Pricing Forwards and Futures Objectives
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Section 1256 and Foreign Currency Derivatives
Section 1256 and Foreign Currency Derivatives Viva Hammer1 Mark-to-market taxation was considered “a fundamental departure from the concept of income realization in the U.S. tax law”2 when it was introduced in 1981. Congress was only game to propose the concept because of rampant “straddle” shelters that were undermining the U.S. tax system and commodities derivatives markets. Early in tax history, the Supreme Court articulated the realization principle as a Constitutional limitation on Congress’ taxing power. But in 1981, lawmakers makers felt confident imposing mark-to-market on exchange traded futures contracts because of the exchanges’ system of variation margin. However, when in 1982 non-exchange foreign currency traders asked to come within the ambit of mark-to-market taxation, Congress acceded to their demands even though this market had no equivalent to variation margin. This opportunistic rather than policy-driven history has spawned a great debate amongst tax practitioners as to the scope of the mark-to-market rule governing foreign currency contracts. Several recent cases have added fuel to the debate. The Straddle Shelters of the 1970s Straddle shelters were developed to exploit several structural flaws in the U.S. tax system: (1) the vast gulf between ordinary income tax rate (maximum 70%) and long term capital gain rate (28%), (2) the arbitrary distinction between capital gain and ordinary income, making it relatively easy to convert one to the other, and (3) the non- economic tax treatment of derivative contracts. Straddle shelters were so pervasive that in 1978 it was estimated that more than 75% of the open interest in silver futures were entered into to accommodate tax straddles and demand for U.S. -
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. -
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. -
Forward Contracts and Futures a Forward Is an Agreement Between Two Parties to Buy Or Sell an Asset at a Pre-Determined Future Time for a Certain Price
Forward contracts and futures A forward is an agreement between two parties to buy or sell an asset at a pre-determined future time for a certain price. Goal To hedge against the price fluctuation of commodity. • Intension of purchase decided earlier, actual transaction done later. • The forward contract needs to specify the delivery price, amount, quality, delivery date, means of delivery, etc. Potential default of either party: writer or holder. Terminal payoff from forward contract payoff payoff K − ST ST − K K ST ST K long position short position K = delivery price, ST = asset price at maturity Zero-sum game between the writer (short position) and owner (long position). Since it costs nothing to enter into a forward contract, the terminal payoff is the investor’s total gain or loss from the contract. Forward price for a forward contract is defined as the delivery price which make the value of the contract at initiation be zero. Question Does it relate to the expected value of the commodity on the delivery date? Forward price = spot price + cost of fund + storage cost cost of carry Example • Spot price of one ton of wood is $10,000 • 6-month interest income from $10,000 is $400 • storage cost of one ton of wood is $300 6-month forward price of one ton of wood = $10,000 + 400 + $300 = $10,700. Explanation Suppose the forward price deviates too much from $10,700, the construction firm would prefer to buy the wood now and store that for 6 months (though the cost of storage may be higher). -
Pricing of Index Options Using Black's Model
Global Journal of Management and Business Research Volume 12 Issue 3 Version 1.0 March 2012 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 2249-4588 & Print ISSN: 0975-5853 Pricing of Index Options Using Black’s Model By Dr. S. K. Mitra Institute of Management Technology Nagpur Abstract - Stock index futures sometimes suffer from ‘a negative cost-of-carry’ bias, as future prices of stock index frequently trade less than their theoretical value that include carrying costs. Since commencement of Nifty future trading in India, Nifty future always traded below the theoretical prices. This distortion of future prices also spills over to option pricing and increase difference between actual price of Nifty options and the prices calculated using the famous Black-Scholes formula. Fisher Black tried to address the negative cost of carry effect by using forward prices in the option pricing model instead of spot prices. Black’s model is found useful for valuing options on physical commodities where discounted value of future price was found to be a better substitute of spot prices as an input to value options. In this study the theoretical prices of Nifty options using both Black Formula and Black-Scholes Formula were compared with actual prices in the market. It was observed that for valuing Nifty Options, Black Formula had given better result compared to Black-Scholes. Keywords : Options Pricing, Cost of carry, Black-Scholes model, Black’s model. GJMBR - B Classification : FOR Code:150507, 150504, JEL Code: G12 , G13, M31 PricingofIndexOptionsUsingBlacksModel Strictly as per the compliance and regulations of: © 2012. -
