White Paper Volatility: Instruments and Strategies
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
University of Illinois at Urbana-Champaign Department of Mathematics
University of Illinois at Urbana-Champaign Department of Mathematics ASRM 410 Investments and Financial Markets Fall 2018 Chapter 5 Option Greeks and Risk Management by Alfred Chong Learning Objectives: 5.1 Option Greeks: Black-Scholes valuation formula at time t, observed values, model inputs, option characteristics, option Greeks, partial derivatives, sensitivity, change infinitesimally, Delta, Gamma, theta, Vega, rho, Psi, option Greek of portfolio, absolute change, option elasticity, percentage change, option elasticity of portfolio. 5.2 Applications of Option Greeks: risk management via hedging, immunization, adverse market changes, delta-neutrality, delta-hedging, local, re-balance, from- time-to-time, holding profit, delta-hedging, first order, prudent, delta-gamma- hedging, gamma-neutrality, option value approximation, observed values change inevitably, Taylor series expansion, second order approximation, delta-gamma- theta approximation, delta approximation, delta-gamma approximation. Further Exercises: SOA Advanced Derivatives Samples Question 9. 1 5.1 Option Greeks 5.1.1 Consider a continuous-time setting t ≥ 0. Assume that the Black-Scholes framework holds as in 4.2.2{4.2.4. 5.1.2 The time-0 Black-Scholes European call and put option valuation formula in 4.2.9 and 4.2.10 can be generalized to those at time t: −δ(T −t) −r(T −t) C (t; S; δ; r; σ; K; T ) = Se N (d1) − Ke N (d2) P = Ft;T N (d1) − PVt;T (K) N (d2); −r(T −t) −δ(T −t) P (t; S; δ; r; σ; K; T ) = Ke N (−d2) − Se N (−d1) P = PVt;T (K) N (−d2) − Ft;T N (−d1) ; where d1 and d2 are given by S 1 2 ln K + r − δ + 2 σ (T − t) d1 = p ; σ T − t S 1 2 p ln K + r − δ − 2 σ (T − t) d2 = p = d1 − σ T − t: σ T − t 5.1.3 The option value V is a function of observed values, t and S, model inputs, δ, r, and σ, as well as option characteristics, K and T . -
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]. -
Ice Crude Oil
ICE CRUDE OIL Intercontinental Exchange® (ICE®) became a center for global petroleum risk management and trading with its acquisition of the International Petroleum Exchange® (IPE®) in June 2001, which is today known as ICE Futures Europe®. IPE was established in 1980 in response to the immense volatility that resulted from the oil price shocks of the 1970s. As IPE’s short-term physical markets evolved and the need to hedge emerged, the exchange offered its first contract, Gas Oil futures. In June 1988, the exchange successfully launched the Brent Crude futures contract. Today, ICE’s FSA-regulated energy futures exchange conducts nearly half the world’s trade in crude oil futures. Along with the benchmark Brent crude oil, West Texas Intermediate (WTI) crude oil and gasoil futures contracts, ICE Futures Europe also offers a full range of futures and options contracts on emissions, U.K. natural gas, U.K power and coal. THE BRENT CRUDE MARKET Brent has served as a leading global benchmark for Atlantic Oseberg-Ekofisk family of North Sea crude oils, each of which Basin crude oils in general, and low-sulfur (“sweet”) crude has a separate delivery point. Many of the crude oils traded oils in particular, since the commercialization of the U.K. and as a basis to Brent actually are traded as a basis to Dated Norwegian sectors of the North Sea in the 1970s. These crude Brent, a cargo loading within the next 10-21 days (23 days on oils include most grades produced from Nigeria and Angola, a Friday). In a circular turn, the active cash swap market for as well as U.S. -
Yuichi Katsura
P&TcrvvCJ Efficient ValtmVmm-and Hedging of Structured Credit Products Yuichi Katsura A thesis submitted for the degree of Doctor of Philosophy of the University of London May 2005 Centre for Quantitative Finance Imperial College London nit~ýl. ABSTRACT Structured credit derivatives such as synthetic collateralized debt obligations (CDO) have been the principal growth engine in the fixed income market over the last few years. Val- uation and risk management of structured credit products by meads of Monte Carlo sim- ulations tend to suffer from numerical instability and heavy computational time. After a brief description of single name credit derivatives that underlie structured credit deriva- tives, the factor model is introduced as the portfolio credit derivatives pricing tool. This thesis then describes the rapid pricing and hedging of portfolio credit derivatives using the analytical approximation model and semi-analytical models under several specifications of factor models. When a full correlation structure is assumed or the pricing of exotic structure credit products with non-linear pay-offs is involved, the semi-analytical tractability is lost and we have to resort to the time-consuming Monte Carlo method. As a Monte Carlo variance reduction technique we extend the importance sampling method introduced to credit risk modelling by Joshi [2004] to the general n-factor problem. The large homogeneouspool model is also applied to the method of control variates as a new variance reduction tech- niques for pricing structured credit products. The combination of both methods is examined and it turns out to be the optimal choice. We also show how sensitivities of a CDO tranche can be computed efficiently by semi-analytical and Monte Carlo framework. -
RT Options Scanner Guide
RT Options Scanner Introduction Our RT Options Scanner allows you to search in real-time through all publicly traded US equities and indexes options (more than 170,000 options contracts total) for trading opportunities involving strategies like: - Naked or Covered Write Protective Purchase, etc. - Any complex multi-leg option strategy – to find the “missing leg” - Calendar Spreads Search is performed against our Options Database that is updated in real-time and driven by HyperFeed’s Ticker-Plant and our Implied Volatility Calculation Engine. We offer both 20-minute delayed and real-time modes. Unlike other search engines using real-time quotes data only our system takes advantage of individual contracts Implied Volatilities calculated in real-time using our high-performance Volatility Engine. The Scanner is universal, that is, it is not confined to any special type of option strategy. Rather, it allows determining the option you need in the following terms: – Cost – Moneyness – Expiry – Liquidity – Risk The Scanner offers Basic and Advanced Search Interfaces. Basic interface can be used to find options contracts satisfying, for example, such requirements: “I need expensive Calls for Covered Call Write Strategy. They should expire soon and be slightly out of the money. Do not care much as far as liquidity, but do not like to bear high risk.” This can be done in five seconds - set the search filters to: Cost -> Dear Moneyness -> OTM Expiry -> Short term Liquidity -> Any Risk -> Moderate and press the “Search” button. It is that easy. In fact, if your request is exactly as above, you can just press the “Search” button - these values are the default filtering criteria. -
