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Assessing Tail Risk Using Expectile Regressions with Partially Varying Coefficients

Zongwu Cai, Ying Fang and Dingshi Tian
Additional contact information
Zongwu Cai: Department of Economics, The University of Kansa
Ying Fang: The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, China
Dingshi Tian: The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, China

No 201804, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics

Abstract: To characterize heteroskedasticity and nonlinearity as well as asymmetry in tail risk, this paper investigates a class of conditional (dynamic) expectile models with partially varying coefficients in which some coefficients are allowed to be constants but others are allowed to be unknown functions of random variables. A three-stage estimation procedure is proposed to estimate both the parametric constant coefficients and nonparametric functional coefficients, and their asymptotic properties are investigated under time series context, together with a new simple and easily implemented test for testing the goodness of fit of models and a bandwidth selector based on newly defined cross-validatory estimation for the expected forecasting expectile errors. The proposed methodology is data-analytic and of sufficient flexibility to analyze complex and multivariate nonlinear structures without suffering from the curse of dimensionality. Finally, the proposed model is illustrated by simulated data and applied to analyzing the daily data of the S&P500 return series.

Keywords: Expectile; Heteroskedasticity; Nonlinearity; Varying Coefficients; Tail Risk (search for similar items in EconPapers)
JEL-codes: C58 C14 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2018-10, Revised 2018-10
References: View references in EconPapers View complete reference list from CitEc
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