Updated Trading Participants' Trading Manual
Annexure 3 TRADING MANUAL BURSA MALAYSIA DERIVATIVES BHD TRADING MANUAL (Version 4.10) This manual is the intellectual property of BURSA MALAYSIA. No part of the manual is to be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system, without permission in writing from Head of BMD Exchange Operations. TRADING MANUAL Version History Version Date Author Comments V 1.0 9 Aug 2010 BMDB Initial Version V 1.1 27 Aug 2010 BMDB Updated # 6.6.3 Review of Trades – Price Adjustments and Cancellations V1.2 6 Sep 2010 BMDB Inserted 15. Operator ID (“Tag 50 ID”) Required for All BMD orders traded on CME Globex V1.3 9 Sep 2010 BMDB Update 1. Introduction – 1.6 TPs’ compliance in relation to access, connectivity, specification or use of CME Globex V1.4 13 Sep 2010 BMDB Updated 13. Messaging And Market Performance Protection Policy V1.5 9 Nov 2011 BMDB Inserted 16 Negotiated Large Trade V1.6 18 Nov 2011 BMDB Amended section 16 for typo errors, consistency and clarity. V1.7 24 Nov 2011 BMDB Amended section 16 - Extended NLT cut-off time for FKLI, FKB3 and FMG5 to 4.00pm and for FCPO to 5.00pm. -Amended the NLT Facility Trade Registration form. V1.8 10 Feb 2012 BMDB Updated section 11 EFP to EFRP V1.9 23 Mar 2012 BMDB Amended sections 11 and 16 (forms and processes) V2.0 5 Apr 2012 BMDB Renamed to “Trading Manual” V2.1 14 May 2012 BMDB Updated Section 6 for OKLI and to align with CME practice V2.2 29 May 2012 BMDB i) Updated for OCPO ii) Change of terminology to be consistent with CME iii) Updated Sections 7.7 & 14.1 for consistency with Rules iv) Updated Sections 12.3 & 12.4 for accuracy v) Updated Section 3.1 on options naming convention V2.3 18 Feb 2013 BMDB Updated Section 16 NLT V2.4 3 Apr 2013 BMDB Updated Section 9 Circuit Breaker on timing V2.5 2 Jul 2013 BMDB Updated FGLD. -
Term Structure Models of Commodity Prices
TERM STRUCTURE MODELS OF COMMODITY PRICES Cahier de recherche du Cereg n°2003–9 Delphine LAUTIER ABSTRACT. This review article describes the main contributions in the literature on term structure models of commodity prices. A first section is devoted to the theoretical analysis of the term structure. It confines itself primarily to the traditional theories of commodity prices and to their explanation of the relationship between spot and futures prices. The normal backwardation and storage theories are however a bit limited when the whole term structure is taken into account. As a result, there is a need for an extension of the analysis for long-term horizon, which constitutes the second point of the section. Finally, a dynamic analysis of the term structure is presented. Section two is centred on term structure models of commodity prices. The presentation shows that these models differ on the nature and the number of factors used to describe uncertainty. Four different factors are generally used: the spot price, the convenience yield, the interest rate, and the long-term price. Section three reviews the main empirical results obtained with term structure models. First of all, simulations highlight the influence of the assumptions concerning the stochastic process retained for the state variables and the number of state variables. Then, the method usually employed for the estimation of the parameters is explained. Lastly, the models’ performances, namely their ability to reproduce the term structure of commodity prices, are presented. Section four exposes the two main applications of term structure models: hedging and valuation. Section five resumes the broad trends in the literature on commodity pricing during the 1990s and early 2000s, and proposes futures directions for research. -
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. -
Value at Risk for Linear and Non-Linear Derivatives
