奈特不确定下考虑红利和机制转换的最优消费投资

分析了在奈特不确定性环境下,股票的预期回报率服从Markov链的跨期消费和资产选择问题.首先,对由风险资产预期回报构成的不可观测状态下的隐Marbv状态转换模型做出了刻画,使人们对感性的“不可观测状态”的实际金融市场到其精确的数学模型表达有一个清晰的认识.其次,在连续时间风险模型下,假设具有递归多先验效用的投资者拥有一个不可观测的投资机会的先验集,借助Malliavin导数和随机积分方程求解投资者最优消费和投资策略的显式表达式.通过数值模拟分析时,发现不完备信息下的连续Bayes修正产生了能够削减跨期对冲需求的含糊对冲需求,含糊厌恶增大了最优投资组合策略中对冲需求的重要性.讨论了当市场上出现红利因素,上述最优投资组合结论将会发生何种变化,并对红利因素进行具体的量化,定量地研究不同大小的红利对最优投资组合的影响.最后,利用Monte Carlo Malliavin导数模拟计算法分别说明了考虑含糊情形下最优股票需求和跨期对冲需求的变化趋势,且考虑在股票是否考虑支付红利的情况下对投资的影响.     

不完全信息和模糊厌恶下DC型养老金最优投资策略

在信息部分可观测的金融市场中,参与者可投资于一个无风险资产、一个滚动债券和一支股票。其中,股票的预期收益率由一个服从均值-回复过程的预测因子预测。参与者是模糊厌恶的,只能观测到股票价格和利率,却无法观测到预测因子。利用滤波技术和动态规划原理,得到了不完全信息和模糊厌恶下DC型养老金最优投资策略的解析式。进一步,利用敏感性分析和比较静态分析,对比仅考虑不完全信息、仅考虑模糊厌恶以及同时考虑不完全信息和模糊厌恶三种情形下的最优投资策略。结果表明同时考虑不完全信息和模糊厌恶时的最优投资策略最保守,仅考虑不完全信息时的最优投资策略对风险厌恶系数的变化最敏感。     

不完全市场下基于对数效用的动态投资组合

应用鞅方法研究不完全市场下的动态投资组合优化问题。首先,通过降低布朗运动的维数将不完全金融市场转化为完全金融市场,并在转化后的完全金融市场里应用鞅方法研究对数效用函数下的动态投资组合问题,得到了最优投资策略的显示表达式。然后,根据转化后的完全金融市场与原不完全金融市场之间的参数关系,得到原不完全金融市场下的最优投资策略。算例分析比较了不完全金融市场与转化后的完全金融市场下最优投资策略的变化趋势,并与幂效用、指数效用下最优投资策略的变化趋势做了比较。     

基于下方风险控制的动态投资组合优化研究

研究了跳扩散结构下带有下方风险控制的动态投资组合优化问题.基于投资组合中每一种资产的收益率观测序列,模型在不断变化的数据窗口下把组合比例看作向量值随机过程,利用马尔可夫链蒙特卡罗模拟方法得到随时间变化的动态投资组合最优配置,这样可以根据市场信息的变化及时做出策略调整,既达到了预期收益目标又控制了风险,使得组合投资更切实际.通过实例分析可以看出,该方法相对传统方法更行之有效而且操作简便.     

基于随机基准的最优投资组合选择问题研究

本文研究基于随机基准的最优投资组合选择问题.假设投资者可以投资于一种无风险资产和一种风险股票,并且选择某一基准作为目标.基准是随机的,并且与风险股票相关.投资者选择最优的投资组合策略使得终端期望绝对财富和基于基准的相对财富效用最大.首先,利用动态规划原理建立相应的HJB方程,并在幂效用函数下,得到最优投资组合策略和值函数的显示表达式.然后,分析相对业绩对投资者最优投资组合策略和值函数的影响.最后,通过数值计算给出了最优投资组合策略和效用损益与模型主要参数之间的关系.     

状态相依效用下的超额损失再保险——投资策略

假设保险公司的盈余过程和金融市场的资产价格过程均由可观测的连续时间马尔科夫链所调节, 以最大化终端财富的状态相依的期望指数效用为目标, 研究了保险公司的超额损失再保险-投资问题. 运用动态规划方法, 得到最优再保险-投资策略的解析解以及最优值函数的半解析式. 最后, 通过数值例子, 分析了模型各参数对最优值函数和最优策略的影响.     

