Justifying Mean-Variance Portfolio Selection when Asset Returns Are Skewed

2021 ◽  
Author(s):  
Frank Schuhmacher ◽  
Hendrik Kohrs ◽  
Benjamin R. Auer
Keyword(s):  
Empirical Evidence ◽  
Sufficient Conditions ◽  
Asset Returns ◽  
Common Belief ◽  
Risky Assets ◽  
Common Stocks ◽  
Elliptical Model ◽  
Mean And Variance ◽  
Mean Variance Portfolio ◽  
Mean Variance

We show that, in the presence of a risk-free asset, the return distribution of every portfolio is determined by its mean and variance if and only if asset returns follow a specific skew-elliptical distribution. Thus, contrary to common belief among academics and practitioners, skewed returns do not allow a rejection of mean-variance analysis. Our work differs from Chamberlain's [Chamberlain G (1983) A characterization of the distributions that imply mean-variance utility functions. J. Econom. Theory 29(1):185–201.] by focusing on the returns of portfolios, where the weights over the risk-free asset and the risky assets sum to unity. Furthermore, it extends Meyer's [Meyer J, Rasche RH (1992) Sufficient conditions for expected utility to imply mean-standard deviation rankings: Empirical evidence concerning the location and scale condition. Econom. J. (London) 102(410):91–106.] by introducing elliptical noise into their generalized location-scale framework. To emphasize the relevance of our skew-elliptical model, we additionally provide empirical evidence that it cannot be rejected for the returns of typical portfolios of common stocks or popular alternative investments. This paper was accepted by Kay Giesecke, finance.

CONTINUOUS-TIME MEAN–VARIANCE OPTIMIZATION FOR DEFINED CONTRIBUTION PENSION FUNDS WITH REGIME-SWITCHING

2019 ◽  
Vol 22 (06) ◽  
pp. 1950029
Author(s):  
ZHIPING CHEN ◽  
LIYUAN WANG ◽  
PING CHEN ◽  
HAIXIANG YAO
Keyword(s):  
Brownian Motion ◽  
Portfolio Selection ◽  
Continuous Time ◽  
Optimal Strategy ◽  
Regime Switching ◽  
Efficient Frontier ◽  
Defined Contribution ◽  
Risky Assets ◽  
Mean Variance Portfolio ◽  
Mean Variance

Using mean–variance (MV) criterion, this paper investigates a continuous-time defined contribution (DC) pension fund investment problem. The framework is constructed under a Markovian regime-switching market consisting of one bank account and multiple risky assets. The prices of the risky assets are governed by geometric Brownian motion while the accumulative contribution evolves according to a Brownian motion with drift and their correlation is considered. The market state is modeled by a Markovian chain and the random regime-switching is assumed to be independent of the underlying Brownian motions. The incorporation of the stochastic accumulative contribution and the correlations between the contribution and the prices of risky assets makes our problem harder to tackle. Luckily, based on appropriate Riccati-type equations and using the techniques of Lagrange multiplier and stochastic linear quadratic control, we derive the explicit expressions of the optimal strategy and efficient frontier. Further, two special cases with no contribution and no regime-switching, respectively, are discussed and the corresponding results are consistent with those results of Zhou & Yin [(2003) Markowitz’s mean-variance portfolio selection with regime switching: A continuous-time model, SIAM Journal on Control and Optimization 42 (4), 1466–1482] and Zhou & Li [(2000) Continuous-time mean-variance portfolio selection: A stochastic LQ framework, Applied Mathematics and Optimization 42 (1), 19–33]. Finally, some numerical analyses based on real data from the American market are provided to illustrate the property of the optimal strategy and the effects of model parameters on the efficient frontier, which sheds light on our theoretical results.

scholarly journals Mean–Variance Portfolio Management with Functional Optimization

2021 ◽  
pp. 2050055
Author(s):  
Ka Wai Tsang ◽  
Zhaoyi He
Keyword(s):  
Portfolio Management ◽  
Optimization Problems ◽  
Empirical Studies ◽  
Optimal Solution ◽  
Weight Vector ◽  
Optimization Approach ◽  
Functional Optimization ◽  
Mean And Variance ◽  
Mean Variance Portfolio ◽  
Mean Variance

