Value‐at‐Risk bounds with two‐sided dependence information

Value‐at‐Risk bounds with two‐sided dependence information
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具有两侧依赖性信息的风险价值界限

DOI:
10.1111/mafi.12192
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发表时间:
2018
影响因子:
1.6
通讯作者:
L. Rüschendorf
L. Rüschendorf
中科院分区:
经济学2区
文献类型:
--
作者:
T. Lux;L. Rüschendorf

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在文献中,除了单个风险因子的边际分布之外,还可以获得风险的联合分布或copula的单侧边界,从而推导出聚合风险的风险价值(VaR)边界。在实际应用中,这些改进的标准界往往太宽,与实际应用无关,特别是当风险因素的数量很大或相关性约束不够强时。在本文中,我们开发了一种方法来计算风险值的边界时,除了风险因素的边际分布,双边依赖信息的形式上和下边界的风险因素的Copula。该方法是基于松弛的确切的对偶界限,我们通过Monge-Kantorovich运输对偶。在几个应用中,我们说明了双边依赖信息通常会导致聚合的VaR上的边界大大改善。
Value‐at‐Risk (VaR) bounds for aggregated risks have been derived in the literature in settings where, besides the marginal distributions of the individual risk factors, one‐sided bounds for the joint distribution or the copula of the risks are available. In applications, it turns out that these improved standard bounds on VaR tend to be too wide to be relevant for practical applications, especially when the number of risk factors is large or when the dependence restriction is not strong enough. In this paper, we develop a method to compute VaR bounds when besides the marginal distributions of the risk factors, two‐sided dependence information in form of an upper and a lower bound on the copula of the risk factors is available. The method is based on a relaxation of the exact dual bounds that we derive by means of the Monge–Kantorovich transportation duality. In several applications, we illustrate that two‐sided dependence information typically leads to strongly improved bounds on the VaR of aggregations.
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