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Futures and Options Workbook
EEXAMININGXAMINING FUTURES AND OPTIONS TABLE OF 130 Grain Exchange Building 400 South 4th Street Minneapolis, MN 55415 www.mgex.com [email protected] 800.827.4746 612.321.7101 Fax: 612.339.1155 Acknowledgements We express our appreciation to those who generously gave their time and effort in reviewing this publication. MGEX members and member firm personnel DePaul University Professor Jin Choi Southern Illinois University Associate Professor Dwight R. Sanders National Futures Association (Glossary of Terms) INTRODUCTION: THE POWER OF CHOICE 2 SECTION I: HISTORY History of MGEX 3 SECTION II: THE FUTURES MARKET Futures Contracts 4 The Participants 4 Exchange Services 5 TEST Sections I & II 6 Answers Sections I & II 7 SECTION III: HEDGING AND THE BASIS The Basis 8 Short Hedge Example 9 Long Hedge Example 9 TEST Section III 10 Answers Section III 12 SECTION IV: THE POWER OF OPTIONS Definitions 13 Options and Futures Comparison Diagram 14 Option Prices 15 Intrinsic Value 15 Time Value 15 Time Value Cap Diagram 15 Options Classifications 16 Options Exercise 16 F CONTENTS Deltas 16 Examples 16 TEST Section IV 18 Answers Section IV 20 SECTION V: OPTIONS STRATEGIES Option Use and Price 21 Hedging with Options 22 TEST Section V 23 Answers Section V 24 CONCLUSION 25 GLOSSARY 26 THE POWER OF CHOICE How do commercial buyers and sellers of volatile commodities protect themselves from the ever-changing and unpredictable nature of today’s business climate? They use a practice called hedging. This time-tested practice has become a stan- dard in many industries. Hedging can be defined as taking offsetting positions in related markets. -
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]. -
Evaluating the Performance of the WTI
An Evaluation of the Performance of Oil Price Benchmarks During the Financial Crisis Craig Pirrong Professor of Finance Energy Markets Director, Global Energy Management Institute Bauer College of Business University of Houston I. IntroDuction The events of late‐summer, 2008 through the spring of 2009 have attracted considerable attention to the performance of oil price benchmarks, most notably the Chicago Mercantile Exchange’s West Texas Intermediate crude oil contract (“WTI” or “CL” hereafter) and ICE Futures’ Brent crude oil contract (“Brent futures” or “CB” hereafter). In particular, the behavior of spreads between the prices of futures contracts of different maturities, and between futures prices and cash prices during the period following the Lehman Brothers collapse has sparked allegations that futures prices have become disconnected from the underlying cash market fundamentals. The WTI contract has been the subject of particular criticisms alleging the unrepresentativeness of the Midcontinent market as a global price benchmark. I have analyzed extensive data from the crude oil cash and futures markets to evaluate the performance of the WTI and Brent futures contracts during the LH2008‐FH2009 period. Although the analysis focuses on this period, it relies on data extending back to 1990 in order to put the performance in historical context. I have also incorporated some data on physical crude market fundamentals, most 1 notably stocks, as these are essential in providing an evaluation of contract performance and the relation between pricing and fundamentals. My conclusions are as follows: 1. The October, 2008‐March 2009 period was one of historically unprecedented volatility. By a variety of measures, volatility in this period was extremely high, even compared to the months surrounding the First Gulf War, previously the highest volatility period in the modern oil trading era. -
An Efficient Lattice Algorithm for the LIBOR Market Model
A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Xiao, Tim Article — Accepted Manuscript (Postprint) An Efficient Lattice Algorithm for the LIBOR Market Model Journal of Derivatives Suggested Citation: Xiao, Tim (2011) : An Efficient Lattice Algorithm for the LIBOR Market Model, Journal of Derivatives, ISSN 2168-8524, Pageant Media, London, Vol. 19, Iss. 1, pp. 25-40, http://dx.doi.org/10.3905/jod.2011.19.1.025 , https://jod.iijournals.com/content/19/1/25 This Version is available at: http://hdl.handle.net/10419/200091 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made available under an Open gelten abweichend von diesen Nutzungsbedingungen die in der dort Content Licence (especially Creative Commons Licences), you genannten Lizenz gewährten Nutzungsrechte. may exercise further usage rights as specified in the indicated licence. www.econstor.eu AN EFFICIENT LATTICE ALGORITHM FOR THE LIBOR MARKET MODEL 1 Tim Xiao Journal of Derivatives, 19 (1) 25-40, Fall 2011 ABSTRACT The LIBOR Market Model has become one of the most popular models for pricing interest rate products. -
