论文标题
LCARE-定位有条件的自动回归期望
lCARE -- localizing Conditional AutoRegressive Expectiles
论文作者
论文摘要
我们考虑到有条件的基于预期的价值（EVAR）模型中的时变参数。与基于分位数的价值（QVAR）相比，EVAR下行风险对投资组合损失的程度更为敏感。本研究并没有将预期模型拟合到临时固定数据窗口上，而是通过使用局部参数方法来关注尾巴风险动态的参数不稳定性。我们的框架通过顺序测试在每个时间点产生数据驱动的最佳间隔长度。从2005 - 2016年开始的三个股市的经验证据表明，所选的长度约为每日观察的大约3-6个月。与一年固定间隔的模型相比，该方法的性能以及基于分位数的候选者在使用时间不变的投资组合保护（TIPP）策略的同时，对DAX，FTSE 100和S＆P 500投资组合使用。我们模型所隐含的尾巴风险度量最终为资产分配和投资组合保险提供了宝贵的见解。
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 utilising 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 3-6 months of daily observations. This method performs favorable 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.
