A Higher-Order Hidden Markov Chain-Modulated Model for Asset Allocation

This paper presents an analysis of asset allocation strategies when the asset returns are governed by a discrete-time higher-order hidden Markov model (HOHMM), also called the weak hidden Markov model. We assume the drifts and volatilities of the asset returns switch over time according to the state of the HOHMM, in which the probability of the current state depends on the information from previous time-steps. The “switching” and “mixed” strategies are studied. We use a multivariate filtering technique in conjunction with the EM algorithm to obtain estimates of model parameter at a given time. This, in turn, aids investors in determining the optimal investment strategy for the next time step. Numerical implementation is applied to data on Russell 3000 value and growth indices. We benchmark the respective performances of portfolio using three classical investment measures.     

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.     

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 investment strategy under partial information

This paper considers a mean–variance portfolio selection problem under partial information, that is, the investor can observe the risky asset price with random drift which is not directly observable in financial markets. Since the dynamic mean–variance portfolio selection problem is time inconsistent, to seek the time-consistent investment strategy, the optimization problem is formulated and tackled in a game theoretic framework. Closed-form expressions of the equilibrium investment strategy and the corresponding equilibrium value function under partial information are derived by solving an extended Hamilton–Jacobi–Bellman system of equations. In addition, the results are also given under complete information, which are need for the partial information case. Furthermore, some numerical examples are presented to illustrate the derived equilibrium investment strategies and numerical sensitivity analysis is provided.     

考虑组合风险的指数化投资与随机线性二次最优控制

指数化投资使投资者享有市场平均收益水平,具有投资风险分散化、投资组合透明化、投资成本低廉等优势,日益受到投资者的亲睐。由于通常指数化投资者不愿意承担较大风险,本文考虑极小化跟踪误差与投资组合的风险之和(其中风险用风险资产的累积方差来衡量)。本文证明了无论是连续时间或离散时间、有限时区或无限时区的情形,在一定的条件下,最优控制都唯一存在,即利用随机线性二次最优控制进行指数化投资,最优投资策略都唯一存在。     

二层次消费与投资决策优化动态规划模型

在动态多阶段情形,投资者面临的环境不仅只有投资环境,还包括消费环境.投资者关于投资与消费的决策具有层次性.因为消费事关人的生存需要,是优先要考虑的问题,且投资的最终目的还是为了消费,所以使消费最大化应是高一层次的目标,而使投资最大化则应是次一级的目标.因此,试图建立一个二层次消费与投资决策优化动态规划模型,以便更好地模拟现实世界的情况.讨论了该模型的动态决策过程和最优解的性质.     

Loss aversion with multiple investment goals

This paper presents a time-continuous portfolio selection model with loss averse investors, who possess multiple investment goals at different time horizons. The model assumes partial narrow framing. Investors follow a two-step approach. First, they optimally allocate wealth among investment goals. Second, they determine an optimal investment strategy for each investment goal separately. We show that when loss aversion is according to the experimental findings, investors mainly invest their wealth to reach long-term goals and adopt investment strategies with high leverage to reach short-term goals. The overall strategy also display high leverage. The same patterns is observed when loss aversion is extreme and goals are very ambitious. By contrast, when loss aversion is extreme but goals are not too ambitious, investors mainly invest to reach short-term goals and adopt safe investment strategies for this purpose.     

Optimal consumption and investment strategies with partial and private information in a multi-asset setting

Information is crucial for decision makers. The more and the better information one possesses, the more qualified a decision one is able to make. In a framework with partial information about the expected returns of n stocks, the effect of having either non-anticipative or anticipative private information is studied. Optimal consumption and investment strategies for investors exhibiting constant relative risk aversion are derived analytically and an economically intuitive condition for an investor to be indifferent between the two types of private information is given. Moreover, a detailed numerical study including the value of private information is carried out to obtain a better economic understanding of the interplay between partial and private information. One novel result is that intertemporal consumption motives decrease the incentive of an investor to acquire private information.     

Explicit solutions of optimal consumption,investment and insurance problems with regime switching

We consider an investor who wants to select his optimal consumption, investment and insurance policies. Motivated by new insurance products, we allow not only the financial market but also the insurable loss to depend on the regime of the economy. The objective of the investor is to maximize his expected total discounted utility of consumption over an infinite time horizon. For the case of hyperbolic absolute risk aversion (HARA) utility functions, we obtain the first explicit solutions for simultaneous optimal consumption, investment, and insurance problems when there is regime switching. We determine that the optimal insurance contract is either no-insurance or deductible insurance, and calculate when it is optimal to buy insurance. The optimal policy depends strongly on the regime of the economy. Through an economic analysis, we calculate the advantage of buying insurance.     

