The Impact of Implied Volatility Fluctuations on Vertical Spread Option Strategies: the Case of WTI Crude Oil Market
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The TIPS-Treasury Bond Puzzle
The TIPS-Treasury Bond Puzzle Matthias Fleckenstein, Francis A. Longstaff and Hanno Lustig The Journal of Finance, October 2014 Presented By: Rafael A. Porsani The TIPS-Treasury Bond Puzzle 1 / 55 Introduction The TIPS-Treasury Bond Puzzle 2 / 55 Introduction (1) Treasury bond and the Treasury Inflation-Protected Securities (TIPS) markets: two of the largest and most actively traded fixed-income markets in the world. Find that there is persistent mispricing on a massive scale across them. Treasury bonds are consistently overpriced relative to TIPS. Price of a Treasury bond can exceed that of an inflation-swapped TIPS issue exactly matching the cash flows of the Treasury bond by more than $20 per $100 notional amount. One of the largest examples of arbitrage ever documented. The TIPS-Treasury Bond Puzzle 3 / 55 Introduction (2) Use TIPS plus inflation swaps to create synthetic Treasury bond. Price differences between the synthetic Treasury bond and the nominal Treasury bond: arbitrage opportunities. Average size of the mispricing: 54.5 basis points, but can exceed 200 basis points for some pairs. I The average size of this mispricing is orders of magnitude larger than transaction costs. The TIPS-Treasury Bond Puzzle 4 / 55 Introduction (3) What drives the mispricing? Slow-moving capital may help explain why mispricing persists. Is TIPS-Treasury mispricing related to changes in capital available to hedge funds? Answer: Yes. Mispricing gets smaller as more capital gets to the hedge fund sector. The TIPS-Treasury Bond Puzzle 5 / 55 Introduction (4) Also find that: Correlation in arbitrage strategies: size of TIPS-Treasury arbitrage is correlated with arbitrage mispricing in other markets. -
Machine Learning Based Intraday Calibration of End of Day Implied Volatility Surfaces
DEGREE PROJECT IN MATHEMATICS, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2020 Machine Learning Based Intraday Calibration of End of Day Implied Volatility Surfaces CHRISTOPHER HERRON ANDRÉ ZACHRISSON KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ENGINEERING SCIENCES Machine Learning Based Intraday Calibration of End of Day Implied Volatility Surfaces CHRISTOPHER HERRON ANDRÉ ZACHRISSON Degree Projects in Mathematical Statistics (30 ECTS credits) Master's Programme in Applied and Computational Mathematics (120 credits) KTH Royal Institute of Technology year 2020 Supervisor at Nasdaq Technology AB: Sebastian Lindberg Supervisor at KTH: Fredrik Viklund Examiner at KTH: Fredrik Viklund TRITA-SCI-GRU 2020:081 MAT-E 2020:044 Royal Institute of Technology School of Engineering Sciences KTH SCI SE-100 44 Stockholm, Sweden URL: www.kth.se/sci Abstract The implied volatility surface plays an important role for Front office and Risk Manage- ment functions at Nasdaq and other financial institutions which require mark-to-market of derivative books intraday in order to properly value their instruments and measure risk in trading activities. Based on the aforementioned business needs, being able to calibrate an end of day implied volatility surface based on new market information is a sought after trait. In this thesis a statistical learning approach is used to calibrate the implied volatility surface intraday. This is done by using OMXS30-2019 implied volatil- ity surface data in combination with market information from close to at the money options and feeding it into 3 Machine Learning models. The models, including Feed For- ward Neural Network, Recurrent Neural Network and Gaussian Process, were compared based on optimal input and data preprocessing steps. -
A Tree-Based Method to Price American Options in the Heston Model
The Journal of Computational Finance (1–21) Volume 13/Number 1, Fall 2009 A tree-based method to price American options in the Heston model Michel Vellekoop Financial Engineering Laboratory, University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands; email: [email protected] Hans Nieuwenhuis University of Groningen, Faculty of Economics, PO Box 800, 9700 AV Groningen, The Netherlands; email: [email protected] We develop an algorithm to price American options on assets that follow the stochastic volatility model defined by Heston. We use an approach which is based on a modification of a combined tree for stock prices and volatilities, where the number of nodes grows quadratically in the number of time steps. We show in a number of numerical tests that we get accurate results in a fast manner, and that features which are essential for the practical use of stock option pricing algorithms, such as the incorporation of cash dividends and a term structure of interest rates, can easily be incorporated. 1 INTRODUCTION One of the most popular models for equity option pricing under stochastic volatility is the one defined by Heston (1993): 1 dSt = µSt dt + Vt St dW (1.1) t = − + 2 dVt κ(θ Vt ) dt ω Vt dWt (1.2) In this model for the stock price process S and squared volatility process V the processes W 1 and W 2 are standard Brownian motions that may have a non-zero correlation coefficient ρ, while µ, κ, θ and ω are known strictly positive parameters. -