Encana Distinguished Lecture Series
Oil Futures Prices and OPEC Spare Capacity J.P. Morgan Center for Commodities Encana Distinguished Lecture Series September 18, 2014 Ms. Hilary Till, Principal, Premia Risk Consultancy, Inc., http://www.premiarisk.com; Research Associate, EDHEC-Risk Institute, http://www.edhec-risk.com; and Co-Editor, Intelligent Commodity Investing, http://www.riskbooks.com/intelligent-commodity-investing Disclaimers • This presentation is provided for educational purposes only and should not be construed as investment advice or an offer or solicitation to buy or sell securities or other financial instruments. • The opinions expressed during this presentation are the personal opinions of Hilary Till and do not necessarily reflect those of other organizations with which Ms. Till is affiliated. • The information contained in this presentation has been assembled from sources believed to be reliable, but is not guaranteed by the presenter. • Any (inadvertent) errors and omissions are the responsibility of Ms. Till alone. 2 Oil Futures Prices and OPEC Spare Capacity I. Crude Oil’s Importance for Commodity Indexes II. Structural Curve Shape of Individual Futures Contracts III. Structural Curve Shape and the Implications for Crude Oil Futures Contracts IV. Commodity Futures Curve Shape and Inventories Icon above is based on the statue in the Chicago Board of Trade plaza. 3 Oil Futures Prices and OPEC Spare Capacity V. Special Features of Crude Oil Markets VI. What Happens When OPEC Spare Capacity Becomes Quite Low? VII. The Plausible Link Between OPEC Spare Capacity and a Crude Oil Futures Curve Shape VIII. Current (Official) Expectations on OPEC Spare Capacity IX. Trading and Investment Conclusion 4 Oil Futures Prices and OPEC Spare Capacity Appendix A: Portfolio-Level Returns from Rebalancing Appendix B: Month-to-Month Factors Influencing a Crude Oil Futures Curve Appendix C: Consideration of the “1986 Oil Tactic” 5 I. -
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. -
Option Prices and Implied Volatility Dynamics Under Ayesian Learning
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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. -
What's Price Got to Do with Term Structure?
What’s Price Got To Do With Term Structure? An Introduction to the Change in Realized Roll Yields: Redefining How Forward Curves Are Measured Contributors: Historically, investors have been drawn to the systematic return opportunities, or beta, of commodities due to their potentially inflation-hedging and Jodie Gunzberg, CFA diversifying properties. However, because contango was a persistent market Vice President, Commodities condition from 2005 to 2011, occurring in 93% of the months during that time, [email protected] roll yield had a negative impact on returns. As a result, it may have seemed to some that the liquidity risk premium had disappeared. Marya Alsati-Morad Associate Director, Commodities However, as discussed in our paper published in September 2013, entitled [email protected] “Identifying Return Opportunities in A Demand-Driven World Economy,” the environment may be changing. Specifically, the world economy may be Peter Tsui shifting from one driven by expansion of supply to one driven by expansion of Director, Index Research & Design demand, which could have a significant impact on commodity performance. [email protected] This impact would be directly related to two hallmarks of a world economy driven by expansion of demand: the increasing persistence of backwardation and the more frequent flipping of term structures. In order to benefit in this changing economic environment, the key is to implement flexibility to keep pace with the quickly changing term structures. To achieve flexibility, there are two primary ways to modify the first-generation ® flagship index, the S&P GSCI . The first method allows an index to select contracts with expirations that are either near- or longer-dated based on the commodity futures’ term structure. -
Term Structure Lattice Models
