Measuring financial risk with generalized asymmetric least squares regression

Authors:
Yongqiao Wang;Shouyang Wang;K. K. Lai
Affiliations:
College of Finance, Zhejiang Gongshang University, Hangzhou 310018, Zhejiang, China;Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China;Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
Venue:
Applied Soft Computing
Year:
2011

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Abstract

Abstract: This paper proposes a generalized asymmetric least squares regression method to estimate Value-at-risk and expected shortfall. By solving an asymmetric least squares regression in a Reproducing Kernel Hilbert Space, the method achieves nonlinear prediction power, while making no assumption on the underlying probability distributions. Two toy datasets are used to demonstrate its nonlinear prediction power. The empirical results on the S&P 500 stock index obviously show that the method is superior to other four benchmark methods.