lCARE - localizing conditional autoregressive expectiles
Andrija Mihoci and
Wolfgang Härdle ()
Journal of Empirical Finance, 2018, vol. 48, issue C, 198-220
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 utilizing 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 4–6 months of daily observations. This method performs favourable 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.
Keywords: Expectile; Tail risk; Local parametric approach; Risk management (search for similar items in EconPapers)
JEL-codes: C32 C51 G17 (search for similar items in EconPapers)
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Working Paper: lCARE – localizing Conditional AutoRegressive Expectiles
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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:48:y:2018:i:c:p:198-220
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