跳扩散市场投资组合研究

研究了连续时间动态均值-方差投资组合选择问题.假设风险资产价格服从跳跃-扩散过程且具有卖空约束.投资者的目标是在给定期望终止时刻财富条件下,最小化终止时刻财富的方差.通过求解模型相应的Hamilton-Jacobi-Bellmen方程,得到了最优投资策略及有效前沿的显示解.结果显示,风险资产的卖空约束及价格过程的跳跃因素对最优投资策略及有效前沿的是不可忽略的.     

基于均值-方差准则的保险公司最优投资策略

研究了保险公司在均值-方差准则下的最优投资问题,其中保险公司的盈余过程由带随机扰动的Cramer-Lundberg模型刻画,而且保险公司可将其盈余投资于无风险资产和一种风险资产.利用随机动态规划方法,通过求解相应的HJB方程,得到了均值方差模型的最优投资策略和有效前沿.最后,给出了数值算例说明扰动项对有效前沿的影响.     

基于基准过程的动态均值-方差最优投资组合选择

利用动态规划方法研究了基于基准过程的动态均值-方差最优投资组合问题,证明了识别定理,得到了剩余过程的均方最优投资策略和有效前沿.     

基于通货膨胀风险和Heston随机波动率下的最优资产配置

本文考虑了基于均值-方差准则下的连续时间投资组合选择问题。为了对冲市场中的利率风险和通货膨胀风险,假定市场上存在可供交易的零息名义债券和零息通货膨胀指数债券。另外,投资者还可以投资一个价格具有Heston随机波动率的风险资产。首先建立了基于均值-方差框架下的最优投资组合问题,然后将原问题进行转换,利用随机动态规划方法和对偶Lagrangian原理,获得了均值-方差准则下的有效投资策略以及有效前沿的解析表达形式,最后对相关参数的敏感性进行了分析。     

收益序列相关的动态资产-负债管理

以条件期望体现风险资产收益的相关性,建立了资产收益序列相关时资产-负债管理的动态均值-方差模型.采用Li和Ng(2000)的嵌入法,构造了一个具有二次效用函数的辅助问题,利用动态规划方法及原问题与辅助问题最优策略之间的关系,得到了原问题的最优投资组合策略和有效边界.     

共同基金和无风险资产投资的最优策略

利用均值-方差模型,分析了非线性交易成本下的共同基金与无风险资产投资组合的有效边界和在一般的效用函数下讨论了投资者的最优投资策略.     

推广的半绝对离差和动态投资组合选择

在标准的Black－Scholes型金融市场下，建立了以推广的半绝对离差（Extended Semi－Absolute Deviation；ESAD）度量风险的动态均值-ESAD投资组合选择模型，研究了模型的求解方法，得到了最优投资组合策略和均值-ESAD有效前沿的解析表达式．同时，与动态均值-方差模型作了比较分析．最后，结合实例说明了模型的求解方法．     

再保险-投资的M-V及M-VaR最优策略

考虑保险公司再保险-投资问题在均值-方差(M-V)模型和均值-在险价值(M-VaR)模型下的最优常数再调整策略.在保险公司盈余过程服从扩散过程的假设及多风险资产的Black-Scholes市场条件下,分别得到均值-方差模型和均值-在险价值模型下保险公司再保险-投资问题的最优常数再调整策略及共有效前沿,并就两种模型下的结...     

具有交易费用和马氏调制的均值-方差组合选择

研究了均值-方差准则下,最优投资组合选择问题.投资者为了增加财富它可以在金融市场上投资.金融市场由一个无风险资产和n个带跳的风险资产组成,并假设金融市场具有马氏调制,买卖风险资产时,考虑交易费用.目标是,在终值财富的均值等于d的限制下,使终值财富的方差最小,即均值-方差组合选择问题.应用随机控制的理论解决该问题,获得了最优的投资策略和有效边界.     

借贷利率限制下资产-负债管理问题的均值-方差模型

将负债过程和借款利率限制引入投资组合优化问题中,并建立该问题的均值-方差模型.通过引入拉格朗日函数并应用拉格朗日对偶定理得到一个等价的新的优化模型,然后应用动态规划原理得到了最优投资策略和有效前沿的解析表达式.算例解释了所得结论.     

