SFB649DP2015 052
lCARE - localizing Conditional AutoRegressive Expectiles
Xiu Xu
Andrija Mihoci
Wolfgang Karl Härdle
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
JEL Classification:
C32, C51, G17