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arXiv · 2009.13215

lCARE -- localizing Conditional AutoRegressive Expectiles

Xiu Xu·Andrija Mihoci·Wolfgang Karl Härdle
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Abstract

We account for time-varying parameters in the conditional expectile-based value at risk (EVaR) model. The EVaR downside risk is more sensitive to the magnitude of portfolio losses compared to the quantile-based value at risk (QVaR). Rather than fitting the expectile models over ad-hoc fixed data windows, this study focuses on parameter instability of tail risk dynamics by utilising a local parametric approach. Our framework yields a data-driven optimal interval length at each time point by a sequential test. Empirical evidence at three stock markets from 2005-2016 shows that the selected lengths account for approximately 3-6 months of daily observations. This method performs favorable compared to the models with one-year fixed intervals, as well as quantile based candidates while employing a time invariant portfolio protection (TIPP) strategy for the DAX, FTSE 100 and S&P 500 portfolios. The tail risk measure implied by our model finally provides valuable insights for asset allocation and portfolio insurance.

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Xiu Xu, Andrija Mihoci, Wolfgang Karl Härdle. 2020-09-28. lCARE -- localizing Conditional AutoRegressive Expectiles. https://doi.org/10.1016/j.jempfin.2018.06.006

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