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Algorithmic Trading Insights and Techniques
BY Andrew Pole
While statistical arbitrage has faced some tough times as markets experienced dramatic changes in dynamics beginning in 2000 new developments in algorithmic trading have allowed it to rise from the ashes of that fire. Based on the results of author Andrew Pole s own research and experience running a statistical arbitrage hedge fund for eight years in partnership with a group whose own history stretches back to the dawn of what was first called pairs trading this unique guide provides detailed insights into the nuances of a proven investment strategy. Filled with in-depth insights and expert advice, Statistical Arbitrage contains comprehensive analysis that will appeal to both investors looking for an overview of this discipline, as well as quants looking for critical insights into modeling, risk management, and implementation of the strategy.
Andrew Pole is a Managing Director at TIG Advisors, LLC, a registered investment advisor in New York. He specializes in quantitative trading strategies and risk management. This book is the result of his own research and experience running a statistical arbitrage hedge fund for eight years. Pole is also the coauthor of Applied Bayesian Forecasting and Time Series Analysis.
TABLE OF CONTENTS:
Chapter 1. Monte Carlo or Bust.
Whither? And Allusions.
Chapter 2. Statistical Arbitrage.
Spread Margins for Trade Rules.
Refining Pair Selection.
Correlation Search in the Twenty-First Century.
Portfolio Configuration and Risk Control.
Exposure to Market Factors.
Risk Control Using Event Correlations.
Dynamics and Calibration.
Evolutionary Operation: Single Parameter Illustration.
Chapter 3. Structural Models.
Formal Forecast Functions.
Exponentially Weighted Moving Average.
Classical Time Series Models.
Autoregression and Cointegration.
Dynamic Linear Model.
Pattern Finding Techniques.
A Factor Model.
Doubling: A Deeper Perspective.
Factor Analysis Primer.
Prediction Model for Defactored Returns.
Chapter 4. Law of Reversion.
Model and Result.
The 75 percent Rule.
Proof of the 75 percent Rule.
Analytic Proof of the 75 percent Rule.
First-Order Serial Correlation.
Applicability of the Result.
Application to U.S. Bond Futures.
Appendix 4.1: Looking Several Days Ahead.
Chapter 5. Gauss is Not the God of Reversion.
Camels and Dromedaries.
Dry River Flow.
Some Bells Clang.
Chapter 6. Interstock Volatility.
Theory versus Practice.
Finish the Theory.
Finish the Examples.
Primer on Measuring Spread Volatility.
Chapter 7. Quantifying Reversion Opportunities.
Reversion in a Stationary Random Process.
Frequency of Reversionary Moves.
Amount of Reversion.
Movements from Quantiles Other Than the Median.
Nonstationary Processes: Inhomogeneous Variance.
Sequentially Structured Variances.
Sequentially Unstructured Variances.
Appendix 7.1: Details of the Lognormal Case in Example.
Chapter 8. Nobel Difficulties.
Will Narrowing Spreads Guarantee Profits?
Rise of a New Risk Factor.
The Story of Regulation Fair Disclosure (FD).
Correlation During Loss Episodes.
Chapter 9. Trinity Troubles.
Advocating the Devil.
Stat. Arb. Arbed Away.
Volatility Is the Key.
Interest Rates and Volatility.
Truth in Fiction.
A Litany of Bad Behavior.
A Perspective on 2003.
Realities of Structural Change.
Chapter 10. Arise Black Boxes.
Modeling Expected Transaction Volume and Market Impact.
More Black Boxes.
Chapter 11. Statistical Arbitrage Rising.
Trend Change Identification.
Using the Cuscore to Identify a Catastrophe.
Is It Over?
Catastrophe Theoretic Interpretation.
Implications for Risk Management.
Appendix 11.1: Understanding the Cuscore.