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............................... -
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. -
Option Greeks Demystified
Webinar Presentation Option Greeks Demystified Presented by Trading Strategy Desk 1 Fidelity Brokerage Services, Member NYSE, SIPC, 900 Salem Street, Smithfield, RI 02917. © 2016 FMR LLC. All rights reserved. 764207.1.0 Disclosures Options trading entails significant risk and is not appropriate for all investors. Certain complex options strategies carry additional risk. Before trading options, please read Characteristics and Risks of Standardized Options, and call 800-544- 5115 to be approved for options trading. Supporting documentation for any claims, if applicable, will be furnished upon request. Examples in this presentation do not include transaction costs (commissions, margin interest, fees) or tax implications, but they should be considered prior to entering into any transactions. The information in this presentation, including examples using actual securities and price data, is strictly for illustrative and educational purposes only and is not to be construed as an endorsement, recommendation. Active Trader Pro PlatformsSM is available to customers trading 36 times or more in a rolling 12-month period; customers who trade 120 times or more have access to Recognia anticipated events and Elliott Wave analysis. Greeks are mathematical calculations used to determine the effect of various factors on options. Goal of this webinar Demystify what Options Greeks are and explain how they are used in plain English. What we will cover: What the Greeks are What the Greeks tell us How the Greeks can help with planning option trades How the Greeks can help with managing option trades The Greeks What the Greeks are: • Delta • Gamma • Vega • Theta • Rho But what do they mean? Greeks What do they tell me? In simplest terms, Greeks give traders a theoretical way to judge their exposure to various options pricing inputs. -
Straddles and Strangles to Help Manage Stock Events
Webinar Presentation Using Straddles and Strangles to Help Manage Stock Events Presented by Trading Strategy Desk 1 Fidelity Brokerage Services LLC ("FBS"), Member NYSE, SIPC, 900 Salem Street, Smithfield, RI 02917 690099.3.0 Disclosures Options’ trading entails significant risk and is not appropriate for all investors. Certain complex options strategies carry additional risk. Before trading options, please read Characteristics and Risks of Standardized Options, and call 800-544- 5115 to be approved for options trading. Supporting documentation for any claims, if applicable, will be furnished upon request. Examples in this presentation do not include transaction costs (commissions, margin interest, fees) or tax implications, but they should be considered prior to entering into any transactions. The information in this presentation, including examples using actual securities and price data, is strictly for illustrative and educational purposes only and is not to be construed as an endorsement, or recommendation. 2 Disclosures (cont.) Greeks are mathematical calculations used to determine the effect of various factors on options. Active Trader Pro PlatformsSM is available to customers trading 36 times or more in a rolling 12-month period; customers who trade 120 times or more have access to Recognia anticipated events and Elliott Wave analysis. Technical analysis focuses on market action — specifically, volume and price. Technical analysis is only one approach to analyzing stocks. When considering which stocks to buy or sell, you should use the approach that you're most comfortable with. As with all your investments, you must make your own determination as to whether an investment in any particular security or securities is right for you based on your investment objectives, risk tolerance, and financial situation. -
FINANCIAL RISK the GREEKS the Whole of This Presentation Is As A
FINANCIAL RISK THE GREEKS The whole of this presentation is as a result of the collective efforts of both participants. MICHAEL AGYAPONG BOATENG Email: [email protected] MAVIS ASSIBEY-YEBOAH Email: [email protected] DECEMBER 1, 2011 Contents 1. INTRODUCTION 3 2. THE BLACK-SCHOLES MODEL 3 3. THE GREEKS 4 4. HIGHER-ORDER GREEKS 7 5. CONCLUSION 8 6. STUDY GUIDE 8 7. REFERENCES 8 2 1. INTRODUCTION The price of a single option or a position involving multiple options as the market changes is very difficult to predict. This results from the fact that price does not always move in correspondence with the price of the underlying asset. As such, it is of major interest to understand factors that contribute to the movement in price of an option, and what effect they have. Most option traders therefore turn to the Greeks which provide a means in measuring the sensitivity of an option price by quantifying the factors. The Greeks are so named because they are often denoted by Greek letters. The Greeks, as vital tools in risk management measures sensitivity of the value of the portfolio relative to a small change in a given underlying parameter. This treats accompanying risks in isolation to rebalance the portfolio accordingly so as to achieve a desirable exposure. In this project, the derivation and analysis of the Greeks will be based on the Black-Scholes model. This is because, they are easy to calculate, and are very useful for derivatives traders, especially those who seek to hedge their portfolios from adverse changes in market conditions. -
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.