Tracking and Trading Volatility 155
ffirs.qxd 9/12/06 2:37 PM Page i The Index Trading Course Workbook www.rasabourse.com ffirs.qxd 9/12/06 2:37 PM Page ii Founded in 1807, John Wiley & Sons is the oldest independent publishing company in the United States. With offices in North America, Europe, Aus- tralia, and Asia, Wiley is globally committed to developing and marketing print and electronic products and services for our customers’ professional and personal knowledge and understanding. The Wiley Trading series features books by traders who have survived the market’s ever changing temperament and have prospered—some by reinventing systems, others by getting back to basics. Whether a novice trader, professional, or somewhere in-between, these books will provide the advice and strategies needed to prosper today and well into the future. For a list of available titles, visit our web site at www.WileyFinance.com. www.rasabourse.com ffirs.qxd 9/12/06 2:37 PM Page iii The Index Trading Course Workbook Step-by-Step Exercises and Tests to Help You Master The Index Trading Course GEORGE A. FONTANILLS TOM GENTILE John Wiley & Sons, Inc. www.rasabourse.com ffirs.qxd 9/12/06 2:37 PM Page iv Copyright © 2006 by George A. Fontanills, Tom Gentile, and Richard Cawood. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the web at www.copyright.com. -
Credit-Market Sentiment and the Business Cycle
Credit-Market Sentiment and the Business Cycle David L´opez-Salido∗ Jeremy C. Stein† Egon Zakrajˇsek‡ First draft: April 2015 This draft: December 2016 Abstract Using U.S. data from 1929 to 2015, we show that elevated credit-market sentiment in year t−2 is associated with a decline in economic activity in years t and t + 1. Underlying this result is the existence of predictable mean reversion in credit-market conditions. When credit risk is aggressively priced, spreads subsequently widen. The timing of this widening is, in turn, closely tied to the onset of a contraction in economic activity. Exploring the mechanism, we find that buoyant credit-market sentiment in year t−2 also forecasts a change in the composition of exter- nal finance: Net debt issuance falls in year t, while net equity issuance increases, consistent with the reversal in credit-market conditions leading to an inward shift in credit supply. Unlike much of the current literature on the role of financial frictions in macroeconomics, this paper suggests that investor sentiment in credit markets can be an important driver of economic fluctuations. JEL Classification: E32, E44, G12 Keywords: credit-market sentiment; financial stability; business cycles We are grateful to Olivier Blanchard, Claudia Buch, Bill English, Robin Greenwood, Sam Hanson, Oscar` Jord`a, Larry Katz, Arvind Krishnamurthy, H´el`ene Rey, Andrei Shleifer, the referees, and seminar participants at numerous institutions for helpful comments. Miguel Acosta, Ibraheem Catovic, Gregory Cohen, George Gu, Shaily Patel, and Rebecca Zhang provided outstanding research assistance. The views expressed in this paper are solely the responsibility of the authors and should not be interpreted as reflecting the views of the Board of Governors of the Federal Reserve System or of anyone else associated with the Federal Reserve System. -
THE BLACK-SCHOLES EQUATION in STOCHASTIC VOLATILITY MODELS 1. Introduction in Financial Mathematics There Are Two Main Approache
THE BLACK-SCHOLES EQUATION IN STOCHASTIC VOLATILITY MODELS ERIK EKSTROM¨ 1,2 AND JOHAN TYSK2 Abstract. We study the Black-Scholes equation in stochastic volatility models. In particular, we show that the option price is the unique classi- cal solution to a parabolic differential equation with a certain boundary behaviour for vanishing values of the volatility. If the boundary is at- tainable, then this boundary behaviour serves as a boundary condition and guarantees uniqueness in appropriate function spaces. On the other hand, if the boundary is non-attainable, then the boundary behaviour is not needed to guarantee uniqueness, but is nevertheless very useful for instance from a numerical perspective. 