Spot and Forward Volatility in Foreign Exchange!
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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. -
Pricing Financial Derivatives Under the Regime-Switching Models Mengzhe Zhang
Pricing Financial Derivatives Under the Regime-Switching Models Mengzhe Zhang A thesis in fulfilment of the requirements the degree of Doctor of Philosophy School of Mathematics and Statistics Faculty of Science, University of New South of Wales, Australia 19th April 2017 Acknowledgement First of all, I would like to thank my supervisor Dr Leung Lung Chan for his valuable guidance, helpful comments and enlightening advice throughout the preparation of this thesis. I also want to thank my parents for their love, support and encouragement throughout my graduate study time at the University of NSW. Last but not the least, I would like to thank my friends- Dr Xin Gao, Dr Xin Zhang, Dr Wanchuang Zhu, Dr Philip Chen and Dr Jinghao Huang from the Math school of UNSW and Dr Zhuo Chen, Dr Xueting Zhang, Dr Tyler Kwong, Dr Henry Rui, Mr Tianyu Cai, Mr Zhongyuan Liu, Mr Yue Peng and Mr Huaizhou Li from the Business school of UNSW. i Contents 1 Introduction1 1.1 BS-Type Model with Regime-Switching.................5 1.2 Heston's Model with Regime-Switching.................6 2 Asymptotics for Option Prices with Regime-Switching Model: Short- Time and Large-Time9 2.1 Introduction................................9 2.2 BS Model with Regime-Switching.................... 13 2.3 Small Time Asymptotics for Option Prices............... 13 2.3.1 Out-of-the-Money and In-the-Money.............. 13 2.3.2 At-the-Money........................... 25 2.3.3 Numerical Results........................ 35 2.4 Large-Maturity Asymptotics for Option Prices............. 39 ii 2.5 Calibration................................ 53 2.6 Conclusion................................. 57 3 Saddlepoint Approximations to Option Price in A Regime-Switching Model 59 3.1 Introduction............................... -
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 ................................................................................................ -
Nonparametric Pricing and Hedging of Volatility Swaps in Stochastic
Nonparametric Pricing and Hedging of Volatility Swaps in Stochastic Volatility Models Frido Rolloos∗ March 28, 2020 Abstract In this paper the zero vanna implied volatility approximation for the price of freshly minted volatility swaps is generalised to seasoned volatility swaps. We also derive how volatility swaps can be hedged using a strip of vanilla options with weights that are directly related to trading intuition. Additionally, we derive first and second order hedges for volatil- ity swaps using only variance swaps. As dynamically trading variance swaps is in general cheaper and operationally less cumbersome compared to dynamically rebalancing a continu- ous strip of options, our result makes the hedging of volatility swaps both practically feasible and robust. Within the class of stochastic volatility models our pricing and hedging results are model-independent and can be implemented at almost no computational cost. arXiv:2001.02404v4 [q-fin.PR] 3 Apr 2020 ∗[email protected] 1 1 Assumptions and notations We will work under the premise that the market implied volatility surface is generated by the following general stochastic volatility (SV) model dS = σS ρ dW + ρdZ¯ (1.1) [ ] dσ = a σ, t dt + b σ, t dW (1.2) ( ) ( ) where ρ¯ = 1 ρ2, dW and dZ are independent standard Brownian motions, and the func- tions a and b −are deterministic functions of time and volatility. The results derived in this p paper are valid for any SV model satisfying (1.1) and (1.2), which includes among others the Heston model, the lognormal SABR model, and the 3 2 model. / The SV process is assumed to be well-behaved in the sense that vanilla options prices are risk-neutral expectations of the payoff function: C S, K = Et S T K + (1.3) ( ) [( ( )− ) ] The option price C can always be expressed in terms of the Black-Merton-Scholes (BS) price CBS with an implied volatility parameter I: BS C S, K = C S, K, I (1.4) ( ) ( ) It is assumed that the implied volatility parameter I = I S, t, K,T, σ, ρ . -
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. -
Volatility Swaps Valuation Under Stochastic Volatility with Jumps and Stochastic Intensity