不完全市场下考虑损失厌恶的连续时间投资组合选择

在不完全市场条件下研究了一般情形下的损失厌恶投资者的连续时间投资组合选择模型. 面对市场风险, 投资者的偏好由一个S-型的价值函数定义. 通过把不完全市场转换为完全市场, 利用鞅方法和复制技术, 分别获得了投资者的最优期末财富以及最优投资策略. 最后讨论了一个分段幂函数的例子, 在模型系数为确定的常数情形下, 得到了最优解的显示表达式.     

CIR框架下的投资组合效用微分博弈(英文)

建立了Cox-Ingersoll-Ross随机利率下的关于两个投资者的投资组合效用微分博弈模型.市场利率具有CIR动力,博弈双方存在唯一的损益函数,损益函数取决于投资者的投资组合财富.一方选择动态投资组合策略以最大化损益函数,而另一方则最小化损益函数.运用随机控制理论,在一般的效用函数下得到了基于效用的博弈双方的最优策略.特别考虑了常数相对风险厌恶情形,获得了显示的最优投资组合策略和博弈值.最后给出了数值例子和仿真结果以说明本文的结论.     

部分信息下带有保费返还条款的DC养老金时间一致性投资策略

本文研究了部分信息下带有保费返还条款的DC养老金的时间一致性投资策略.假设养老金管理者只拥有股票的部分信息,即只能观测到股票的价格,而不能观测到股票的收益率.养老金带有保费返还条款,在基金累积期死亡的参与者可以获得前期缴纳的所有保费.此外,本文还考虑了通胀风险以及随机工资.首先,利用卡尔曼滤波理论,将部分信息情形下的最优投资组合问题转化为一个完全信息情形下的问题.然后,通过求解一个扩展的HJB方程,得到时间一致性投资策略和最优值函数,并给出了均值–方差有效前沿的参数表达式.最后,用蒙特卡洛方法进行数值模拟,分析了部分信息和保费返还条款对股票投资比例和有效前沿的影响,并给出了相应的经济学解释.     

Heston随机波动率市场中带VaR约束的最优投资策略

本文研究了Heston随机波动率市场下, 基于VaR约束下的动态最优投资组合问题。
假设Heston随机波动率市场由一个无风险资产和一个风险资产构成,投资者的目标为最大化其终端的期望效用。与此同时, 投资者将动态地评估其待选的投资组合的VaR风险,并将其控制在一个可接受的范围之内。本文在合理的假设下,使用动态规划的方法,来求解该问题的最优投资策略。在特定的参数范围内,利用数值方法计算出近似的最优投资策略和相应值函数, 并对结果进行了分析。     

Multi‐period mean variance portfolio selection under incomplete information

This paper solves an optimal portfolio selection problem in the discrete‐time setting where the states of the financial market cannot be completely observed, which breaks the common assumption that the states of the financial market are fully observable. The dynamics of the unobservable market state is formulated by a hidden Markov chain, and the return of the risky asset is modulated by the unobservable market state. Based on the observed information up to the decision moment, an investor wants to find the optimal multi‐period investment strategy to maximize the mean‐variance utility of the terminal wealth. By adopting a sufficient statistic, the portfolio optimization problem with incompletely observable information is converted into the one with completely observable information. The optimal investment strategy is derived by using the dynamic programming approach and the embedding technique, and the efficient frontier is also presented. Compared with the case when the market state can be completely observed, we find that the unobservable market state does decrease the investment value on the risky asset in average. Finally, numerical results illustrate the impact of the unobservable market state on the efficient frontier, the optimal investment strategy and the Sharpe ratio. Copyright © 2016 John Wiley & Sons, Ltd.     

Time-consistent reinsurance and investment strategies for mean–variance insurer under partial information

In this paper, based on equilibrium control law proposed by Björk and Murgoci (2010), we study an optimal investment and reinsurance problem under partial information for insurer with mean–variance utility, where insurer’s risk aversion varies over time. Instead of treating this time-inconsistent problem as pre-committed, we aim to find time-consistent equilibrium strategy within a game theoretic framework. In particular, proportional reinsurance, acquiring new business, investing in financial market are available in the market. The surplus process of insurer is depicted by classical Lundberg model, and the financial market consists of one risk free asset and one risky asset with unobservable Markov-modulated regime switching drift process. By using reduction technique and solving a generalized extended HJB equation, we derive closed-form time-consistent investment–reinsurance strategy and corresponding value function. Moreover, we compare results under partial information with optimal investment–reinsurance strategy when Markov chain is observable. Finally, some numerical illustrations and sensitivity analysis are provided.     

