Measuring financial risk with generalized asymmetric least squares regression

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Yongqiao Wang
  • Shouyang Wang
  • K. K. Lai

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)5793-5800
Journal / PublicationApplied Soft Computing Journal
Volume11
Issue number8
Publication statusPublished - Dec 2011

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. © 2011 Elsevier B.V. All rights reserved.

Research Area(s)

  • Asymmetric least squares regression, Expected shortfall, Kernel trick, Risk measurement, Value-at-risk

Citation Format(s)

Measuring financial risk with generalized asymmetric least squares regression. / Wang, Yongqiao; Wang, Shouyang; Lai, K. K.

In: Applied Soft Computing Journal, Vol. 11, No. 8, 12.2011, p. 5793-5800.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review