Portfolio Choice under the Mean-Variance Model with Parameter Uncertainty

Assuming the investor is uncertainty-aversion,the multiprior approach is applied to studying the problem of portfolio choice under the uncertainty about the expected return of risky asset based on the mean-variance model. By introducing a set of constraint constants to measure uncertainty degree of the estimated expected return,it built the max-min model of multi-prior portfolio,and utilized the Lagrange method to obtain the closed-form solution of the model,which was compared with the mean-variance model and the minimum-variance model; then,an empirical study was done based on the monthly returns over the period June 2011 to May 2014 of eight kinds of stocks in Shanghai Exchange 50 Index. Results showed,the weight of multi-prior portfolio was a weighted average of the weight of mean-variance portfolio and that of minimumvariance portfolio; the steady of multi-prior portfolio was strengthened compared with the mean-variance portfolio; the performance of multi-prior portfolio was greater than that of minimum-variance portfolio. The study demonstrates that the investor can improve the steady of multi-prior portfolio as well as its performance for some appropriate constraint constants.

A Mean-variance Problem in the Constant Elasticity of Variance(CEV) Model

In this paper, we focus on a constant elasticity of variance （CEV） modeland want to find its optimal strategies for a mean-variance problem under two constrainedcontrols： reinsurance/new business and investment （no-shorting）. First, aLagrange multiplier is introduced to simplify the mean-variance problem and thecorresponding Hamilton-Jacobi-Bellman （HJB） equation is established. Via a powertransformation technique and variable change method, the optimal strategies withthe Lagrange multiplier are obtained. Final, based on the Lagrange duality theorem,the optimal strategies and optimal value for the original problem （i.e., the efficientstrategies and efficient frontier） are derived explicitly.

The Time-Consistent Optimal Reinsurance Strategy of Insurance Group under the CEV Model

The article introduces proportional reinsurance contracts under the mean-variance criterion,studying the time-consistence investment portfolio problem considering the interests of both insurance companies and reinsurance companies.The insurance claims process follows a jump-diffusion model,assuming that the risk asset prices of insurance companies and reinsurance companies follow CEV models different from each other.In the framework of game theory,the time-consistent equilibrium reinsurance strategy is obtained by solving the extended HJB equation analytically.Finally,numerical examples are used to illustrate the impact of model parameters on equilibrium strategies and provide economic explanations.The results indicate that the decision weights of insurance companies and reinsurance companies do have a significant impact on both the reinsurance ratio and the equilibrium reinsurance strategy.

Goal Achieving Probabilities of Mean-Variance Strategies in a Market with Regime-Switching Volatility

In this paper, we establish properties for the switch-when-safe mean-variance strategies in the context of a Black-Scholes market model with stochastic volatility processes driven by a continuous-time Markov chain with a finite number of states. More precisely, expressions for the goal-achieving probabilities of the terminal wealth are obtained and numerical comparisons of lower bounds for these probabilities are shown for various market parameters. We conclude with asymptotic results when the Markovian changes in the volatility parameters appear with either higher or lower frequencies.

Online risk‑based portfolio allocation on subsets of crypto assets applying a prototype‑based clustering algorithm

Mean-variance portfolio optimization models are sensitive to uncertainty in risk-return estimates,which may result in poor out-of-sample performance.In particular,the estimates may suffer when the number of assets considered is high and the length of the return time series is not sufficiently long.This is precisely the case in the cryptocur-rency market,where there are hundreds of crypto assets that have been traded for a few years.We propose enhancing the mean-variance(MV)model with a pre-selection stage that uses a prototype-based clustering algorithm to reduce the number of crypto assets considered at each investment period.In the pre-selection stage,we run a prototype-based clustering algorithm where the assets are described by variables representing the profit-risk duality.The prototypes of the clustering partition are auto-matically examined and the one that best suits our risk-aversion preference is selected.We then run the MV portfolio optimization with the crypto assets of the selected cluster.The proposed approach is tested for a period of 17 months in the whole cryp-tocurrency market and two selections of the cryptocurrencies with the higher market capitalization(175 and 250 cryptos).We compare the results against three methods applied to the whole market:classic MV,risk parity,and hierarchical risk parity methods.We also compare our results with those from investing in the market index CCI30.The simulation results generally favor our proposal in terms of profit and risk-profit financial indicators.This result reaffirms the convenience of using machine learning methods to guide financial investments in complex and highly-volatile environments such as the cryptocurrency market.

