论文标题
在重尾时间序列中通过预期的尾巴风险推断
Tail risk inference via expectiles in heavy-tailed time series
论文作者
论文摘要
Expectiles定义了唯一的不变，连贯和可观的风险措施，除了期望之外。基于预期的风险措施的普及正在稳步增长，并且已经研究了它们的独立数据，但是需要进一步的结果来使用具有依赖时间序列（例如财务数据）的极端期望。在本文中，我们在一般的$β$混合环境中为极端预期和基于期望的边际预期短缺建立了基础，该环境涵盖了ARMA，ARCH和GARCH模型具有重型创新。财务收益的模拟和应用程序表明，当数据取决于数据时，新的估计器和置信区间会大大改善现有的估计间隔。
Expectiles define the only law-invariant, coherent and elicitable risk measure apart from the expectation. The popularity of expectile-based risk measures is steadily growing and their properties have been studied for independent data, but further results are needed to use extreme expectiles with dependent time series such as financial data. In this paper we establish a basis for inference on extreme expectiles and expectile-based marginal expected shortfall in a general $β$-mixing context that encompasses ARMA, ARCH and GARCH models with heavy-tailed innovations. Simulations and applications to financial returns show that the new estimators and confidence intervals greatly improve on existing ones when the data are dependent.
