基于动态VaR约束与随机波动率模型的最优投资策略

研究Stein-Stein随机波动率模型下带动态VaR约束的最优投资组合选择问题. 假设投资者的目标是最大化终端财富的期望幂效用,可投资于无风险资产和一种风险资产, 风险资产的价格过程由Stein-Stein随机波动率模型刻画. 同时, 投资者期望能在投资过程中利用动态VaR约束控制所面对的风险.运用Bellman动态规划方法和Lagrange乘子法, 得到了该约束问题最优策略的解析式及特殊情形下最优值函数的解析式; 并通过理论分析和数值算例, 阐述了动态VaR约束与随机波动率对最优投资策略的影响.     

VaR约束下最优比例再保险和投资策略问题

研究了VaR动态约束下保险人的最优投资和再保险策略选择问题.假设保险人选择比例再保险来分散索赔风险,并通过银行存款和投资股票的手段来增加额外收益,其中股票价格满足Heston模型.保险人的目标是寻求使其终端财富的期望效用最大的最优策略.引入VaR约束条件并采用期望效用最大化为准则,运用随机控制理论建立具有VaR约束的随机控制问题,采用动态规划推导HJB方程,并利用Lagrange函数等方法得到指数效用下VaR约束有效和无效时的最优策略.另外,考虑了仅投资情形下的最优投资策略.最后通过仿真对最优策略进行敏感性分析.     

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.     

激励机制和VaR约束下的DC养老金计划的最优投资

研究了DC养老金经理在单一管理费以及混合收费(同时收取管理费与绩效费)这两种不同的薪酬机制和损失厌恶下的最优投资组合问题。利用凹化方法得到了存在终端财富约束下的最优财富过程和最优投资策略的解析表达式。数值结果表明损失厌恶,VaR约束和薪酬机制会极大地影响最优终端财富的分布。特别地,在决策参照点较高时,损失厌恶会导致混合薪酬机制下最优终端财富的尾部风险较低。     

破产控制约束下组合投资:两阶段MiniMax模型

以绝对偏差函数作为风险测度,考虑不允许卖空约束条件下基于MiniMax的多期证券组合选择问题。为了避免在投资周期内破产事件的发生,增加了风险控制约束。利用动态规划和拉格朗日乘子法,给出了两阶段MiniMax投资组合模型最优解析策略。本文所提出策略可以为需要同时资产管理和破产控制的投资者提供决策依据。     

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。     

离散约束条件下的实用投资组合选择模型

鉴于现实证券市场中的投资会受到很多类型的约束的限制,本文在同时综合反映多种市场摩擦与恰当度量投资风险的原则下,构建了两种分别以CVaR和双边一致性度量为风险度量的离散型多重约束实用投资组合选择模型。基于深圳证券交易所A股的日交易数据,我们从实证角度着重考虑了交易费用约束与逻辑约束对最优投资策略选择及其性能的影响,并给出了一些实用的投资建议。实证结果表明:新模型不仅可行、有效,而且能合理反映不同市场摩擦的作用。     

基于CaR风险约束的动态投资组合模型

研究了带有风险约束的动态投资组合优化问题.在Black-Scholes型金融市场下,引入了在险资本(Captical at risk,CaR)风险约束,与以往文献的风险约束仅仅施加于终端时点不同,该模型将风险约束施加于每一个交易区间.即利用条件信息不断地对风险进行重新评估,从而对投资决策连续地施加影响.利用动态规划技术和优化理论,在合理的假定下,从理论上对问题进行了分析,给出了最优投资策略的显式表达式,并与无风险约束情形进行了比较.最后给出了一些数值例子进行说明.     

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.     

跳-扩散风险模型的最优投资和再保险策略

本文对跳-扩散风险模型,在赔付进行比例再保险,以及盈余投资于无风险资产和风险资产的条件下,研究使得最终财富的指数期望效用最大的最优投资和比例再保险策略.得到最优投资策略和最优再保险策略,以及最大指数期望效用函数的显式表达式,发现最优策略和值函数都受到无风险利率的影响.最后通过数值计算,得到最优投资和比例再保险策略,以及值函数与模型各个参数之间的关系.     

Optimal portfolio strategies benchmarking the stock market

The paper investigates the impact of adding a shortfall risk constraint to the problem of a portfolio manager who wishes to maximize his utility from the portfolios terminal wealth. Since portfolio managers are often evaluated relative to benchmarks which depend on the stock market we capture risk management considerations by allowing a prespecified risk of falling short such a benchmark. This risk is measured by the expected loss in utility. Using the Black–Scholes model of a complete financial market and applying martingale methods, explicit analytic expressions for the optimal terminal wealth and the optimal portfolio strategies are given. Numerical examples illustrate the analytic results.     