1. Introduction In financial mathematics there are two main approaches to the calculation of option prices. Either the price of an option is viewed as a risk-neutral expected value, or it is obtained by solving the Black-Scholes partial differ- ential equation. The connection between these approaches is furnished by the classical Feynman-Kac theorem, which states that a classical solution to a linear parabolic PDE has a stochastic representation in terms of an ex- pected value. In the standard Black-Scholes model, a standard logarithmic change of variables transforms the Black-Scholes equation into an equation with constant coefficients. Since such an equation is covered by standard PDE theory, the existence of a unique classical solution is guaranteed. Con- sequently, the option price given by the risk-neutral expected value is the unique classical solution to the Black-Scholes equation. However, in many situations outside the standard Black-Scholes setting, the pricing equation has degenerate, or too fast growing, coefficients and standard PDE theory does not apply. -
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]. -
Module 6 Option Strategies.Pdf
zerodha.com/varsity TABLE OF CONTENTS 1 Orientation 1 1.1 Setting the context 1 1.2 What should you know? 3 2 Bull Call Spread 6 2.1 Background 6 2.2 Strategy notes 8 2.3 Strike selection 14 3 Bull Put spread 22 3.1 Why Bull Put Spread? 22 3.2 Strategy notes 23 3.3 Other strike combinations 28 4 Call ratio back spread 32 4.1 Background 32 4.2 Strategy notes 33 4.3 Strategy generalization 38 4.4 Welcome back the Greeks 39 5 Bear call ladder 46 5.1 Background 46 5.2 Strategy notes 46 5.3 Strategy generalization 52 5.4 Effect of Greeks 54 6 Synthetic long & arbitrage 57 6.1 Background 57 zerodha.com/varsity 6.2 Strategy notes 58 6.3 The Fish market Arbitrage 62 6.4 The options arbitrage 65 7 Bear put spread 70 7.1 Spreads versus naked positions 70 7.2 Strategy notes 71 7.3 Strategy critical levels 75 7.4 Quick notes on Delta 76 7.5 Strike selection and effect of volatility 78 8 Bear call spread 83 8.1 Choosing Calls over Puts 83 8.2 Strategy notes 84 8.3 Strategy generalization 88 8.4 Strike selection and impact of volatility 88 9 Put ratio back spread 94 9.1 Background 94 9.2 Strategy notes 95 9.3 Strategy generalization 99 9.4 Delta, strike selection, and effect of volatility 100 10 The long straddle 104 10.1 The directional dilemma 104 10.2 Long straddle 105 10.3 Volatility matters 109 10.4 What can go wrong with the straddle? 111 zerodha.com/varsity 11 The short straddle 113 11.1 Context 113 11.2 The short straddle 114 11.3 Case study 116 11.4 The Greeks 119 12 The long & short straddle 121 12.1 Background 121 12.2 Strategy notes 122 12..3 Delta and Vega 128 12.4 Short strangle 129 13 Max pain & PCR ratio 130 13.1 My experience with option theory 130 13.2 Max pain theory 130 13.3 Max pain calculation 132 13.4 A few modifications 137 13.5 The put call ratio 138 13.6 Final thoughts 140 zerodha.com/varsity CHAPTER 1 Orientation 1.1 – Setting the context Before we start this module on Option Strategy, I would like to share with you a Behavioral Finance article I read couple of years ago. -
Users/Robertjfrey/Documents/Work
AMS 511.01 - Foundations Class 11A Robert J. Frey Research Professor Stony Brook University, Applied Mathematics and Statistics [email protected] http://www.ams.sunysb.edu/~frey/ In this lecture we will cover the pricing and use of derivative securities, covering Chapters 10 and 12 in Luenberger’s text. April, 2007 1. The Binomial Option Pricing Model 1.1 – General Single Step Solution The geometric binomial model has many advantages. First, over a reasonable number of steps it represents a surprisingly realistic model of price dynamics. Second, the state price equations at each step can be expressed in a form indpendent of S(t) and those equations are simple enough to solve in closed form. 1+r D-1 u 1 1 + r D 1 + r D y y ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ = u fl u = 1+r D u-1 u 1+r-u 1 u 1 u yd yd ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ1+r D ÅÅÅÅÅÅÅÅ1 uêÅÅÅÅ-ÅÅÅÅu ÅÅ H L H ê L i y As we will see shortlyH weL Hwill solveL the general problemj by solving a zsequence of single step problems on the lattice. That K O K O K O K O j H L H ê L z sequence solutions can be efficientlyê computed because wej only have to zsolve for the state prices once. k { 1.2 – Valuing an Option with One Period to Expiration Let the current value of a stock be S(t) = 105 and let there be a call option with unknown price C(t) on the stock with a strike price of 100 that expires the next three month period. -
Empirical Performance of Alternative Option Pricing Models For
