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Nonlinear expectile regression with application to Value-at-Risk and expected shortfall estimation

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  • Kim, Minjo
  • Lee, Sangyeol

Abstract

This paper considers nonlinear expectile regression models to estimate conditional expected shortfall (ES) and Value-at-Risk (VaR). In the literature, the asymmetric least squares (ALS) regression method has been widely used to estimate expectile regression models. However, no literatures rigorously investigated the asymptotic properties of the ALS estimates in nonlinear models with heteroscedasticity. Motivated by this aspect, this paper studies the consistency and asymptotic normality of the ALS estimates and conditional VaR and ES in those models. To illustrate, a simulation study and real data analysis are conducted.

Suggested Citation

  • Kim, Minjo & Lee, Sangyeol, 2016. "Nonlinear expectile regression with application to Value-at-Risk and expected shortfall estimation," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 1-19.
  • Handle: RePEc:eee:csdana:v:94:y:2016:i:c:p:1-19
    DOI: 10.1016/j.csda.2015.07.011
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    References listed on IDEAS

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    1. repec:eee:csdana:v:116:y:2017:i:c:p:49-66 is not listed on IDEAS
    2. repec:eee:ecmode:v:67:y:2017:i:c:p:355-367 is not listed on IDEAS

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