证券市场上含有多个基金时的最优投资策略

本文研究了证券市场中包含多个基金和股票时的均值-方差最优投资决策模型,得到了最优投资组合的解析表达形式,以及对应的投资有效前沿,证明了两基金分离问题,由于最优解是不唯一的,进而讨论了最优解集合的结构,并对实例进行计算与分析。     

Discrete time mean-variance analysis with singular second moment matrixes and an exogenous liability

We apply the dynamic programming methods to compute the analytical solution of the dynamic mean-variance optimization problem affected by an exogenous liability in a multi-periods market model with singular second moment matrixes of the return vector of assets. We use orthogonai transformations to overcome the difficulty produced by those singular matrixes, and the analytical form of the efficient frontier is obtained. As an application, the explicit form of the optimal mean-variance hedging strategy is also obtained for our model.     

A note on the dynamic liquidity trading problem with a mean-variance objective

This note studies the dynamic liquidity trader’s problem with a mean-variance objective function. Independent of the market impact functions and the market price dynamics, we provide a necessary and sufficient condition under which the dynamic programming equation (Bellman equation) can be extended to mean-variance objectives. Evaluation of this condition involves solving an optimization problem and taking variance of its optimal value. This computation may be difficult even when random disturbances in the market price dynamics follow a well-known distribution. To avoid this pitfall, we then provide some sufficient condition which can be assessed very easily.     

Performances of socially responsible investment and environmentally friendly funds

The socially responsible investment (SRI) funds performances remain inconclusive. Hence, more studies need to be conducted to determine if SRI funds systematically underperform or outperform conventional funds. This paper has employed dynamic mean-variance model using shortage function approach to evaluate the performance of SRI and Environmentally friendly funds (EF). Unlike the traditional methods, this approach estimates fund performance considering both the return and risk at the same time. The empirical results show that SRI funds outperformed conventional funds in EU and US. In addition, the results of EU are among the top-performing categories. EF do not perform as well as SRI, but perform in manners equal or superior to conventional funds. These results show statistically significant in some cases.     

Better than pre-committed optimal mean-variance policy in a jump diffusion market

Dynamic mean-variance investment model can not be solved by dynamic programming directly due to the nonseparable structure of variance minimization problem. Instead of adopting embedding scheme, Lagrangian duality approach or mean-variance hedging approach, we transfer the model into mean field mean-variance formulation and derive the explicit pre-committed optimal mean-variance policy in a jump diffusion market. Similar to multi-period setting, the pre-committed optimal mean-variance policy is not time consistent in efficiency. When the wealth level of the investor exceeds some pre-given level, following pre-committed optimal mean-variance policy leads to irrational investment behaviors. Thus, we propose a semi-self-financing revised policy, in which the investor is allowed to withdraw partial of his wealth out of the market. And show the revised policy has a better investment performance in the sense of achieving the same mean-variance pair as pre-committed policy and receiving a nonnegative free cash flow stream.     

Portfolio optimization in stochastic markets

We consider a multiperiod mean-variance model where the model parameters change according to a stochastic market. The mean vector and covariance matrix of the random returns of risky assets all depend on the state of the market during any period where the market process is assumed to follow a Markov chain. Dynamic programming is used to solve an auxiliary problem which, in turn, gives the efficient frontier of the mean-variance formulation. An explicit expression is obtained for the efficient frontier and an illustrative example is given to demonstrate the application of the procedure.     

Classical mean-variance model revisited: pseudo efficiency

Investigating the inverse problem of the classical Markowitz mean-variance formulation: Given a mean-variance pair, find initial investment levels and their corresponding portfolio policies such that the given mean-variance pair can be realized, we reveal that any mean-variance pair inside the reachable region can be achieved by multiple portfolio policies associated with different initial investment levels. Therefore, in the mean-variance world for a market of all risky assets, the common belief of monotonicity: ‘The larger you invest, the larger expected future wealth you can expect for a given risk (variance) level’ does not hold, which stimulates us to extend the classical two-objective mean-variance framework to an expanded three-objective framework: to maximize the mean and minimize the variance of the final wealth as well as to minimize the initial investment level. As a result, we eliminate from the policy candidate list the set of pseudo efficient policies that are efficient in the original mean-variance space, but inefficient in this newly introduced three-dimensional objective space.