This paper introduces a new functional optimization approach to portfolio optimization problems by treating the unknown weight vector as a function of past values instead of treating them as fixed unknown coefficients in the majority of studies. We first show that the optimal solution, in general, is not a constant function. We give the optimal conditions for a vector function to be the solution, and hence give the conditions for a plug-in solution (replacing the unknown mean and variance by certain estimates based on past values) to be optimal. After showing that the plug-in solutions are sub-optimal in general, we propose gradient-ascent algorithms to solve the functional optimization for mean–variance portfolio management with theorems for convergence provided. Simulations and empirical studies show that our approach can perform significantly better than the plug-in approach.

scholarly journals Time-Consistent Strategies for Multi-Period Portfolio Optimization with/without the Risk-Free Asset

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Zhongbao Zhou ◽  
Xianghui Liu ◽  
Helu Xiao ◽  
TianTian Ren ◽  
Wenbin Liu
Keyword(s):  
Empirical Studies ◽  
Lagrange Function ◽  
Investment Strategy ◽  
Open Loop ◽  
Investment Strategies ◽  
Variance Model ◽  
Risky Assets ◽  
Consistent Solution ◽  
Mean Variance Portfolio ◽  
Mean Variance

The pre-commitment and time-consistent strategies are the two most representative investment strategies for the classic multi-period mean-variance portfolio selection problem. In this paper, we revisit the case in which there exists one risk-free asset in the market and prove that the time-consistent solution is equivalent to the optimal open-loop solution for the classic multi-period mean-variance model. Then, we further derive the explicit time-consistent solution for the classic multi-period mean-variance model only with risky assets, by constructing a novel Lagrange function and using backward induction. Also, we prove that the Sharpe ratio with both risky and risk-free assets strictly dominates that of only with risky assets under the time-consistent strategy setting. After the theoretical investigation, we perform extensive numerical simulations and out-of-sample tests to compare the performance of pre-commitment and time-consistent strategies. The empirical studies shed light on the important question: what is the primary motivation of using the time-consistent investment strategy.

Equilibrium Pricing Extending the Mean-Variance Theory Using Weighted Possibilistic Mean of Investor’s Subjectivity

2013 ◽  
Vol 17 (2) ◽  
pp. 237-243
Author(s):  
Takashi Hasuike ◽  
Keyword(s):  
Capital Market ◽  
Analytical Approach ◽  
Risky Assets ◽  
Equilibrium Pricing ◽  
Proposed Model ◽  
Fuzzy Mean ◽  
Equilibrium Approach ◽  
Mean And Variance ◽  
The Mean ◽  
Mean Variance

This paper proposes an extended analytical approach to developing an equilibrium pricing vector with various types of investor’s subjectivity based on extended Mean-Variance (MV) theory. Weighted fuzzy mean and variance are introduced in order to represent investor’s subjectivity numerically. Similar to the traditional MV-based equilibrium approach, the equilibrium pricing vector of the proposed model is analytically obtained in mathematical programming. A macroeconomic index based on risky assets, which provides information with respect to the soundness of the capital market with subjectivity, is also constructed.

Implied Ambiguity: Mean-Variance Inefficiency and Pricing Errors

2021 ◽  
Author(s):  
Chiaki Hara ◽  
Toshiki Honda
Keyword(s):  
Portfolio Choice ◽  
Ambiguity Aversion ◽  
Sufficient Conditions ◽  
Asset Returns ◽  
Sharpe Ratio ◽  
Choice Problem ◽  
Pricing Errors ◽  
Necessary And Sufficient ◽  
Mean Variance ◽  
The U.S

We investigate the optimal portfolio choice problem for an investor who has a utility function of the smooth ambiguity model. We identify necessary and sufficient conditions for a given portfolio to be optimal for such an investor. We define the implied ambiguity of a portfolio as the smallest ambiguity aversion coefficient with which the portfolio is optimal, and the measure of ambiguity perception as the part of the variability in asset returns that can be attributed to the ambiguity. We show that there are one-to-one relations between the implied ambiguity, the Sharpe ratio, and the pricing errors when the portfolio is taken as the pricing portfolio, and that the measure of ambiguity perception is determined by the Sharpe ratio and the alpha. Based on the U.S. stock market data, we assess how ambiguity averse the representative investor is and what types of stocks the investor perceives as having more ambiguous returns than others. This paper was accepted by Manel Baucells, behavioral economics and decision analysis.

Dynamic mean-variance portfolio analysis under model risk

2009 ◽  
Vol 12 (4) ◽  
pp. 91-115 ◽  
Author(s):  
Daniel Kuhn ◽  
Panos Parpas ◽  
Berç Rustem ◽  
Raquel Fonseca
Keyword(s):  
Portfolio Analysis ◽  
Model Risk ◽  
Mean Variance Portfolio ◽  
Mean Variance
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