论文标题
聚集风险模型的依赖性有条件价值风险
Dependent Conditional Value-at-Risk for Aggregate Risk Models
论文作者
论文摘要
已经开发了风险措施预测和模型，不仅为了提供更好的预测，而且还保留其（经验）特性尤其是连贯的财产。虽然广泛使用的风险价值风险度量（VAR）在许多应用中表现出了其性能和收益，但实际上并不是一项连贯的风险措施。有条件的VAR（COVAR）定义为超出VAR的损失的平均值，是满足相干性质的替代风险度量之一。有几个Covar扩展，例如改装Covar（McOvar）和Copula Covar（Ccovar）。在本文中，我们提出了另一种称为依赖Covar（DCOVAR）的风险措施，因为目标损失取决于另一个随机损失，包括被视为随机损失的模型参数。发现我们的DCOVAR的表现都比McOvar和Ccovar都胜过。进行数值模拟以说明所提出的DCOVAR。此外，我们对财务收益数据进行了实证研究，以计算异质流程过程的DCOVAR预测。
Risk measure forecast and model have been developed in order to not only provide better forecast but also preserve its (empirical) property especially coherent property. Whilst the widely used risk measure of Value-at-Risk (VaR) has shown its performance and benefit in many applications, it is in fact not a coherent risk measure. Conditional VaR (CoVaR), defined as mean of losses beyond VaR, is one of alternative risk measures that satisfies coherent property. There has been several extensions of CoVaR such as Modified CoVaR (MCoVaR) and Copula CoVaR (CCoVaR). In this paper, we propose another risk measure, called Dependent CoVaR (DCoVaR), for a target loss that depends on another random loss, including model parameter treated as random loss. It is found that our DCoVaR outperforms than both MCoVaR and CCoVaR. Numerical simulation is carried out to illustrate the proposed DCoVaR. In addition, we do an empirical study of financial returns data to compute the DCoVaR forecast for heteroscedastic process.