Optimal mean–variance asset-liability management with stochastic interest rates and inflation risks

This paper considers an optimal asset-liability management problem with stochastic interest rates and inflation risks under the mean–variance framework. It is assumed that there are \(n+1\) assets available in the financial market, including a risk-free asset, a default-free zero-coupon bond, an inflation-indexed bond and \(n-2\) risky assets (stocks). Moreover, the liability of the investor is assumed to follow a geometric Brownian motion process. By using the stochastic dynamic programming principle and Hamilton–Jacobi–Bellman equation approach, we derive the efficient investment strategy and efficient frontier explicitly. Finally, we provide numerical examples to illustrate the effects of model parameters on the efficient investment strategy and efficient frontier.     

Filter‐based portfolio strategies in an HMM setting with varying correlation parametrizations

We consider portfolio optimization in a regime‐switching market. The assets of the portfolio are modeled through a hidden Markov model (HMM) in discrete time, where drift and volatility of the single assets are allowed to switch between different states. We consider different parametrizations of the involved asset covariances: statewise uncorrelated assets (though linked through the common Markov chain), assets correlated in a state‐independent way, and assets where the correlation varies from state to state. As a benchmark, we also consider a model without regime switches. We utilize a filter‐based expectation‐maximization (EM) algorithm to obtain optimal parameter estimates within this multivariate HMM and present parameter estimators in all three HMM settings. We discuss the impact of these different models on the performance of several portfolio strategies. Our findings show that for simulated returns, our strategies in many settings outperform naïve investment strategies, like the equal weights strategy. Information criteria can be used to detect the best model for estimation as well as for portfolio optimization. A second study using real data confirms these findings.     

含有信用风险的跳扩散市场的最优组合选择

假定投资者将其财富分配在这样两种风险资产中,一种是股票,价格服从跳跃扩散过程;一种是有信用风险的债券,其价格服从复合泊松过程.在均值-方差准则下通过最优控制原理来研究投资者的最优投资策略选择问题,得到了最优投资策略及有效边界,最后通过数值例子分析了违约强度、债券预期收益率以及目标财富对最优投资策略的影响.     

STAR-GARCH模型与股票市场投资策略非线性

本文首次基于投资者异质性和行为金融理论,从投资者风险偏好的角度度量市场情绪,应用STAR-GARCH模型,对我国股票市场投资策略的非线性转换进行了实证研究。研究结果表明:(1)我国股票市场投资策略表现出明显的两机制,说明我国股票市场存在显著的投资者异质性;(2)当市场情绪指数分别为0.06和0.046时,沪深股票投资策略处于中间机制,表现为偏向理性投资策略,而当市场情绪指数大于约0.6或小于约-0.5时,沪深股票投资策略处于外机制,表现为偏向噪音交易策略,在两种机制间,存在着投资策略的非线性转换;(3)当沪深股票投资策略处于中间机制时,股票市场日收益率平均波动更小,因而表现得更稳定。     

模型不确定和极端事件冲击下带通胀的最优投资组合选择问题研究

本文研究了投资者在极端事件冲击下带通胀的最优投资组合选择问题, 其中投资者不仅对损失风险是厌恶的而且对模型不确定也是厌恶的. 投资者在风险资产和无风险资产中进行投资. 首先, 利用Ito公式推导考虑通胀的消费篮子价格动力学方程, 其次由通胀折现的终端财富预期效用最大化, 对含糊厌恶投资者的最优期望效用进行刻画. 利用动态规划原理, 建立最优消费和投资策略所满足的HJB方程. 再次, 利用市场分解的方法解出HJB方程, 获得投资者最优消费和投资策略的显式解. 最后, 通过数值模拟, 分析了含糊厌恶、风险厌恶、跳和通胀因素对投资者最优资产配置策略的影响.     

Optimal Basket Liquidation for CARA Investors is Deterministic

Abstract

We consider the problem faced by an investor who must liquidate a given basket of assets over a finite time horizon. The investor's goal is to maximize the expected utility of the sales revenues over a class of adaptive strategies. We assume that the investor's utility has constant absolute risk aversion (CARA) and that the asset prices are given by a very general continuous-time, multiasset price impact model. Our main result is that (perhaps surprisingly) the investor does no worse if he narrows his search to deterministic strategies. In the case where the asset prices are given by an extension of the nonlinear price impact model of Almgren [(2003) Applied Mathematical Finance, 10, pp. 1–18], we characterize the unique optimal strategy via the solution of a Hamilton equation and the value function via a nonlinear partial differential equation with singular initial condition.     