Time-Consistent Portfolio Policy for Asset-Liability Mean-Variance Model with State-Dependent Risk Aversion

In reality,when facing a multi-period asset-liability portfolio selection problem,the risk aversion attitude of a mean-variance investor may depend on the wealth level and liability level.Thus,in this paper,we propose a state-dependent risk aversion model for the investor,in which risk aversion is a linear function of current wealth level and current liability level.Due to the time inconsistency of the resulting multi-period asset-liability mean-variance model,we investigate its time-consistent portfolio policy by solving a nested mean-variance game formulation.We derive the analytical time-consistent portfolio policy,which takes a linear form of current wealth level and current liability level.We also analyze the influence of the risk aversion coefficients on the time-consistent portfolio policy and the investment performance via a numerical example.

MEAN-VARIANCE MODEL BASED ON FILTERS OF MINIMUM SPANNING TREE

This study aims to reduce the statistical uncertainty of the correlation coefficient matrix in the mean-variance model of Markowitz. A filtering algorithm based on minimum spanning tree （MST） is proposed. Daily data of the 30 stocks of the Hang Seng Index （HSI） and Dow Jones Index （DJI） from 2004 to 2009 are selected as the base dataset. The proposed algorithm is compared with the Markowitz method in terms of risk, reliability, and effective size of the portfolio. Results show that （1） although the predicted risk of portfolio built with the MST is slightly higher than that of Markowitz, the realized risk of MST filtering algorithm is much smaller; and （2） the reliability and the effective size of filtering algorithm based on MST is apparently better than that of the Markowitz portfolio. Therefore, conclusion is that filtering algorithm based on MST improves the mean-variance model of Markowitz.

ASSET-LIABILITY MANAGEMENT UNDER BENCHMARK AND MEAN-VARIANCE CRITERIA IN A JUMP DIFFUSION MARKET

This paper investigates continuous-time asset-liability management under benchmark and mean-variance criteria in a jump diffusion market. Specifically, the authors consider one risk-free asset, one risky asset and one liability, where the risky asset＇s price is governed by an exponential Levy process, the liability evolves according to a Levy process, and there exists a correlation between the risky asset and the liability. Two models are established. One is the benchmark model and the other is the mean-variance model. The benchmark model is solved by employing the stochastic dynamic programming and its results are extended to the mean-variance model by adopting the duality theory. Closed-form solutions of the two models are derived.

A novel hybrid algorithm based on a harmony search and artificial bee colony for solving a portfolio optimization problem using a mean-semi variance approach

Portfolio selection is one of the major capital allocation and budgeting issues in financial management, and a variety of models have been presented for optimal selection. Semi-variance is usually considered as a risk factor in drawing up an efficient frontier and the optimal portfolio. Since semi-variance offers a better estimation of the actual risk portfolio, it was used as a measure to approximate the risk of investment in this work. The optimal portfolio selection is one of the non-deterministic polynomial(NP)-hard problems that have not been presented in an exact algorithm, which can solve this problem in a polynomial time. Meta-heuristic algorithms are usually used to solve such problems. A novel hybrid harmony search and artificial bee colony algorithm and its application were introduced in order to draw efficient frontier portfolios. Computational results show that this algorithm is more successful than the harmony search method and genetic algorithm. In addition, it is more accurate in finding optimal solutions at all levels of risk and return.

论投资连结保险的收益

投资连结保险作为寿险公司的投资型险种,其收益和风险均有投保人承担。收益的取得来源于投资账户投资于银行存款、债券、基金及股票的加权平均收益,即投资组合收益。文中运用马科维茨的均值方差模型及多目标非线性规划模型,引入投资者偏好系数,对投资连结保险的收益进行分析,以期对消费者购买投资连结保险有所帮助。

Empirical analysis on risk of security investment

The paper analyzes the theory and application of Markowitz Mean-Variance Model and CAPM model. Firstly, it explains the development process and standpoints of two models and deduces the whole process in detail. Then 30 stocks are choosen from Shangzheng 50 stocks and are testified whether the prices of Shanghai stocks conform to the two models. With the technique of time series and panel data analysis, the research on the stock risk and effective portfolio by ORIGIN and MATLAB software is conducted. The result shows that Shanghai stock market conforms to Markowitz Mean-Variance Model to a certain extent and can give investors reliable suggestion to gain higher return, but there is no positive relation between system risk and profit ratio and CAPM doesn＇t function well in China＇s security market.

A Note on the Mean-Variance Criteria for Discrete Time Financial Markets

It was shown in Xia that for incomplete markets with continuous assets＇ price processes and for complete markets the mean-variance portfolio selection can be viewed as expected utility maximization with non-negative marginal utility. In this paper we show that for discrete time incomplete markets this result is not true.

