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E-grāmata: Statistical Arbitrage: Algorithmic Trading Insights and Techniques

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  • Formāts: EPUB+DRM
  • Sērija : Wiley Finance
  • Izdošanas datums: 07-Jul-2011
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118160732
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Statistical Arbitrage: Algorithmic Trading Insights and TechniquesPalielināt
  • Formāts: EPUB+DRM
  • Sērija : Wiley Finance
  • Izdošanas datums: 07-Jul-2011
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118160732
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While statistical arbitrage has faced some tough timesas markets experienced dramatic changes in dynamics beginning in 2000new developments in algorithmic trading have allowed it to rise from the ashes of that fire. Based on the results of author Andrew Poles own research and experience running a statistical arbitrage hedge fund for eight yearsin partnership with a group whose own history stretches back to the dawn of what was first called pairs tradingthis 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.

Recenzijas

"Over time, anything that creates an edge for a particular group of bettorsincluding the most astute observers of horse fleshgets factored into the odds and becomes unreliable as a system. That's the classic argument of random walk theorists, and the equally classic response is that there's a lot of money to be made before that factoring is complete. This book is a contribution to that never-ending debate." (Hedgeworld.com)

Preface xiii
Foreword xix
Acknowledgments xxiii
Monte Carlo or Bust
1(8)
Beginning
1(3)
Whither? And Allusions
4(5)
Statistical Arbitrage
9(28)
Introduction
9(1)
Noise Models
10(8)
Reverse Bets
11(1)
Multiple Bets
11(1)
Rule Calibration
12(4)
Spread Margins for Trade Rules
16(2)
Popcorn Process
18(2)
Identifying Pairs
20(6)
Refining Pair Selection
21(1)
Event Analysis
22(4)
Correlation Search in the Twenty-First Century
26(1)
Portfolio Configuration and Risk Control
26(6)
Exposure to Market Factors
29(1)
Market Impact
30(1)
Risk Control Using Event Correlations
31(1)
Dynamics and Calibration
32(5)
Evolutionary Operation: Single Parameter Illustration
34(3)
Structural Models
37(30)
Introduction
37(2)
Formal Forecast Functions
39(1)
Exponentially Weighted Moving Average
40(7)
Classical Time Series Models
47(5)
Autoregression and Cointegration
47(2)
Dynamic Linear Model
49(1)
Volatility Modeling
50(1)
Pattern Finding Techniques
51(1)
Fractal Analysis
52(1)
Which Return?
52(1)
A Factor Model
53(5)
Factor Analysis
54(1)
Defactored Returns
55(2)
Prediction Model
57(1)
Stochastic Resonance
58(1)
Practical Matters
59(2)
Doubling: A Deeper Perspective
61(2)
Factor Analysis Primer
63(4)
Prediction Model for Defactored Returns
65(2)
Law of Reversion
67(24)
Introduction
67(1)
Model and Result
68(6)
The 75 percent Rule
68(1)
Proof of the 75 percent Rule
69(2)
Analytic Proof of the 75 percent Rule
71(2)
Discrete Counter
73(1)
Generalizations
73(1)
Inhomogeneous Variances
74(3)
Volatility Bursts
75(1)
Numerical Illustration
76(1)
First-Order Serial Correlation
77(5)
Analytic Proof
79(3)
Examples
82(1)
Nonconstant Distributions
82(2)
Applicability of the Result
84(1)
Application to U.S. Bond Futures
85(2)
Summary
87(1)
Appendix 4.1: Looking Several Days Ahead
87(4)
Gauss Is Not the God of Reversion
91(8)
Introduction
91(1)
Camels and Dromedaries
92(6)
Dry River Flow
95(3)
Some Bells Clang
98(1)
Interstock Volatility
99(14)
Introduction
99(4)
Theoretical Explanation
103(10)
Theory versus Practice
105(1)
Finish the Theory
105(1)
Finish the Examples
106(2)
Primer on Measuring Spread Volatility
108(5)
Quantifying Reversion Opportunities
113(28)
Introduction
113(1)
Reversion in a Stationary Random Process
114(22)
Frequency of Reversionary Moves
117(1)
Amount of Reversion
118(17)
Movements from Quantiles Other Than the Median
135(1)
Nonstationary Processes: Inhomogeneous Variance
136(2)
Sequentially Structured Variances
136(1)
Sequentially Unstructured Variances
137(1)
Serial Correlation
138(1)
Appendix 7.1: Details of the Lognormal Case in Example 6
139(2)
Nobel Difficulties
141(14)
Introduction
141(1)
Event Risk
142(3)
Will Narrowing Spreads Guarantee Profits?
144(1)
Rise of a New Risk Factor
145(3)
Redemption Tension
148(2)
Supercharged Destruction
150(1)
The Story of Regulation Fair Disclosure (FD)
150(1)
Correlation During Loss Episodes
151(4)
Trinity Troubles
155(28)
Introduction
155(1)
Decimalization
156(3)
European Experience
157(1)
Advocating the Devil
158(1)
Stat. Arb. Arbed Away
159(1)
Competition
160(3)
Institutional Investors
163(1)
Volatility Is the Key
163(3)
Interest Rates and Volatility
165(1)
Temporal Considerations
166(8)
Truth in Fiction
174(1)
A Litany of Bad Behavior
174(4)
A Perspective on 2003
178(1)
Realities of Structural Change
179(1)
Recap
180(3)
Arise Black Boxes
183(8)
Introduction
183(2)
Modeling Expected Transaction Volume and Market Impact
185(3)
Dynamic Updating
188(1)
More Black Boxes
189(1)
Market Deflation
189(2)
Statistical Arbitrage Rising
191(32)
Catastrophe Process
194(4)
Catastrophic Forecasts
198(2)
Trend Change Identification
200(5)
Using the Cuscore to Identify a Catastrophe
202(2)
Is It Over?
204(1)
Catastrophe Theoretic Interpretation
205(4)
Implications for Risk Management
209(2)
Sign Off
211(1)
Appendix 11.1: Understanding the Cuscore
211(12)
Bibliography 223(2)
Index 225


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.
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