Transport infrastructure equities in mixed-asset portfolios: estimating risk with a Garch-Copula CVaR model

Transport infrastructure is an important subsector within infrastructure, but knowledge of its equities in terms of risk-return characteristics and contribution to portfolio performance is still limited. This study assesses the subsector individually and in a multi-asset, index-based portfolio. In doing so, we apply a t-Copula-based Conditional Value-at-Risk model to simulate risk and returns. Our findings reveal that the subsector has a relatively low dependency on other equities, performs like other alternative asset classes such as general real estate, and does not grant significant risk diversification benefits for mainstream institutional investors such as pension funds. Investors aiming for higher target returns may however assign substantial weights to transport infrastructure, supporting our conjecture that it does not share the same asset class characteristics as general infrastructure. By contrasting Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) scores for both the mean-variance framework and the t-Copula simulation, we further document the limitations of traditional VaR approaches. Hence, this study’s results support the use of risk assessment tools that incorporate non-normal distributions to represent multivariate dependence structures.     

组合投资决策的收益—风险分析框架

运用均值-方差、均值-VaR与均值-CVaR模型对上证综指、香港恒生指数、台湾加权指数、标准普尔指数和日经指数的收益-风险进行了实证分析。结果表明,在收益服从正态分布下,均值-方差、均值-VaR以及均值-CVaR模型能用一个模型统一表示,三个模型的边界方程也能用一个方程统一表示,三个模型的有效前沿存在子集关系。在收益服从正态分布下三个模型的最优组合投资权重是等价的;在任意分布下三个模型的最优组合投资权重不是等价的。     

长寿风险对基本医疗保险统筹基金的冲击效应研究——基于VaR与CVaR模型的综合测算

借鉴金融风险管理中VaR和CVaR模型对尾部风险的测量思路,通过构建有限数据Lee-Carter死亡率预测模型,测算了人口的长寿风险及其对基本医疗保险统筹基金的冲击效应,结果表明:2015-2060年基本医疗保险参保人群将面临巨大的长寿风险,极端情况下长寿风险将给统筹基金收支结余超预期下降造成不容忽视的尾部损失;推迟退休年龄、提高生育率、调整个人账户和报销比例、提高职工缴费工资和控制住院费用增长均可以在一定程度上缓解长寿风险的冲击.建议明确政府在基本医疗保险长寿风险管理中的主导作用,构建医疗、养老和长期护理保险的三险联动保障机制.     

企业债券组合优化模型实证分析

现代投资组合理论已有几十年的发展历史,近些年随着投资实践的要求和数学、计算技术的发展,投资组合理论也出现了一些新的分支和发展动向。本文从资产组合的角度出发,通过结构模型估计企业债券的未来价格,建立了投资损失函数,并利用CVaR风险度量方法构造了期望收益最大化的债券组合优化模型,最后利用我国债券市场数据进行了实证分析。     

金融风险度量方法的新进展

本文对风险度量的新进展进行了讨论,介绍了一些当前流行的风险度量工具,并从失真函数的角度考察风险度量,指出它们和传统方法的区别,同时对动态风险度量方法也进行了总结。     

VaR的改进方法CVaR在投资组合风险中的应用

VaR(在险价值)理论是当今国际上比较成熟的分析和度量风险的理论,在世界范围内得到广泛应用,但在对风险分布函数的度量中,却常常忽视对函数尾部特殊值的分析和计量,从而影响到风险评估的准确性。CVaR作为投资组合风险度量的指标克服了VaR的不足,是一致性的投资组合风险度量指标,具有很多优良特性。目前CVaR尚未成为金融业的一项公认标准,但因为它有厚实的理论根基和鲜明的可操作性,在投资组合风险控制方面比VaR更为有效合理、更适宜,同时建立CVaR模型可以降低风险,使资源合理配置。     

基于门限分位点回归的条件VaR风险度量

门限分位点回归模型是线性分位点回归模型的改进,是一种更加客观实际的非线性估计方法。利用该模型实证分析了单只股票(民生银行)的条件VaR。选择一种流动性风险指标作为条件,经过分析发现,由门限分位点回归模型得到结果能够更好地描述市场,也能更好地预测市场风险。     

CVaR HEDGING USING QUANTIZATION‐BASED STOCHASTIC APPROXIMATION ALGORITHM

In this paper, we investigate a method based on risk minimization to hedge observable but nontradable source of risk on financial or energy markets. The optimal portfolio strategy is obtained by minimizing dynamically the conditional value‐at‐risk (CVaR) using three main tools: a stochastic approximation algorithm, optimal quantization, and variance reduction techniques (importance sampling and linear control variable), as the quantities of interest are naturally related to rare events. As a first step, we investigate the problem of CVaR regression, which corresponds to a static portfolio strategy where the number of units of each tradable assets is fixed at time 0 and remains unchanged till maturity. We devise a stochastic approximation algorithm and study its a.s. convergence and weak convergence rate. Then, we extend our approach to the dynamic case under the assumption that the process modeling the nontradable source of risk and financial assets prices is Markovian. Finally, we illustrate our approach by considering several portfolios in connection with energy markets.     

如何用CVaR模型监测极端风险

金融活动在很大程度上是风险管理活动,一次次的灾难性事件提示着我们风险管理的重要性。本文通过实证检验,发现CVaR模型对历史上海外几次大级别股灾都有显著警示作用,同时计算了1999年至今的沪深指数VaR与CVaR时序,以及2006年至今的模拟股票投资组合VaR与CVaR时序,发现CVaR指标对极端事件的发生更为敏感,并且与VaR模型结合使用会起到更好的风险识别作用,从而及早提示黑天鹅的到来。