Volatility swaps valuation under stochastic volatility with jumps and stochastic intensity∗ Ben-zhang Yanga, Jia Yueb, Ming-hui Wanga and Nan-jing Huanga† a. Department of Mathematics, Sichuan University, Chengdu, Sichuan 610064, P.R. China b. Department of Economic Mathematics, South Western University of Finance and Economics, Chengdu, Sichuan 610074, P.R. China Abstract. In this paper, a pricing formula for volatility swaps is delivered when the underlying asset follows the stochastic volatility model with jumps and stochastic intensity. By using Feynman-Kac theorem, a partial integral differential equation is obtained to derive the joint moment generating function of the previous model. Moreover, discrete and continuous sampled volatility swap pricing formulas are given by employing transform techniques and the relationship between two pricing formulas is discussed. Finally, some numerical simulations are reported to support the results presented in this paper. Key Words and Phrases: Stochastic volatility model with jumps; Stochastic intensity; Volatility derivatives; Pricing. 2010 AMS Subject Classification: 91G20, 91G80, 60H10. 1 Introduction Since the finance volatility has always been considered as a key measure, the development and growth of the financial market have changed the role of the volatility over last century. Volatility derivatives in general are important tools to display the market fluctuation and manage volatility risk for investors. arXiv:1805.06226v2 [q-fin.PR] 18 May 2018 More precisely, volatility derivatives are traded for decision-making between long or short positions, trading spreads between realized and implied volatility, and hedging against volatility risks. The utmost advantage of volatility derivatives is their capability in providing direct exposure towards the assets volatility without being burdened with the hassles of continuous delta-hedging. -
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. -
The Hedging Process
2016 Hedging What is hedging? Why would a business need it? How would it help mitigate risks? How would one be able to get started with it? How can MFX help? Everything it entails can be summarized in a concise, yet detailed step-by-step process. There are several key steps a company must follow in order to go through with this process and benefit. Let the simple flow chart below direct you as you read through this guide. Key Steps to the Hedging Process Why would you need hedging? Hedging using derivatives is a common and popular method for managing different kinds of risk for all different kinds of business. However, before going down the path of contracting a hedge, it is important to understand exactly what risk your institution faces. Without fully understanding how your financial position will react to changes in the risk factors you are trying to hedge, there is a risk that entering into a derivative contract could add additional risk of different kinds of risk than you currently face. It is important to think about other risk factors other than the one you are hedging that could affect the hedge. For example, how certain are you of the cash flows you are trying to hedge? Are you hedging balance sheet or cash risk? Are there natural hedges in other parts of your business that offset the risk you are trying to hedge? MFX’s team can help you consider the decision to hedge, but the client is responsible for assessing its risk and the appropriateness of hedging it. -
Forward and Futures Contracts
FIN-40008 FINANCIAL INSTRUMENTS SPRING 2008 Forward and Futures Contracts These notes explore forward and futures contracts, what they are and how they are used. We will learn how to price forward contracts by using arbitrage and replication arguments that are fundamental to derivative pricing. We shall also learn about the similarities and differences between forward and futures markets and the differences between forward and futures markets and prices. We shall also consider how forward and future prices are related to spot market prices. Keywords: Arbitrage, Replication, Hedging, Synthetic, Speculator, Forward Value, Maintainable Margin, Limit Order, Market Order, Stop Order, Back- wardation, Contango, Underlying, Derivative. Reading: You should read Hull chapters 1 (which covers option payoffs as well) and chapters 2 and 5. 1 Background From the 1970s financial markets became riskier with larger swings in interest rates and equity and commodity prices. In response to this increase in risk, financial institutions looked for new ways to reduce the risks they faced. The way found was the development of exchange traded derivative securities. Derivative securities are assets linked to the payments on some underlying security or index of securities. Many derivative securities had been traded over the counter for a long time but it was from this time that volume of trading activity in derivatives grew most rapidly. The most important types of derivatives are futures, options and swaps. An option gives the holder the right to buy or sell the underlying asset at a specified date for a pre-specified price. A future gives the holder the 1 2 FIN-40008 FINANCIAL INSTRUMENTS obligation to buy or sell the underlying asset at a specified date for a pre- specified price. -
Financial Derivatives Classification of Derivatives
