英文标题：
《Regularizing Portfolio Risk Analysis: A Bayesian Approach》
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作者：
Sourish Das, Aritra Halder and Dipak K. Dey
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最新提交年份：
2015
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英文摘要：
  It is important for a portfolio manager to estimate and analyze recent portfolio volatility to keep the portfolio\'s risk within limit. Though the number of financial instruments in the portfolio can be very large, sometimes more than thousands, daily returns considered for analysis are only for a month or even less. In this case rank of portfolio covariance matrix is less than full, hence solution is not unique. It is typically known as the ``ill-posed\" problem. In this paper we discuss a Bayesian approach to regularize the problem. One of the additional advantages of this approach is to analyze the source of risk by estimating the probability of positive `conditional contribution to total risk\' (CCTR). Each source\'s CCTR would sum up to the portfolio\'s total volatility risk. Existing methods only estimate CCTR of a source, and does not estimate the probability of CCTR to be significantly greater (or less) than zero. This paper presents Bayesian methodology to do so. We use a parallelizable and easy to use Monte Carlo (MC) approach to achieve our objective. Estimation of various risk measures, such as Value at Risk and Expected Shortfall, becomes a by-product of this Monte-Carlo approach.
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中文摘要：
投资组合经理评估和分析最近的投资组合波动率，以将投资组合的风险控制在有限的范围内，这一点很重要。虽然投资组合中的金融工具数量可能非常多，有时超过数千种，但考虑用于分析的每日回报率仅为一个月甚至更少。在这种情况下，投资组合协方差矩阵的秩小于全秩，因此解不是唯一的。这通常被称为“不适定”问题。在本文中，我们讨论了一种规范化问题的贝叶斯方法。这种方法的另一个优点是通过估计“对总风险的条件贡献”（CCTR）的概率来分析风险源. 每个来源的CCTR总计为投资组合的总波动风险。现有方法仅估计源的CCTR，未估计CCTR显著大于（或小于）零的概率。本文介绍了贝叶斯方法。我们使用一种可并行化且易于使用的蒙特卡罗（MC）方法来实现我们的目标。各种风险度量的估计，如风险价值和预期缺口，成为这种蒙特卡罗方法的副产品。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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