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

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题名:	Assessing value at risk with CARE, the Conditional Autoregressive Expectile models
作者:	Kuan,CM;Yeh,JH;Hsu,YC
贡献者:	財務金融所
关键词:	REGRESSION QUANTILES;VOLATILITY;RETURNS
日期:	2009
上传时间:	2010-06-29 20:30:25 (UTC+8)
出版者:	中央大學
摘要:	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 theta 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. (c) 2008 Elsevier B.V. All rights reserved.
關聯:	JOURNAL OF ECONOMETRICS
显示于类别:	[財務金融研究所] 期刊論文

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