Term Structure Models: IEOR E4710 Spring 2005 °c 2005 by Martin Haugh Term Structure Lattice Models 1 The Term-Structure of Interest Rates If a bank lends you money for one year and lends money to someone else for ten years, it is very likely that the rate of interest charged for the one-year loan will di®er from that charged for the ten-year loan. Term-structure theory has as its basis the idea that loans of di®erent maturities should incur di®erent rates of interest. This basis is grounded in reality and allows for a much richer and more realistic theory than that provided by the yield-to-maturity (YTM) framework1. We ¯rst describe some of the basic concepts and notation that we need for studying term-structure models. In these notes we will often assume that there are m compounding periods per year, but it should be clear what changes need to be made for continuous-time models and di®erent compounding conventions. Time can be measured in periods or years, but it should be clear from the context what convention we are using. Spot Rates: Spot rates are the basic interest rates that de¯ne the term structure. De¯ned on an annual basis, the spot rate, st, is the rate of interest charged for lending money from today (t = 0) until time t. In particular, 2 mt this implies that if you lend A dollars for t years today, you will receive A(1 + st=m) dollars when the t years have elapsed. -
Decision Analysis and Real Options: a Discrete Time Approach to Real Option Valuation
Annals of Operations Research 135, 21–39, 2005 c 2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands. Decision Analysis and Real Options: A Discrete Time Approach to Real Option Valuation LUIZ E. BRANDAO˜ [email protected] JAMES S. DYER [email protected] The University of Texas at Austin, McCombs School of Business, Department of Management Science and Information Systems, 1 University Station B6500, CBA 5.202, Austin, TX 78712-1175 Abstract. In this paper we seek to enhance the real options methodology developed by Copeland and Antikarov (2001) with traditional decision analysis tools to propose a discrete time method that allows the problem to be specified and solved with off the shelf decision analysis software. This method uses dynamic programming with an innovative algorithm to model the project’s stochastic process and real options with decision trees. The method is computationally intense, but simpler and more intuitive than traditional methods, thus allowing for greater flexibility in the modeling of the problem. Keywords: real options, lattice methods, decision trees Introduction Due to its importance for the creation of value for the shareholder, the investment decision in the firm has always been a focus of academic and managerial interest. The use of the discounted cash flow method (DCF), introduced in firms in the 1950’s, was initially considered a sophisticated approach for the valuation of projects due to the need to use present value tables. In spite of its obvious advantages over the obsolete payback method, its widespread use occurred only after the development of portable calculators and computers that automated the necessary financial calculations, and most practitioners currently consider it the model of choice. -
Structural Position-Taking in Crude Oil Futures Contracts
Structural Position-Taking in Crude Oil Futures Contracts November 2015 Hilary Till Research Associate, EDHEC-Risk Institute Principal, Premia Research LLC Abstract Should an investor enter into long-term positions in oil futures contracts? In answering this question, this paper will cover the following three considerations: (1) the case for structural positions in crude oil futures contracts; (2) useful indicators for avoiding crash risk; and (3) financial asset diversification for downside hedging of oil price risk. This paper will conclude by noting the conditions under which one might consider including oil futures contracts in an investment portfolio. This paper is provided for educational purposes only and should not be construed as investment advice or an offer or solicitation to buy or sell securities or other financial instruments. The views expressed in this article are the personal opinions of Hilary Till and do not necessarily reflect the views of institutions with which Ms. Till is affiliated. Research assistance from Katherine Farren, CAIA, of Premia Risk Consultancy, Inc. is gratefully acknowledged. EDHEC is one of the top five business schools in France. Its reputation is built on the high quality of its faculty and the privileged relationship with professionals that the school has cultivated since its establishment in 1906. EDHEC Business School has decided to draw on its extensive knowledge of the professional environment and has therefore focused its research on themes that satisfy the needs of professionals. EDHEC pursues an active research policy in the field of finance. EDHEC-Risk Institute carries out numerous research programmes in the areas of asset allocation and risk management in both the 2 traditional and alternative investment universes.