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Book Description

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.

    Table of Contents

    1. Title Page
    2. Copyright
    3. Foreword
    4. Acknowledgments (Second Edition)
    5. Acknowledgments (First Edition)
    6. Introduction (Second Edition)
    7. Introduction (First Edition)
      1. Summary
      2. Contributions and Further Research
      3. Data and Programs
    8. Chapter 1: The Volatility Problem
      1. Introduction
      2. The Stock Market
      3. The Derivatives Market
      4. Jump Diffusion and Level-Dependent Volatility
      5. Local Volatility
      6. Stochastic Volatility
      7. The Pricing PDE under Stochastic Volatility
      8. The Generalized Fourier Transform
      9. The Mixing Solution
      10. The Long-Term Asymptotic Case
      11. Local Volatility Stochastic Volatility Models
      12. Stochastic Implied Volatility
      13. Joint SPX and VIX Dynamics
      14. Pure-Jump Models
    9. Chapter 2: The Inference Problem
      1. Introduction
      2. Using Option Prices
      3. Using Stock Prices
      4. Recapitulation
    10. Chapter 3: The Consistency Problem
      1. Introduction
      2. The Consistency Test
      3. The “Peso” Theory
      4. Trading Strategies
      5. Non-Gaussian Case
      6. A Word of Caution
      7. Foreign Exchange, Fixed Income, and Other Markets
    11. Chapter 4: The Quality Problem
      1. Introduction
      2. An Exact Solution?
      3. Quality of Observations
      4. Conclusion
    12. Bibliography
    13. Index
    14. End User License Agreement
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