英文标题：
《Portfolio Risk Assessment using Copula Models》
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作者：
Mikhail Semenov, Daulet Smagulov
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最新提交年份：
2017
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英文摘要：
In the paper, we use and investigate copulas models to represent multivariate dependence in financial time series. We propose the algorithm of risk measure computation using copula models. Using the optimal mean-$CVaR$ portfolio we compute portfolio\'s Profit and Loss series and corresponded risk measures curves. Value-at-risk and Conditional-Value-at-risk curves were simulated by three copula models: full Gaussian, Student\'s $t$ and regular vine copula. These risk curves are lower than historical values of the risk measures curve. All three models have superior prediction ability than a usual empirical method. Further directions of research are described.
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中文摘要：
在本文中，我们使用并研究了copulas模型来表示金融时间序列中的多元依赖性。提出了基于copula模型的风险度量计算算法。利用最优均值-$CVaR$投资组合，我们计算了投资组合的损益序列和相应的风险度量曲线。风险价值和条件风险价值曲线由三个copula模型模拟：全高斯、Student\'s$t$和正则vine copula。这些风险曲线低于风险度量曲线的历史值。这三个模型都比通常的经验方法具有更好的预测能力。还描述了进一步的研究方向。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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