Statistical Arbitrage: Algorithmic Trading Insights and by Andrew Pole

By Andrew Pole

Whereas statistical arbitrage has confronted a few difficult times?as markets skilled dramatic adjustments in dynamics starting in 2000?new advancements in algorithmic buying and selling have allowed it to upward push from the ashes of that fireside. in accordance with the result of writer Andrew Pole?s personal study and event operating a statistical arbitrage hedge fund for 8 years?in partnership with a gaggle whose personal historical past stretches again to the sunrise of what used to be first known as pairs trading?this distinct advisor offers targeted insights into the nuances of a confirmed funding procedure. choked with in-depth insights and specialist recommendation, Statistical Arbitrage includes accomplished research that may attract either traders searching for an outline of this self-discipline, in addition to quants trying to find serious insights into modeling, possibility administration, and implementation of the method

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Significant stress would quickly ensue as the spread increased to over $20 by July. Losses would still be on the books at the end of the year. Clearly we will have to determine a different calibration for any of Rules 1–3. Equally clearly, the basic form of the rules will work just fine. Now consider the problem of calibration applied to hundreds or thousands of potential spreads. Eyeballing graphs would require a lot of eyeballs. A numerical procedure, an automatic way of calibrating rules, is needed.

Analysis of a classic pair-trading strategy employing a first-order, dynamic linear model (see Chapter 3) and exhibiting a holding period of about two weeks applied to large capital equities shows a fascinating and revealing development. In March 2000 a trend to a lower frequency that began in 1996 was discovered. First hinted at in 1996, the scale of the change was within experienced local variation bounds, so the hint was only identifiable later. Movement in 1997 was marginal. In 1998, the problems with international credit defaults and the Long Term Capital Management debacle totally disrupted all patterns of performance making inference difficult and hazardous.

Fn ) Expected variance of returns, V[f ] Value to be invested in stock i; p = (p1 , . . 1 Exposure to Market Factors Statistical arbitrage fund managers typically do not want a portfolio that takes long positions only: Such a portfolio is exposed to the market. ) If the market crashes, the value of the portfolio crashes with it. This much we can say regardless of the precise composition of the portfolio. Given a desire for a market neutral strategy, the goal is to pick off moves in stock prices after allowing for overall market movement.

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