部分信息下均值-方差准则下的投资组合问题研究

研究了部分信息下,投资组合效用最大化的问题.在风险资产(股票)价格满足跳扩散过程,对同时该过程中的系数受马尔科夫调制参数的影响.通过运用非线性滤波技术,将部分信息的问题转化完全信息的问题.并运用随机优化与倒向随机微分方程得到在均值-方差准则的最优投资策略.     

Multiperiod portfolio optimization models in stochastic markets using the mean–variance approach

We consider several multiperiod portfolio optimization models where the market consists of a riskless asset and several risky assets. The returns in any period are random with a mean vector and a covariance matrix that depend on the prevailing economic conditions in the market during that period. An important feature of our model is that the stochastic evolution of the market is described by a Markov chain with perfectly observable states. Various models involving the safety-first approach, coefficient of variation and quadratic utility functions are considered where the objective functions depend only on the mean and the variance of the final wealth. An auxiliary problem that generates the same efficient frontier as our formulations is solved using dynamic programming to identify optimal portfolio management policies for each problem. Illustrative cases are presented to demonstrate the solution procedure with an interpretation of the optimal policies.     

Optimal reinsurance and investment with unobservable claim size and intensity

We consider the optimal reinsurance and investment problem in an unobservable Markov-modulated compound Poisson risk model, where the intensity and jump size distribution are not known but have to be inferred from the observations of claim arrivals. Using a recently developed result from filtering theory, we reduce the partially observable control problem to an equivalent problem with complete observations. Then using stochastic control theory, we get the closed form expressions of the optimal strategies which maximize the expected exponential utility of terminal wealth. In particular, we investigate the effect of the safety loading and the unobservable factors on the optimal reinsurance strategies. With the help of a generalized Hamilton–Jacobi–Bellman equation where the derivative is replaced by Clarke’s generalized gradient as in Bäuerle and Rieder (2007), we characterize the value function, which helps us verify that the strategies we constructed are optimal.     

HARA frontiers of optimal portfolios in stochastic markets

In this paper, we consider the optimal portfolio selection problem in continuous-time settings where the investor maximizes the expected utility of the terminal wealth in a stochastic market. The utility function has the structure of the HARA family and the market states change according to a Markov process. The states of the market describe the prevailing economic, financial, social and other conditions that affect the deterministic and probabilistic parameters of the model. This includes the distributions of the random asset returns as well as the utility function. We analyzed Black–Scholes type continuous-time models where the market parameters are driven by Markov processes. The Markov process that affects the state of the market is independent of the underlying Brownian motion that drives the stock prices. The problem of maximizing the expected utility of the terminal wealth is investigated and solved by stochastic optimal control methods for exponential, logarithmic and power utility functions. We found explicit solutions for optimal policy and the associated value functions. We also constructed the optimal wealth process explicitly and discussed some of its properties. In particular, it is shown that the optimal policy provides linear frontiers.     

Optimal liquidation under partial information with price impact

We study the optimal liquidation problem in a market model where the bid price follows a geometric pure jump process whose local characteristics are driven by an unobservable finite-state Markov chain and by the liquidation rate. This model is consistent with stylized facts of high frequency data such as the discrete nature of tick data and the clustering in the order flow. We include both temporary and permanent effects into our analysis. We use stochastic filtering to reduce the optimal liquidation problem to an equivalent optimization problem under complete information. This leads to a stochastic control problem for piecewise deterministic Markov processes (PDMPs). We carry out a detailed mathematical analysis of this problem. In particular, we derive the optimality equation for the value function, we characterize the value function as continuous viscosity solution of the associated dynamic programming equation, and we prove a novel comparison result. The paper concludes with numerical results illustrating the impact of partial information and price impact on the value function and on the optimal liquidation rate.     

Portfolio selection under incomplete information

We study an optimal investment problem under incomplete information and power utility. We analytically solve the Bellman equation, and identify the optimal portfolio policy. Moreover, we compare the solution to the value function in the fully observable case, and quantify the loss of utility due to incomplete information.