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lCARE – localizing Conditional AutoRegressive Expectiles

Xiu Xu, Andrija Mihoci () and Wolfgang Härdle ()

No SFB649DP2015-052, SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649

Abstract: We account for time-varying parameters in the conditional expectile based value at risk (EVaR) model. EVaR appears more sensitive to the magnitude of portfolio losses compared to the quantile-based Value at Risk (QVaR), nevertheless, by fitting the models over relatively long ad-hoc fixed time intervals, research ignores the potential time-varying parameter properties. Our work focuses on this issue by exploiting the local parametric approach in quantifying tail risk dynamics. By achieving a balance between parameter variability and modelling bias, one can safely fit a parametric expectile model over a stable interval of homogeneity. Empirical evidence at three stock markets from 2005- 2014 shows that the parameter homogeneity interval lengths account for approximately 1-6 months of daily observations. Our method outperforms models with one-year fixed intervals, as well as quantile based candidates while employing a time invariant portfolio protection (TIPP) strategy for the DAX portfolio. The tail risk measure implied by our model finally provides valuable insights for asset allocation and portfolio insurance.

Keywords: expectiles; tail risk; local parametric approach; risk management (search for similar items in EconPapers)
JEL-codes: C32 C51 G17 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
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Journal Article: lCARE - localizing conditional autoregressive expectiles (2018) Downloads
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