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  • 统计与管理学院2017年学术报告第3

     

    【主  题】lCARE - localizing Conditional AutoRegressive Expectiles

    【报告人】 Xiu Xu, Ph.D. candidate

    Humboldt-Universität zu Berlin.

    【时  间】 2017年03月13日(星期一)08:30-09:30

    【地  点】 上海财经大学统计与管理学院大楼1208室

    【摘  要】The expectile-based Value at Risk (EVaR) is a downside risk measure, which is more sensitive to the magnitude of portfolio losses compared to the conventional quantile-based Value at Risk (QVaR). Unlike most of the existing researches which fit the models over relatively long ad-hoc fixed time intervals, this research focuses on the issue of potential time-varying parameter properties by exploiting the local parametric approach in quantifying tail risk dynamics. Through achieving a balance between parameter variability and modelling bias, one can safely fit a parametric expectile model over a stable interval of homogeneity. Empirical evidences at three stock markets from 2005- 2014 show that the interval lengths with parameter homogeneity account for approximately 1-6 months of daily observations. Our method performs favorable compared to the models with one-year fixed intervals, as well as quantile based candidates when 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.

    【邀请人】 冯兴东