Application of Black-Litterman Bayesian in Statistical Arbitrage

Fuente: arXiv
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Main Author: Zhou, Qiqin
Format: Preprint
Published: 2024
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author Zhou, Qiqin
author_facet Zhou, Qiqin
contents \begin{abstract} In this paper, we integrated the statistical arbitrage strategy, pairs trading, into the Black-Litterman model and constructed efficient mean-variance portfolios. Typically, pairs trading underperforms under volatile or distressed market condition because the selected asset pairs fail to revert to equilibrium within the investment horizon. By enhancing this strategy with the Black-Litterman portfolio optimization, we achieved superior performance compared to the S\&P 500 market index under both normal and extreme market conditions. Furthermore, this research presents an innovative idea of incorporating traditional pairs trading strategies into the portfolio optimization framework in a scalable and systematic manner.
format Preprint
id arxiv_https___arxiv_org_abs_2406_06706
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Application of Black-Litterman Bayesian in Statistical Arbitrage
Zhou, Qiqin
Computational Finance
Applications
\begin{abstract} In this paper, we integrated the statistical arbitrage strategy, pairs trading, into the Black-Litterman model and constructed efficient mean-variance portfolios. Typically, pairs trading underperforms under volatile or distressed market condition because the selected asset pairs fail to revert to equilibrium within the investment horizon. By enhancing this strategy with the Black-Litterman portfolio optimization, we achieved superior performance compared to the S\&P 500 market index under both normal and extreme market conditions. Furthermore, this research presents an innovative idea of incorporating traditional pairs trading strategies into the portfolio optimization framework in a scalable and systematic manner.
title Application of Black-Litterman Bayesian in Statistical Arbitrage
topic Computational Finance
Applications
url https://arxiv.org/abs/2406.06706