Assessing value at risk with CARE, the Conditional Autoregressive Expectile models

Chung Ming Kuan, Jin Huei Yeh, Yu Chin Hsu

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98 Scopus citations

Abstract

In this paper we propose a downside risk measure, the expectile-based Value at Risk (EVaR), which is more sensitive to the magnitude of extreme losses than the conventional quantile-based VaR (QVaR). The index θ of an EVaR is the relative cost of the expected margin shortfall and hence reflects the level of prudentiality. It is also shown that a given expectile corresponds to the quantiles with distinct tail probabilities under different distributions. Thus, an EVaR may be interpreted as a flexible QVaR, in the sense that its tail probability is determined by the underlying distribution. We further consider conditional EVaR and propose various Conditional AutoRegressive Expectile models that can accommodate some stylized facts in financial time series. For model estimation, we employ the method of asymmetric least squares proposed by Newey and Powell [Newey, W.K., Powell, J.L., 1987. Asymmetric least squares estimation and testing. Econometrica 55, 819-847] and extend their asymptotic results to allow for stationary and weakly dependent data. We also derive an encompassing test for non-nested expectile models. As an illustration, we apply the proposed modeling approach to evaluate the EVaR of stock market indices.

Original languageEnglish
Pages (from-to)261-270
Number of pages10
JournalJournal of Econometrics
Volume150
Issue number2
DOIs
StatePublished - Jun 2009

Keywords

  • Asymmetric least squares
  • CARE model
  • Expectile
  • Prudentiality
  • Quantile
  • Value at Risk

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