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《統計套利》什麼是統計套利？假設，工商銀行和建設銀行股票價差大於1元的情況很少，那麼當市場的價格波動導緻建設銀行的股票價格比工商銀行高齣1元時，你可以通過賣齣建設銀行股票、買進工商銀行股票構建投資組閤；當兩傢公司的股票價差迴歸到1元以內時，做相反的交易，從而獲得交易收益。如何通過股指期貨和股票聯動，利用市場暫時的失靈，獲得無風險收益，是很多人的想法。如果想長期獲得收益，需要通讀本書，理解統計套利的精髓。《統計套利》作者具有多年運作統計套利對衝基金的管理經驗，通過閱讀本書，你可以探究統計套利的真正含義，瞭解統計套利的發展曆程。更重要的是，在明瞭統計套利的運作方式和獲利機理後，敏銳的投資者可以藉此在金融市場中捕捉獲利的機會。

·匹配交易的基本原理；

·重要的時間序列模型，從最基本的加權移動平均模型，到復雜的動態因子分析模型；

·爆米花理論、反轉理論、突變理論等；·統計套利15年的曆程。 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.

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統計套利

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.

Preface.
Foreword.
Acknowledgments.
Chapter 1. Monte Carlo or Bust.
Beginning.
Whither? And Allusions.
Chapter 2. Statistical Arbitrage.
Introduction.
Noise Models.
Reverse Bets.
Multiple Bets.
Rule Calibration.
Spread Margins for Trade Rules.
Popcorn Process.
Identifying Pairs.
Refining Pair Selection.
Event Analysis.
Correlation Search in the Twenty-First Century.
Portfolio Configuration and Risk Control.
Exposure to Market Factors.
Market Impact.
Risk Control Using Event Correlations.
Dynamics and Calibration.
Evolutionary Operation: Single Parameter Illustration.
Chapter 3. Structural Models.
Introduction.
Formal Forecast Functions.
Exponentially Weighted Moving Average.
Classical Time Series Models.
Autoregression and Cointegration.
Dynamic Linear Model.
Volatility Modeling.
Pattern Finding Techniques.
Fractal Analysis.
Which Return?
A Factor Model.
Factor Analysis.
Defactored Returns.
Prediction Model.
Stochastic Resonance.
Practical Matters.
Doubling: A Deeper Perspective.
Factor Analysis Primer.
Prediction Model for Defactored Returns.
Chapter 4. Law of Reversion.
Introduction.
Model and Result.
The 75 percent Rule.
Proof of the 75 percent Rule.
Analytic Proof of the 75 percent Rule.
Discrete Counter.
Generalizations.
Inhomogeneous Variances.
Volatility Bursts.
Numerical Illustration.
First-Order Serial Correlation.
Analytic Proof.
Examples.
Nonconstant Distributions.
Applicability of the Result.
Application to U.S. Bond Futures.
Summary.
Appendix 4.1: Looking Several Days Ahead.
Chapter 5. Gauss is Not the God of Reversion.
Introduction.
Camels and Dromedaries.
Dry River Flow.
Some Bells Clang.
Chapter 6. Interstock Volatility.
Introduction.
Theoretical Explanation.
Theory versus Practice.
Finish the Theory.
Finish the Examples.
Primer on Measuring Spread Volatility.
Chapter 7. Quantifying Reversion Opportunities.
Introduction.
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.
Serial Correlation.
Appendix 7.1: Details of the Lognormal Case in Example.
Chapter 8. Nobel Difficulties.
Introduction.
Event Risk.
Will Narrowing Spreads Guarantee Profits?
Rise of a New Risk Factor.
Redemption Tension.
Supercharged Destruction.
The Story of Regulation Fair Disclosure (FD).
Correlation During Loss Episodes.
Chapter 9. Trinity Troubles.
Introduction.
Decimalization.
European Experience.
Advocating the Devil.
Stat. Arb. Arbed Away.
Competition.
Institutional Investors.
Volatility Is the Key.
Interest Rates and Volatility.
Temporal Considerations.
Truth in Fiction.
A Litany of Bad Behavior.
A Perspective on 2003.
Realities of Structural Change.
Recap.
Chapter 10. Arise Black Boxes.
Introduction.
Modeling Expected Transaction Volume and Market Impact.
Dynamic Updating.
More Black Boxes.
Market Deflation.
Chapter 11. Statistical Arbitrage Rising.
Catastrophe Process.
Catastrophic Forecasts.
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.
Bibliography.
Index.
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