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Portfolio Optimization using Rank Correlation
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Author(s): Chanaka Edirisinghe (University of Tennessee, USA)and Wenjun Zhou (University of Tennessee, USA)
Copyright: 2014
Pages: 14
Source title:
Encyclopedia of Business Analytics and Optimization
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-4666-5202-6.ch167
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Abstract
A critical challenge in managing quantitative funds is the computation of volatilities and correlations of the underlying financial assets. We present a study of Kendall's t coefficient, one of the best-known rank-based correlation measures, for computing the portfolio risk. Incorporating within risk-averse portfolio optimization, we show empirically that this correlation measure outperforms that of Pearson's in our out-of-sample testing with real-world financial data. This phenomenon is mainly due to the fat-tailed nature of stock return distributions. We also discuss computational properties of Kendall's t, and describe efficient procedures for incremental and one-time computation of Kendall's rank correlation.
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