英文标题：
《Efficient Randomized Quasi-Monte Carlo Methods For Portfolio Market Risk》
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作者：
Halis Sak and \\.Ismail Ba\\c{s}o\\u{g}lu
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最新提交年份：
2015
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英文摘要：
  We consider the problem of simulating loss probabilities and conditional excesses for linear asset portfolios under the t-copula model. Although in the literature on market risk management there are papers proposing efficient variance reduction methods for Monte Carlo simulation of portfolio market risk, there is no paper discussing combining the randomized quasi-Monte Carlo method with variance reduction techniques. In this paper, we combine the randomized quasi-Monte Carlo method with importance sampling and stratified importance sampling. Numerical results for realistic portfolio examples suggest that replacing pseudorandom numbers (Monte Carlo) with quasi-random sequences (quasi-Monte Carlo) in the simulations increases the robustness of the estimates once we reduce the effective dimension and the non-smoothness of the integrands.
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中文摘要：
我们考虑t-copula模型下线性资产组合的损失概率和条件超额的模拟问题。虽然在有关市场风险管理的文献中，有论文提出了有效的方差缩减方法，用于投资组合市场风险的蒙特卡罗模拟，但没有论文讨论将随机拟蒙特卡罗方法与方差缩减技术相结合。本文将随机拟蒙特卡罗方法与重要性抽样和分层重要性抽样相结合。实际投资组合实例的数值结果表明，在模拟中用准随机序列（准蒙特卡罗）代替伪随机数（蒙特卡罗）可以在降低被积函数的有效维数和非光滑性后提高估计的稳健性。
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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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