanding modern derivatives by identifying the characteristicsthat all derivatives share. All derivatives are contracts between two counterparties in which the payoffs to andfrom each counterparty depend on the outcome of one or more extrinsic, future, uncertain event or metric and in which each counterparty expects (or takes) such outcome to be opposite to that expected (or taken) by the other counterparty. -
VIX Futures As a Market Timing Indicator
Journal of Risk and Financial Management Article VIX Futures as a Market Timing Indicator Athanasios P. Fassas 1 and Nikolas Hourvouliades 2,* 1 Department of Accounting and Finance, University of Thessaly, Larissa 41110, Greece 2 Department of Business Studies, American College of Thessaloniki, Thessaloniki 55535, Greece * Correspondence: [email protected]; Tel.: +30-2310-398385 Received: 10 June 2019; Accepted: 28 June 2019; Published: 1 July 2019 Abstract: Our work relates to the literature supporting that the VIX also mirrors investor sentiment and, thus, contains useful information regarding future S&P500 returns. The objective of this empirical analysis is to verify if the shape of the volatility futures term structure has signaling effects regarding future equity price movements, as several investors believe. Our findings generally support the hypothesis that the VIX term structure can be employed as a contrarian market timing indicator. The empirical analysis of this study has important practical implications for financial market practitioners, as it shows that they can use the VIX futures term structure not only as a proxy of market expectations on forward volatility, but also as a stock market timing tool. Keywords: VIX futures; volatility term structure; future equity returns; S&P500 1. Introduction A fundamental principle of finance is the positive expected return-risk trade-off. In this paper, we examine the dynamic dependencies between future equity returns and the term structure of VIX futures, i.e., the curve that connects daily settlement prices of individual VIX futures contracts to maturities across time. This study extends the VIX futures-related literature by testing the market timing ability of the VIX futures term structure regarding future stock movements. -
WACC and Vol (Valuation for Companies with Real Options)
Counterpoint Global Insights WACC and Vol Valuation for Companies with Real Options CONSILIENT OBSERVER | December 21, 2020 Introduction AUTHORS Twenty-twenty has been extraordinary for a lot of reasons. The global Michael J. Mauboussin pandemic created a major health challenge and an unprecedented [email protected] economic shock, there was plenty of political uncertainty, and the Dan Callahan, CFA stock market’s results were surprising to many.1 The year was also [email protected] fascinating for investors concerned with corporate valuation. Theory prescribes that the value of a company is the present value of future free cash flow. The task of valuing a business comes down to an assessment of cash flows and the opportunity cost of capital. The stock prices of some companies today imply expectations for future cash flows in excess of what seems reasonably achievable. Automatically assuming these companies are mispriced may be a mistake because certain businesses also have real option value. The year 2020 is noteworthy because the weighted average cost of capital (WACC), which is applied to visible parts of the business, is well below its historical average, and volatility (vol), which drives option value, is well above its historical average. Businesses that have real option value benefit from a lower discount rate on their current operations and from the higher volatility of the options available to them. In this report, we define real options, discuss what kinds of businesses are likely to have them, and review the valuation implication of a low cost of capital and high volatility. We finish with a method to incorporate real options analysis into traditional valuation. -
Approximation and Calibration of Short-Term
APPROXIMATION AND CALIBRATION OF SHORT-TERM IMPLIED VOLATILITIES UNDER JUMP-DIFFUSION STOCHASTIC VOLATILITY Alexey MEDVEDEV and Olivier SCAILLETa 1 a HEC Genève and Swiss Finance Institute, Université de Genève, 102 Bd Carl Vogt, CH - 1211 Genève 4, Su- isse. [email protected], [email protected] Revised version: January 2006 Abstract We derive a closed-form asymptotic expansion formula for option implied volatility under a two-factor jump-diffusion stochastic volatility model when time-to-maturity is small. Based on numerical experiments we describe the range of time-to-maturity and moneyness for which the approximation is accurate. We further propose a simple calibration procedure of an arbitrary parametric model to short-term near-the-money implied volatilities. An important advantage of our approximation is that it is free of the unobserved spot volatility. Therefore, the model can be calibrated on option data pooled across different calendar dates in order to extract information from the dynamics of the implied volatility smile. An example of calibration to a sample of S&P500 option prices is provided. We find that jumps are significant. The evidence also supports an affine specification for the jump intensity and Constant-Elasticity-of-Variance for the dynamics of the return volatility. Key words: Option pricing, stochastic volatility, asymptotic approximation, jump-diffusion. JEL Classification: G12. 1 An early version of this paper has circulated under the title "A simple calibration procedure of stochastic volatility models with jumps by short term asymptotics". We would like to thank the Editor and the referee for constructive criticism and comments which have led to a substantial improvement over the previous version.