Value at Risk for Linear and Non-Linear Derivatives by Clemens U. Frei (Under the direction of John T. Scruggs) Abstract This paper examines the question whether standard parametric Value at Risk approaches, such as the delta-normal and the delta-gamma approach and their assumptions are appropriate for derivative positions such as forward and option contracts. The delta-normal and delta-gamma approaches are both methods based on a first-order or on a second-order Taylor series expansion. We will see that the delta-normal method is reliable for linear derivatives although it can lead to signif- icant approximation errors in the case of non-linearity. The delta-gamma method provides a better approximation because this method includes second order-effects. The main problem with delta-gamma methods is the estimation of the quantile of the profit and loss distribution. Empiric results by other authors suggest the use of a Cornish-Fisher expansion. The delta-gamma method when using a Cornish-Fisher expansion provides an approximation which is close to the results calculated by Monte Carlo Simulation methods but is computationally more efficient. Index words: Value at Risk, Variance-Covariance approach, Delta-Normal approach, Delta-Gamma approach, Market Risk, Derivatives, Taylor series expansion. Value at Risk for Linear and Non-Linear Derivatives by Clemens U. Frei Vordiplom, University of Bielefeld, Germany, 2000 A Thesis Submitted to the Graduate Faculty of The University of Georgia in Partial Fulfillment of the Requirements for the Degree Master of Arts Athens, Georgia 2003 °c 2003 Clemens U. Frei All Rights Reserved Value at Risk for Linear and Non-Linear Derivatives by Clemens U. -
Monte Carlo Strategies in Option Pricing for Sabr Model
MONTE CARLO STRATEGIES IN OPTION PRICING FOR SABR MODEL Leicheng Yin A dissertation submitted to the faculty of the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Statistics and Operations Research. Chapel Hill 2015 Approved by: Chuanshu Ji Vidyadhar Kulkarni Nilay Argon Kai Zhang Serhan Ziya c 2015 Leicheng Yin ALL RIGHTS RESERVED ii ABSTRACT LEICHENG YIN: MONTE CARLO STRATEGIES IN OPTION PRICING FOR SABR MODEL (Under the direction of Chuanshu Ji) Option pricing problems have always been a hot topic in mathematical finance. The SABR model is a stochastic volatility model, which attempts to capture the volatility smile in derivatives markets. To price options under SABR model, there are analytical and probability approaches. The probability approach i.e. the Monte Carlo method suffers from computation inefficiency due to high dimensional state spaces. In this work, we adopt the probability approach for pricing options under the SABR model. The novelty of our contribution lies in reducing the dimensionality of Monte Carlo simulation from the high dimensional state space (time series of the underlying asset) to the 2-D or 3-D random vectors (certain summary statistics of the volatility path). iii To Mom and Dad iv ACKNOWLEDGEMENTS First, I would like to thank my advisor, Professor Chuanshu Ji, who gave me great instruction and advice on my research. As my mentor and friend, Chuanshu also offered me generous help to my career and provided me with great advice about life. Studying from and working with him was a precious experience to me. -
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. -
Enns for Corporate and Sovereign CDS and FX Swaps
ENNs for Corporate and Sovereign CDS and FX Swaps by Lee Baker, Richard Haynes, Madison Lau, John Roberts, Rajiv Sharma, and Bruce Tuckman1 February, 2019 I. Introduction The sizes of swap markets, and the sizes of market participant footprints in swap markets, are most often measured in terms of notional amount. It is widely recognized, however, that notional amount is a poor metric of both size and footprint. First, when calculating notional amounts, the long and short positions between two counterparties are added together, even though longs and shorts essentially offset each other. Second, notional calculations add together positions with very different amounts of risk, like a relatively low-risk 3-month interest rate swap (IRS) and a relatively high-risk 30- year IRS. The use of notional amount to measure size distorts understanding of swap markets. A particularly powerful example arose around Lehman Brothers’ bankruptcy in September, 2008. At that time, there were $400 billion notional of outstanding credit default swaps (CDS) on Lehman, and Lehman’s debt was trading at 8.6 cents on the dollar. Many were frightened by the prospect that sellers of protection would soon have to pay buyers of protection a total of $400 billion x (1 – 8.6%), or about $365 billion. As it turned out, however, a large amount of protection sold had been offset by protection bought: in the end, protection sellers paid protection buyers between $6 and $8 billion.2 In January, 2018, the Office of the Chief Economist at the Commodity Futures Trading Commission (CFTC) introduced ENNs (Entity-Netted Notionals) as a metric of size in IRS markets.3 To compute IRS ENNs, all notional amounts are expressed in terms of the risk of a 5-year IRS, and long and short positions are netted when they are between the same pair of legal counterparties and denominated in the same currency.