An Empirical Analysis on the Stable Return of Resident Funds

This paper mainly studies how investors invest in funds to obtain high returns while avoiding risks.Firstly,from the perspective of portfolio investment,this paper introduces the traditional Markowitz mean-variance model and capital asset pricing model(CAPM),then selects four funds from different industries by MATLAB program in Sina Finance and Economics Network for application analysis from which the optimal portfolio point can be obtained under the combination of efficient frontier and capital allocation line.Subsequently,by analyzing the returns of long-term holdings and short-term operations of Noan Growth Hybrid Fund,it is confirmed that long-term holding funds can better cope with the changing market so as to obtain more stable returns.Finally,this paper discusses the dynamic adjustments of asset portfolio.Resident investors are supposed to take into account the market situation and the changes of the fund itself to adjust the holding fund portfolio.Based on the research in this paper,resident investors ought to combine investment funds to diversify risk allocation and make long-term holding plans according to their risk tolerance.At the same time,they should also make appropriate dynamic adjustments when the external environment changes to ensure long-term benefits.

Time-consistent investment-reinsurance strategies towards joint interests of the insurer and the reinsurer under CEV models

The present paper studies time-consistent solutions to an investment-reinsurance problem under a mean-variance framework.The paper is distinguished from other literature by taking into account the interests of both an insurer and a reinsurer jointly.The claim process of the insurer is governed by a Brownian motion with a drift.A proportional reinsurance treaty is considered and the premium is calculated according to the expected value principle.Both the insurer and the reinsurer are assumed to invest in a risky asset,which is distinct for each other and driven by a constant elasticity of variance model.The optimal decision is formulated on a weighted sum of the insurer’s and the reinsurer’s surplus processes.Upon a verification theorem,which is established with a formal proof for a more general problem,explicit solutions are obtained for the proposed investment-reinsurance model.Moreover,numerous mathematical analysis and numerical examples are provided to demonstrate those derived results as well as the economic implications behind.

Time-Consistent Investment Strategy for DC Pension Plan with Stochastic Salary Under CEV Model

This paper aims to derive the time-consistent investment strategy for the defined contribution(DC) pension plan under the mean-variance criterion.The financial market consists of a risk-free asset and a risky asset of which price process satisfies the constant elasticity of variance(CEV) model.Compared with the geometric Brownian motion model,the CEV model has the ability of capturing the implied volatility skew and explaining the volatility smile.The authors assume that the contribution to the pension fund is a constant proportion of the pension member's salary.Meanwhile,the salary is stochastic and its volatility arises from the price process of the risky asset,which makes the proposed model different from most of existing researches and more realistic.In the proposed model,the optimization problem can be decomposed into two sub-problems:Before and after retirement cases.By applying a game theoretic framework and solving extended Hamilton-Jacobi-Bellman(HJB) systems,the authors derive the time-consistent strategies and the corresponding value functions explicitly.Finally,numerical simulations are presented to illustrate the effects of model parameters on the time-consistent strategies.

The Stochastic Maximum Principle for a Jump-Diffusion Mean-Field Model Involving Impulse Controls and Applications in Finance

This paper establishes a stochastic maximum principle for a stochastic control of mean-field model which is governed by a Lévy process involving continuous and impulse control.The authors also show the existence and uniqueness of the solution to a jump-diffusion mean-field stochastic differential equation involving impulse control.As for its application,a mean-variance portfolio selection problem has been solved.

Optimal reactive power dispatch with wind power integrated using group search optimizer with intraspecific competition and le´vy walk

This paper presents the mean–variance(MV)model to solve power system reactive power dispatch problems with wind power integrated.The MV model considers the profit and risk simultaneously under the uncertain wind power(speed)environment.To describe this uncertain environment,the Latin hypercube sampling with Cholesky decomposition simulation method is used to sample uncertain wind speeds.An improved optimization algorithm,group search optimizer with intraspecific competition and le´vy walk,is then used to optimize the MV model by introducing the risk tolerance parameter.The simulation is conducted based on the IEEE 30-bus power system,and the results demonstrate the effectiveness and validity of the proposed model and the optimization algorithm.

Time-Consistent Investment Strategies for a DC Pension Member with Stochastic Interest Rate and Stochastic Income

This paper studies two multi-period mean-variance investment problems for a DC pension member before and after retirement.At any time,the pension manager can invest in a risk-free asset and multi-risky assets.Before retirement,the manager tries to optimize the mean-variance utility of the wealth in the member’s pension account at retirement.At retirement,the pension account wealth(or part of it)is used to purchase a paid-up annuity.After retirement,the manager has to pay the guaranteed annuity,continues to invest,and aims to optimize the mean-variance utility of the terminal wealth at a fix future time,to satisfy the pension member’s heritage and life needs in the next stage.Interest rate risk and income risk are introduced.Applying the game theory and the extended Bellman equation,the time-consistent investment strategies and the efficient frontiers before and after retirement are obtained explicitly.Obtained results indicate that the stochastic interest rate and the stochastic income have essential effects on the investment strategies.