TY - RPRT A1 - Xu, Xiu A1 - Mihoci, Andrija A1 - Härdle, Wolfgang Karl T1 - lCARE - localizing Conditional AutoRegressive Expectiles N2 - 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. KW - Expectiles KW - Tail Risk KW - Local Parametric Approach KW - Risk Management Y1 - 2015 UR - https://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2015-052.pdf PB - SFB 649 CY - Berlin ER -