投资者情绪指数及中国股市的实证

准确度量投资者情绪,有助于深刻理解市场,进行有效的监管和制定正确的投资策略.从重新界定投资者情绪的定义出发,对投资者情绪与当期收益的关系进行了理论演绎,总结出5条有关投资者情绪与当前收益关系的假说.依据可以获得的投资者情绪代理变量,利用主成分分析构建了中国证券市场投资者情绪指数,进而利用EGARCH模型实证检验了上述5条假说.实证结果表明,理论演绎与市场实际运行有很好的相合性.     

Precommitment and equilibrium investment strategies for defined contribution pension plans under a jump–diffusion model

In this paper, we study an optimal investment problem under the mean–variance criterion for defined contribution pension plans during the accumulation phase. To protect the rights of a plan member who dies before retirement, a clause on the return of premiums for the plan member is adopted. We assume that the manager of the pension plan is allowed to invest the premiums in a financial market, which consists of one risk-free asset and one risky asset whose price process is modeled by a jump–diffusion process. The precommitment strategy and the corresponding value function are obtained using the stochastic dynamic programming approach. Under the framework of game theory and the assumption that the manager’s risk aversion coefficient depends on the current wealth, the equilibrium strategy and the corresponding equilibrium value function are also derived. Our results show that with the same level of variance in the terminal wealth, the expected optimal terminal wealth under the precommitment strategy is greater than that under the equilibrium strategy with a constant risk aversion coefficient; the equilibrium strategy with a constant risk aversion coefficient is revealed to be different from that with a state-dependent risk aversion coefficient; and our results can also be degenerated to the results of He and Liang (2013b) and Björk et al. (2014). Finally, some numerical simulations are provided to illustrate our derived results.     

风险规避和最优套期保值比率

在一般的期望效用框架下,研究投资者的风险厌恶态度对于其套期保值策略的影响.首先,给出了投资者采用不同套期保值策略时,效用函数应该满足的条件;其次,讨论了期望效用框架下,Rubinstein整体风险厌恶度量与经典的Arrow Pratt局部风险厌恶度量和更强的Ross的风险度量之间的关系,提出了一组条件,使得在该组条件下,风险厌恶的人际间比较可以用Rubinstein整体风险厌恶度量来刻画;最后,在现货和期货服从正态分布的假设下,使用之前提出的条件,研究投资者风险厌恶程度对于其持有的最优套期保值比率的影响.     

基于文本信息考虑投资者情绪的均值回归策略设计——以东方财富股吧发帖文本和A股市场为例

在当前互联网时代下,越来越多的文本信息为人们所认识。借助机器学习等技术工具,目前已能较为便捷地从海量的文本数据中挖掘出与投资者行为、情绪有关的信息。基于此,本研究探讨了利用文本信息刻画投资者情绪,并对仅利用价格信息的均值回归策略进行改进。利用东方财富股吧中发帖内容等文本数据构建投资者情绪指标,结合非理性投资行为的特征,设计新的权重转移方程,得到新的均值回归策略。最后,利用部分沪深300成分股的价格数据和股吧文本数据进行实证检验,结果表明:相比于仅利用价格信息刻画均值回归特征的策略,本研究提出的考虑投资者情绪的策略有更好的收益表现。     

Optimal surrender strategies for equity-indexed annuity investors

An equity-indexed annuity (EIA) is a hybrid between a variable and a fixed annuity that allows the investor to participate in the stock market, and earn at least a minimum interest rate. The investor sacrifices some of the upside potential for the downside protection of the minimum guarantee. Because EIAs allow investors to participate in equity growth without the downside risk, their popularity has grown rapidly.An optimistic EIA owner might consider surrendering an EIA contract, paying a surrender charge, and investing the proceeds directly in the index to earn the full (versus reduced) index growth, while using a risk-free account for downside protection. Because of the popularity of these products, it is important for individuals and insurers to understand the optimal policyholder behavior.We consider an EIA investor who seeks the surrender strategy and post-surrender asset allocation strategy that maximizes the expected discounted utility of bequest. We formulate a variational inequality and a Hamilton-Jacobi-Bellman equation that govern the optimal surrender strategy and post-surrender asset allocation strategy, respectively. We examine the optimal strategies and how they are affected by the product features, model parameters, and mortality assumptions. We observe that in many cases, the “no-surrender” region is an interval (w_l,w_u); i.e., that there are two free boundaries. In these cases, the investor surrenders the EIA contract if the fund value becomes too high or too low. In other cases, there is only one free boundary; the lower (or upper) surrender threshold vanishes. In these cases, the investor holds the EIA, regardless of how low (or high) the fund value goes. For a special case, we prove a succinct and intuitive condition on the model parameters that dictates whether one or two free boundaries exist.