Dynamic utility maximization with bounded shortfall risks

We address the dynamic portfolio optimization problem where the expected utility from terminal wealth has to be maximized. The special feature of this paper is an additional constraint on the portfolio strategy modeling bounded shortfall risks. We consider the risk, that the terminal wealth of the portfolio falls short of a certain benchmark. This benchmark is chosen to be proportional to the stock price. The risk is measured by the Expected Utility Loss. Using a continuous-time model of a complete financial market and applying martingale methods, analytic expressions for the optimal terminal wealth and the optimal portfolio strategies are given. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Data-Recursive Smoother Formulae for Partially Observed Discrete-Time Markov Chains

Abstract

We address a dynamic portfolio optimization problem where the expected utility from terminal wealth has to be maximized. The special feature of this paper is an additional constraint on the portfolio strategy modeling bounded shortfall risks, which are measured by value at risk or expected loss. Using a continuous-time model of a complete financial market and applying martingale methods, analytic expressions for the optimal terminal wealth and the optimal portfolio strategies are given. Finally, some numerical results are presented.     

考虑红利支付与提前退休的最优投资组合

研究了在经济代理人通过不可逆退休时间选择来调整劳动时间框架下的最优消费和投资问题,主要考虑风险资产派发红利的情形.运用随机控制方法,求解使得消费-闲暇预期效用最大化的最优策略.最优投资组合及最优退休时刻表明,代理人在为提前退休积累财富的同时,也能最佳享受消费和闲暇所带来的快乐.     

CIR框架下的投资组合效用微分博弈

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．     

Can long-run dynamic optimal strategies outperform fixed-mix portfolios? Evidence from multiple data sets

Using five alternative data sets and a range of specifications concerning the underlying linear predictability models, we study whether long-run dynamic optimizing portfolio strategies may actually outperform simpler benchmarks in out-of-sample tests. The dynamic portfolio problems are solved using a combination of dynamic programming and Monte Carlo methods. The benchmarks are represented by two typical fixed mix strategies: the celebrated equally-weighted portfolio and a myopic, Markowitz-style strategy that fails to account for any predictability in asset returns. Within a framework in which the investor maximizes expected HARA (constant relative risk aversion) utility in a frictionless market, our key finding is that there are enormous difference in optimal long-horizon (in-sample) weights between the mean–variance benchmark and the optimal dynamic weights. In out-of-sample comparisons, there is however no clear-cut, systematic, evidence that long-horizon dynamic strategies outperform naively diversified portfolios.     

Portfolio selection problem with multiple risky assets under the constant elasticity of variance model

This paper focuses on the constant elasticity of variance (CEV) model for studying the utility maximization portfolio selection problem with multiple risky assets and a risk-free asset. The Hamilton-Jacobi-Bellman (HJB) equation associated with the portfolio optimization problem is established. By applying a power transform and a variable change technique, we derive the explicit solution for the constant absolute risk aversion (CARA) utility function when the elasticity coefficient is −1 or 0. In order to obtain a general optimal strategy for all values of the elasticity coefficient, we propose a model with two risky assets and one risk-free asset and solve it under a given assumption. Furthermore, we analyze the properties of the optimal strategies and discuss the effects of market parameters on the optimal strategies. Finally, a numerical simulation is presented to illustrate the similarities and differences between the results of the two models proposed in this paper.     

Robust portfolios: contributions from operations research and finance

In this paper we provide a survey of recent contributions to robust portfolio strategies from operations research and finance to the theory of portfolio selection. Our survey covers results derived not only in terms of the standard mean-variance objective, but also in terms of two of the most popular risk measures, mean-VaR and mean-CVaR developed recently. In addition, we review optimal estimation methods and Bayesian robust approaches.     

BSDE approach to utility maximization with square-root factor processes

We consider the utility-based portfolio selection problem in a continuous-time setting. We assume the market price of risk depends on a stochastic factor that satisfies an affine-form, square-root, Markovian model. This financial market framework includes the classical geometric Brownian motion, CEV model, and Heston’s model as special cases. Adopting the BSDE approach, we obtain closed-form solutions for the optimal portfolio strategies and value functions for the logarithmic, power, and exponential utility functions.