一个非对称线性风险函数与证券组合投资分析

在分析Jia&D yer的风险-价值理论基础上,给出了一个基于预先给定的目标收益的非对称线性风险函数.该风险函数是低于参考点的离差和高于参考点的离差的加权和,它利用一阶"上偏矩"来修正一阶下偏矩,进一步建立了在此非对称风险函数下的线性规划证券投资组合模型;并证明了该模型与二阶随机占优准则的一致性;最后通过上海证券市场的实际数据验证了该模型的有效性和实用性.     

与TSD准则一致的组合证券投资分析

基于预先给定的目标收益率,利用投资者对低于目标收益率的风险损失和高于目标收益率的风险报酬之间的权衡,给出了一些非对称风险度量模型,特别其中一种风险度量是低于参考点的方差和高于参考点的方差的加权和,它利用二阶上偏矩来修正二阶下偏矩,进一步建立了在该非对称风险度量下的组合投资优化模型,并证明了该模型在三阶随机占优的意义下是有效的.此外,还给出了其它3个模型与三阶随机占优准则是否一致的结论,并对所给出的几个组合证券投资模型的求解方法及其应用进行了分析.以上研究和分析为投资者在选择投资模型时避免盲目性、任意性提供了有益的决策参考.     

随机凸序与投资组合的风险值

本文利用随机凸序的理论证明了任意随机资产组合的风险不会超过其各个随机资产的风险值之和 ,即给出了投资组合的风险值上界 .指出了当投资者无法确定各随机资产的相依关系时 ,独立性假定会低投资组合的风险值 ;并分别针对正态资产、幂关系资产、指数资产给出了这种风险低估值的具体计算公式     

相依索赔下受VaR及政策约束的最优保险投资

在索赔风险两两拟渐近独立且正则变化尾的假定下,以VaR度量整体风险(承保风险和投资风险),兼顾政策约束,研究最优保险投资问题.以终期期望财富最大为目标,利用破产概率的渐近结果得到了近似的最优策略,并结合数值案例进行了模拟分析.结果表明:由于相依风险的复杂性,在最优策略的求解条件中,需明确限定索赔重尾指数;当保险公司合理设置风险水平时,最优策略可以最大化终期期望财富;在风险水平设置偏高时,监管比例可以有效地控制风险.     

投资组合保险CPPI策略研究

随着期权理论应用的发展,投资组合保险在国外已成为一种盛行的资产配置策略, 常数比例投资组合保险策略(CPPI)以其模型简单、参数的设置又能充分反映投资人不同的风险偏好、而且易于实施,成为大型安全型基金的基金经理首选的投资策略.本文研究并推广了CPPI策略,找出CPPI与期权的关系,讨论了借贷限制对(CPPI策略的影响,最后对CPPI策略在中国市场的可投资性进行了评测.     

投资组合风险的分散化研究

风险是金融投资领域的研究热点问题之下一,投资组合是降低投资风险的有效方法之一。人们在做出投资决策时总是追求在一定收益率下风险最小。本文论述了投资组合收益和风险的数学统计方法,阐明风险可分为系统风险和非系统风险,后者可以通过投资组合分散化。本文还探讨了证券相关性和组合风险之间的关系。最后作了实证分析。     

风险限额、交易费用与相依结构:对证券投资组合优化影响的实证研究

本文提出了一个考虑交易费用,允许以无风险利率自由借贷,追求期末财富最大化的投资组合选择优化模型。进一步,通过实证分析,研究了风险(VaR)限额约束、交易费率变动,以及投资组合间相依结构对证券组合优化配置的影响。     

关于相依风险投资组合风险价值的界

在正相依和分组相依的假设下,风险投资组合风险价值的上、下界被分析性地得到.所得到的结果实质性地将分组独立的设定推广到分组相依的情形.     

考虑Vasicek利率和相依风险的最优投资与再保险

本文研究了利率由Vasicek过程描述,两类保险业务具有相依风险的最优投资和再保险模型.盈余过程由扩散近似模型刻画, 保险人的目标是在给定期望终端财富的情况下,寻找使得终端财富的方差最小的投资和再保险策略.通过使用随机线性二次最优控制理论, 建立Hamilton-Jacobi-Bellman (HJB)方程,我们获得了值函数的精确表达式以及最优投资和再保险策略. 另外,我们给出了有效策略和有效前沿. 最后,通过数值例子说明了模型参数对最优投资和再保险策略的影响.     

最优投资组合和风险补偿

本文在M arkow itz均值-方差模型的基础上,引入风险补偿函数,研究了在投资组合中协方差、半协方差、负指数等目标函数之间的关系。     

Preserving the Rothschild–Stiglitz type increase in risk with background risk: A characterization

In order to generalize previous results by Li et al. (2016), Guo et al. (2016) extended the definition of the Rothschild–Stiglitz type of increase in risk to a background risk framework. They provided several sufficient conditions for such a ranking to hold, involving expectation dependence concepts. In this short note, the corresponding characterizations are established, based on the bivariate higher-degree increasing concave orders introduced by Denuit et al. (1999).     

几类随机序关系关于随机最大与随机最小的反向封闭性质

研究了右扩展序、TTT序、单调增凸序和单调增凹序分别关于随机最大与随机最小的反向封闭性质, 并讨论了相关年龄概念关于随机最大与随机最小的反向封闭性质.     

Portfolio insurance and synthetic securities

This paper aims to look at the situation of portfolio insurance, both from a theoretical and a practical standpoint, a decade after the methodology was first introduced by Leland and Rubinstein, and a few years after management strategies based on portfolio insurance were hurt by the crash of 1987. After a general introduction in Section 1, Section 2 presents a survey of different portfolio strategies and more specifically those related to portfolio insurance. Section 3 underlines the different issues raised by the market plunge of 1987, namely the problem of volatility misspecification, and studies the possible answers offered at this point by academics and practitioners. Section 4 contains some concluding comments.     

LIR序关系的一些封闭性质

证明了Location Independent Riskier Order随机序关系关于简单随机样本的极小值与可微递增凹变换的封闭性质，并讨论了Dilation序关系的一些矩不等式．     

Numerical methods for stochastic programs with second order dominance constraints with applications to portfolio optimization

Inspired by the successful applications of the stochastic optimization with second order stochastic dominance (SSD) model in portfolio optimization, we study new numerical methods for a general SSD model where the underlying functions are not necessarily linear. Specifically, we penalize the SSD constraints to the objective under Slater’s constraint qualification and then apply the well known stochastic approximation (SA) method and the level function method to solve the penalized problem. Both methods are iterative: the former requires to calculate an approximate subgradient of the objective function of the penalized problem at each iterate while the latter requires to calculate a subgradient. Under some moderate conditions, we show that w.p.1 the sequence of approximated solutions generated by the SA method converges to an optimal solution of the true problem. As for the level function method, the convergence is deterministic and in some cases we are able to estimate the number of iterations for a given precision. Both methods are applied to portfolio optimization problem where the return functions are not necessarily linear and some numerical test results are reported.     

Optimal allocation of policy limits and deductibles

In this paper, we study the problems of optimal allocation of policy limits and deductibles. Several objective functions are considered: maximizing the expected utility of wealth assuming the losses are independent, minimizing the expected total retained loss and maximizing the expected utility of wealth when the dependence structure is unknown. Orderings of the optimal allocations are obtained.