1纽约大学教授Avellaneda：统计套利模型在美国股票市场的应用（英文版）

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Statistical Arbitrage in the U.S. Equities Market
第一作者是纽约大学克朗数学学院金融工程主任教授 Marco Avellaneda ，曾在Capital Fund Management任head of volatility arbitrage，摩根斯坦利任固定收益研究副总裁

实用分析统计套利模型分析：分析运用统计套利模型（基于ETF和PCA）对美国股票市场过去20多年的运用

摘要

We study model-driven statistical arbitrage in U.S. equities. The trading

signals are generated in two ways: using Principal Component Analysis

and using sector ETFs. In both cases, we consider the residuals, or idiosyncratic

components of stock returns, and model them as mean-reverting

processes. This leads naturally to “contrarian” trading signals.

The main contribution of the paper is the construction, back-testing

and comparison of market-neutral PCA- and ETF- based strategies applied

to the broad universe of U.S. stocks. Back-testing shows that, after

accounting for transaction costs, PCA-based strategies have an average

annual Sharpe ratio of 1.44 over the period 1997 to 2007, with

much stronger performances prior to 2003. During 2003-2007, the average

Sharpe ratio of PCA-based strategies was only 0.9. Strategies based

on ETFs achieved a Sharpe ratio of 1.1 from 1997 to 2007, experiencing

a similar degradation after 2002.

We also introduce a method to account for daily trading volume information

in the signals (which is akin to using “trading time” as opposed to

calendar time), and observe significant improvement in performance in the

case of ETF-based signals. ETF strategies which use volume information

achieve a Sharpe ratio of 1.51 from 2003 to 2007.

The paper also relates the performance of mean-reversion statistical

arbitrage strategies with the stock market cycle. In particular, we study

in detail the performance of the strategies during the liquidity crisis of the

summer of 2007. We obtain results which are consistent with Khandani

and Lo (2007) and validate their “unwinding” theory for the quant fund

drawdown of August 2007.

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关键词：套利模型 Lane 统计套利 大学教授 纽约大学 ETF 统计套利 美国股市

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