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Inside Volatility Filtering Secrets of the Skew 2nd Edition by Alireza Javaheri ISBN 1118943984 978-1118943984

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Product details:

ISBN 10: 1118943984
ISBN 13: 978-1118943984
Author: Alireza Javaheri 

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.

Inside Volatility Filtering Secrets of the Skew 2nd Table of contents:

CHAPTER 1 — The Volatility Problem

  • Introduction

  • The Stock Market

  • The Stock Price Process

  • Historic Volatility

  • The Derivatives Market

  • The Black-Scholes Approach

  • The Cox Ross Rubinstein Approach

Jump Diffusion and Level-Dependent Volatility

  • Jump Diffusion

  • Level-Dependent Volatility

Local Volatility

  • The Dupire Approach

  • The Derman Kani Approach

  • Stability Issues

  • Calibration Frequency

Stochastic Volatility

  • Stochastic Volatility Processes

  • GARCH and Diffusion Limits

  • The Pricing PDE under Stochastic Volatility

  • The Market Price of Volatility Risk

  • The Two-Factor PDE

The Generalized Fourier Transform

  • The Transform Technique

  • Special Cases

  • The Mixing Solution

  • The Romano Touzi Approach

  • A One-Factor Monte-Carlo Technique

The Long-Term Asymptotic Case

  • The Deterministic Case

  • The Stochastic Case

  • A Series Expansion on Volatility-of-Volatility

Local Volatility Stochastic Volatility Models

  • Stochastic Implied Volatility

  • Joint SPX and VIX Dynamics

Pure-Jump Models

  • Variance Gamma

  • Variance Gamma with Stochastic Arrival

  • Variance Gamma with Gamma Arrival Rate

CHAPTER 2 — The Inference Problem

  • Introduction

  • Using Option Prices

    • Conjugate Gradient (Fletcher-Reeves-Polak-Ribiere) Method

    • Levenberg-Marquardt (LM) Method

    • Direction Set (Powell) Method

    • Numeric Tests

    • The Distribution of the Errors

  • Using Stock Prices

    • The Likelihood Function

    • Filtering

    • The Simple and Extended Kalman Filters

    • The Unscented Kalman Filter

    • Kushner’s Nonlinear Filter

  • Parameter Learning

    • Parameter Estimation via MLE

    • Diagnostics

    • Particle Filtering

  • Comparing Heston with Other Models

  • The Performance of the Inference Tools

The Bayesian Approach

  • Using the Characteristic Function

  • Introducing Jumps

  • Pure-Jump Models

  • Recapitulation

  • Model Identification

  • Convergence Issues and Solutions

CHAPTER 3 — The Consistency Problem

  • Introduction

  • The Consistency Test

    • The Setting

    • The Cross-Sectional Results

    • Time-Series Results

    • Financial Interpretation

The “Peso” Theory

  • Background

  • Numeric Results

  • Trading Strategies

    • Skewness Trades

    • Kurtosis Trades

    • Directional Risks

    • An Exact Replication

    • The Mirror Trades

    • An Example of the Skewness Trade

    • Multiple Trades

    • High Volatility-of-Volatility and High Correlation

    • Non-Gaussian Case

    • VGSA

    • A Word of Caution

Foreign Exchange, Fixed Income, and Other Markets

  • Foreign Exchange

  • Fixed Income

CHAPTER 4 — The Quality Problem

  • Introduction

  • An Exact Solution?

  • Nonlinear Filtering

  • Stochastic PDE

  • Wiener Chaos Expansion

    • First-Order WCE

    • Simulations

    • Second-Order WCE

  • Quality of Observations

    • Historic Spot Prices

    • Historic Option Prices

  • Conclusion

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Tags: Alireza Javaheri, Inside Volatility, Filtering Secrets