FINANCIAL DERIVATIVES A derivative is a financial instrument or contract that derives its value from an underlying asset. The buyer agrees to purchase the asset on a specific date at a specific price. Derivatives are often used for commodities, such as oil, gasoline, or gold. Another asset class is currencies, often the U.S. dollar. There are derivatives based on stocks or bonds. The most common underlying assets include stocks, bonds, commodities, currencies, interest rates and market indexes. The contract's seller doesn't have to own the underlying asset. He can fulfill the contract by giving the buyer enough money to buy the asset at the prevailing price. He can also give the buyer another derivative contract that offsets the value of the first. This makes derivatives much easier to trade than the asset itself. According to the Securities Contract Regulation Act, 1956 the term ‘Derivative’ includes: i. a security derived from a debt instrument, share, loan, whether secured or unsecured, risk instrument or contract for differences or any other form of security. ii. a contract which derives its value from the prices or index of prices, of underlying securities. CLASSIFICATION OF DERIVATIVES Derivatives can be classified into broad categories depending upon the type of underlying asset, the nature of derivative contract or the trading of derivative contract. 1. Commodity derivative and Financial derivative In commodity derivatives, the underlying asset is a commodity, such as cotton, gold, copper, wheat, or spices. Commodity derivatives were originally designed to protect farmers from the risk of under- or overproduction of crops. Commodity derivatives are investment tools that allow investors to profit from certain commodities without possessing them. -
Derivative Instruments and Hedging Activities
www.pwc.com 2015 Derivative instruments and hedging activities www.pwc.com Derivative instruments and hedging activities 2013 Second edition, July 2015 Copyright © 2013-2015 PricewaterhouseCoopers LLP, a Delaware limited liability partnership. All rights reserved. PwC refers to the United States member firm, and may sometimes refer to the PwC network. Each member firm is a separate legal entity. Please see www.pwc.com/structure for further details. This publication has been prepared for general information on matters of interest only, and does not constitute professional advice on facts and circumstances specific to any person or entity. You should not act upon the information contained in this publication without obtaining specific professional advice. No representation or warranty (express or implied) is given as to the accuracy or completeness of the information contained in this publication. The information contained in this material was not intended or written to be used, and cannot be used, for purposes of avoiding penalties or sanctions imposed by any government or other regulatory body. PricewaterhouseCoopers LLP, its members, employees and agents shall not be responsible for any loss sustained by any person or entity who relies on this publication. The content of this publication is based on information available as of March 31, 2013. Accordingly, certain aspects of this publication may be superseded as new guidance or interpretations emerge. Financial statement preparers and other users of this publication are therefore cautioned to stay abreast of and carefully evaluate subsequent authoritative and interpretative guidance that is issued. This publication has been updated to reflect new and updated authoritative and interpretative guidance since the 2012 edition. -
EQUITY DERIVATIVES Faqs
NATIONAL INSTITUTE OF SECURITIES MARKETS SCHOOL FOR SECURITIES EDUCATION EQUITY DERIVATIVES Frequently Asked Questions (FAQs) Authors: NISM PGDM 2019-21 Batch Students: Abhilash Rathod Akash Sherry Akhilesh Krishnan Devansh Sharma Jyotsna Gupta Malaya Mohapatra Prahlad Arora Rajesh Gouda Rujuta Tamhankar Shreya Iyer Shubham Gurtu Vansh Agarwal Faculty Guide: Ritesh Nandwani, Program Director, PGDM, NISM Table of Contents Sr. Question Topic Page No No. Numbers 1 Introduction to Derivatives 1-16 2 2 Understanding Futures & Forwards 17-42 9 3 Understanding Options 43-66 20 4 Option Properties 66-90 29 5 Options Pricing & Valuation 91-95 39 6 Derivatives Applications 96-125 44 7 Options Trading Strategies 126-271 53 8 Risks involved in Derivatives trading 272-282 86 Trading, Margin requirements & 9 283-329 90 Position Limits in India 10 Clearing & Settlement in India 330-345 105 Annexures : Key Statistics & Trends - 113 1 | P a g e I. INTRODUCTION TO DERIVATIVES 1. What are Derivatives? Ans. A Derivative is a financial instrument whose value is derived from the value of an underlying asset. The underlying asset can be equity shares or index, precious metals, commodities, currencies, interest rates etc. A derivative instrument does not have any independent value. Its value is always dependent on the underlying assets. Derivatives can be used either to minimize risk (hedging) or assume risk with the expectation of some positive pay-off or reward (speculation). 2. What are some common types of Derivatives? Ans. The following are some common types of derivatives: a) Forwards b) Futures c) Options d) Swaps 3. What is Forward? A forward is a contractual agreement between two parties to buy/sell an underlying asset at a future date for a particular price that is pre‐decided on the date of contract.