Alireza Javaheri

Inside Volatility Filtering

Secrets of the Skew

• Gebundenes Buch

Produktbeschreibung

Author and financial expert Alireza Javaheri uses the classic approach to evaluating volatility--time series and financial econometrics--in a way that he believes is superior to methods presently used by market participants. He also suggests that there may be "skewness" trading opportunities that can be sued to trade the markets mroe profitably. Filed with in-depth insight and expert advice, this book will focus on the idea of filtering.

The idea behind filtering is to obtain the best possible estimation of a hidden state given all the available information up to that point. This estimation is done in an iterative manner in two stages: The first step is a time update in which the prior distribution from all the past information via a Chapman-Kolmogorov equation. The second step would then involve a measurement update where this prior distribution is used together with the conditional likelihood of the newest observation in order to compute the posterior distribution of the hidden state. The Bayes rule is used for this purpose. Once the posterior distribution is determined, it can be exploited for the optimal estimation of the hidden state.

For practitioners and students, the author is adding content on:
* estimation from historic option prices instead of stocks, as the observation quality is better
* spectral approaches and in particular Wiener Chaos Expansions
* on the statistical trading strategy in section 3

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A new, more accurate take on the classical approach to volatility evaluation
Inside Volatility Filtering presents a new approach to volatility estimation, using financial econometrics based on a more accurate estimation of the hidden state. Based on the idea of "filtering", this book lays out a two-step framework involving a Chapman-Kolmogorov prior distribution followed by Bayesian posterior distribution to develop a robust estimation based on all available information. This new second edition includes guidance toward basing estimations on historic option prices instead of stocks, as well as Wiener Chaos Expansions and other spectral approaches. The author s statistical trading strategy has been expanded with more in-depth discussion, and the companion website offers new topical insight, additional models, and extra charts that delve into the profitability of applied model calibration. You ll find a more precise approach to the classical time series and financial econometrics evaluation, with expert advice on turning data into profit.

Financial markets do not always behave according to a normal bell curve. Skewness creates uncertainty and surprises, and tarnishes trading performance, but it s not going away. This book shows traders how to work with skewness: how to predict it, estimate its impact, and determine whether the data is presenting a warning to stay away or an opportunity for profit.

Base volatility estimations on more accurate data
Integrate past observation with Bayesian probability
Exploit posterior distribution of the hidden state for optimal estimation
Boost trade profitability by utilizing "skewness" opportunities
Wall Street is constantly searching for volatility assessment methods that will make their models more accurate, but precise handling of skewness is the key to true accuracy. Inside Volatility Filtering shows you a better way to approach non-normal distributions for more accurate volatility estimation.

Produktdetails

• Wiley Finance Series
• Verlag: Wiley & Sons
• 2. Aufl.
• Seitenzahl: 320
• Erscheinungstermin: September 2015
• Englisch
• Abmessung: 235mm x 157mm x 22mm
• Gewicht: 516g
• ISBN-13: 9781118943977
• ISBN-10: 111894397X
• Artikelnr.: 42198532

Autorenporträt

ALIREZA JAVAHERI is the head of Equities Quantitative Research Americas at JP Morgan and an adjunct professor of Mathematical Finance at the Courant Institute of New York University, as well as Baruch College. He has worked in the field of derivatives quantitative research since 1994 in a variety of investment banks, including Goldman Sachs and Citigroup.

Inhaltsangabe

Foreword ix Acknowledgments (Second Edition) xi Acknowledgments (First
Edition) xiii Introduction (Second Edition) xv Introduction (First Edition)
xvii Summary xvii Contributions and Further Research xxiii Data and
Programs xxiv CHAPTER 1 The Volatility Problem 1 Introduction 1 The Stock
Market 2 The Stock Price Process 2 Historic Volatility 3 The Derivatives
Market 5 The Black-Scholes Approach 5 The Cox Ross Rubinstein Approach 7
Jump Diffusion and Level-Dependent Volatility 8 Jump Diffusion 8
Level-Dependent Volatility 11 Local Volatility 14 The Dupire Approach 14
The Derman Kani Approach 17 Stability Issues 18 Calibration Frequency 19
Stochastic Volatility 21 Stochastic Volatility Processes 21 GARCH and
Diffusion Limits 22 The Pricing PDE under Stochastic Volatility 26 The
Market Price of Volatility Risk 26 The Two-Factor PDE 27 The Generalized
Fourier Transform 28 The Transform Technique 28 Special Cases 30 The Mixing
Solution 32 The Romano Touzi Approach 32 A One-Factor Monte-Carlo Technique
34 The Long-Term Asymptotic Case 35 The Deterministic Case 35 The
Stochastic Case 37 A Series Expansion on Volatility-of-Volatility 39 Local
Volatility Stochastic Volatility Models 42 Stochastic Implied Volatility 43
Joint SPX and VIX Dynamics 45 Pure-Jump Models 47 Variance Gamma 47
Variance Gamma with Stochastic Arrival 51 Variance Gamma with Gamma Arrival
Rate 53 CHAPTER 2 The Inference Problem 55 Introduction 55 Using Option
Prices 58 Conjugate Gradient (Fletcher-Reeves-Polak-Ribiere) Method 59
Levenberg-Marquardt (LM) Method 59 Direction Set (Powell) Method 61 Numeric
Tests 62 The Distribution of the Errors 65 Using Stock Prices 65 The
Likelihood Function 65 Filtering 69 The Simple and Extended Kalman Filters
72 The Unscented Kalman Filter 74 Kushner's Nonlinear Filter 77 Parameter
Learning 80 Parameter Estimation via MLE 95 Diagnostics 108 Particle
Filtering 111 Comparing Heston with Other Models 133 The Performance of the
Inference Tools 141 The Bayesian Approach 158 Using the Characteristic
Function 172 Introducing Jumps 174 Pure-Jump Models 184 Recapitulation 201
Model Identification 201 Convergence Issues and Solutions 202 CHAPTER 3 The
Consistency Problem 203 Introduction 203 The Consistency Test 206 The
Setting 206 The Cross-Sectional Results 206 Time-Series Results 209
Financial Interpretation 210 The "Peso" Theory 214 Background 214 Numeric
Results 215 Trading Strategies 216 Skewness Trades 216 Kurtosis Trades 217
Directional Risks 217 An Exact Replication 219 The Mirror Trades 220 An
Example of the Skewness Trade 220 Multiple Trades 225 High
Volatility-of-Volatility and High Correlation 225 Non-Gaussian Case 230
VGSA 232 A Word of Caution 236 Foreign Exchange, Fixed Income, and Other
Markets 237 Foreign Exchange 237 Fixed Income 238 CHAPTER 4 The Quality
Problem 241 Introduction 241 An Exact Solution? 241 Nonlinear Filtering 242
Stochastic PDE 243 Wiener Chaos Expansion 244 First-Order WCE 247
Simulations 248 Second-Order WCE 251 Quality of Observations 251 Historic
Spot Prices 252 Historic Option Prices 252 Conclusion 262 Bibliography 263
Index 279