Empirical Performance of Alternative Option Pricing Models for Commodity Futures Options (Very draft: Please do not quote) Gang Chen, Matthew C. Roberts, and Brian Roe¤ Department of Agricultural, Environmental, and Development Economics The Ohio State University 2120 Fy®e Road Columbus, Ohio 43210 Contact: [email protected] Selected Paper prepared for presentation at the American Agricultural Economics Association Annual Meeting, Providence, Rhode Island, July 24-27, 2005 Copyright 2005 by Gang Chen, Matthew C. Roberts, and Brian Boe. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. ¤Graduate Research Associate, Assistant Professor, and Associate Professor, Department of Agri- cultural, Environmental, and Development Economics, The Ohio State University, Columbus, OH 43210. Empirical Performance of Alternative Option Pricing Models for Commodity Futures Options Abstract The central part of pricing agricultural commodity futures options is to ¯nd appro- priate stochastic process of the underlying assets. The Black's (1976) futures option pricing model laid the foundation for a new era of futures option valuation theory. The geometric Brownian motion assumption girding the Black's model, however, has been regarded as unrealistic in numerous empirical studies. Option pricing models incor- porating discrete jumps and stochastic volatility have been studied extensively in the literature. This study tests the performance of major alternative option pricing models and attempts to ¯nd the appropriate model for pricing commodity futures options. Keywords: futures options, jump-di®usion, option pricing, stochastic volatility, sea- sonality Introduction Proper model for pricing agricultural commodity futures options is crucial to estimating implied volatility and e®ectively hedging in agricultural ¯nancial markets. -
A Fundamental Introduction to Japanese Candlestick Charting
© Copyright 2018 TheoTrade, LLC. All Rights Reserved 1 TRADE LIKE THE FLOUNDER An overview to Matt’s trading style, methods, thoughts, and insights © Copyright 2018 TheoTrade, LLC. All Rights Reserved 2 Acronyms and Shorthand Cheat Sheet • PnL – Profit and Loss (P. and L. → P n L) • IV – implied volatility • IRA – Individual Retirement Account • Vol – Volatility (typically, IMPLIED volatility) • DTE – Days to Expiration • Roll – move an option from one strike and/or expiration to a different strike and/or expiration • ATM/ITM/OTM/DITM – At the Money, In the Money, Out of the Money, DEEP in the Money • Legging – trading a piece of a spread by itself instead of the whole spread • Skew – the difference between supply/demand (or IV) at different strikes or expirations • Front month – the shorter-dated contract of a multiple-expiration spread • Back month – the longer-dated contract of a multiple-expiration spread • Opex – Option expiration © Copyright 2018 TheoTrade, LLC. All Rights Reserved 3 PART 1 – WHAT KIND OF TRADER AM I? Vehicle, products, time frames, structures, targets, etc. © Copyright 2018 TheoTrade, LLC. All Rights Reserved 4 What do I prefer to trade? Ask most people that question, they will tell you what PRODUCTS they like to trade (GOOG, AAPL, VIX, etc.) but before you settle on a product, learn WHAT you’re trading. Things you can trade: Direction (up or down) Volatility (IV vs. HV) Correlation/Relative Strength/Beta (Pairs Trading) Mispricing/Difference in pricing (Pure arbitrage) Order Flow (HFT) M&A/Special Situations (Merger Arb) Almost ALL retail traders trade direction. I primarily trade volatility. -
VERTICAL SPREADS: Taking Advantage of Intrinsic Option Value
Advanced Option Trading Strategies: Lecture 1 Vertical Spreads – The simplest spread consists of buying one option and selling another to reduce the cost of the trade and participate in the directional movement of an underlying security. These trades are considered to be the easiest to implement and monitor. A vertical spread is intended to offer an improved opportunity to profit with reduced risk to the options trader. A vertical spread may be one of two basic types: (1) a debit vertical spread or (2) a credit vertical spread. Each of these two basic types can be written as either bullish or bearish positions. A debit spread is written when you expect the stock movement to occur over an intermediate or long- term period [60 to 120 days], whereas a credit spread is typically used when you want to take advantage of a short term stock price movement [60 days or less]. VERTICAL SPREADS: Taking Advantage of Intrinsic Option Value Debit Vertical Spreads Bull Call Spread During March, you decide that PFE is going to make a large up move over the next four months going into the Summer. This position is due to your research on the portfolio of drugs now in the pipeline and recent phase 3 trials that are going through FDA approval. PFE is currently trading at $27.92 [on March 12, 2013] per share, and you believe it will be at least $30 by June 21st, 2013. The following is the option chain listing on March 12th for PFE. View By Expiration: Mar 13 | Apr 13 | May 13 | Jun 13 | Sep 13 | Dec 13 | Jan 14 | Jan 15 Call Options Expire at close Friday,