Mean-Variance Policy for Discrete-time Cone Constrained Markets: The Consistency in Efficiency and Minimum-Variance Signed Supermartingale Measure

The discrete-time mean-variance portfolio selection formulation, a representative of general dynamic mean-risk portfolio selection problems, does not satisfy time consistency in efficiency (TCIE) in general, i.e., a truncated pre-committed efficient policy may become inefficient when considering the corresponding truncated problem, thus stimulating investors' irrational investment behavior. We investigate analytically effects of portfolio constraints on time consistency of efficiency for convex cone constrained markets. More specifically, we derive the semi-analytical expressions for the pre-committed efficient mean-variance policy and the minimum-variance signed supermartingale measure (VSSM) and reveal their close relationship. Our analysis shows that the pre-committed discrete-time efficient mean-variance policy satisfies TCIE if and only if the conditional expectation of VSSM's density (with respect to the original probability measure) is nonnegative, or once the conditional expectation becomes negative, it remains at the same negative value until the terminal time. Our findings indicate that the property of time consistency in efficiency only depends on the basic market setting, including portfolio constraints, and this fact motivates us to establish a general solution framework in constructing TCIE dynamic portfolio selection problem formulations by introducing suitable portfolio constraints

Conditional Mean-Variance Efficiency of the U.S. Stock Market

We apply the method of constrained asset share estimation (CASE) to test the mean-variance efficiency (MVE) of the stock market. This method allows conditional expected returns to vary in unrestricted ways, given investor preferences. We also allow conditional variances to follow an ARCH process. The data estimate reasonably the coefficient of relative risk aversion, though are unable to reject investor risk neutrality. We reject the restrictions implied by MVE, although changing conditional variances improve statistically upon measured market efficiency. We find that unrestricted asset-share and ARCH models help forecast excess returns. Once MVE is imposed, however, this forecasting ability disappears.

Semi-bayesian D-optimal designs and estimation procedures for mean and variance functions.

Semi-Bayesian D-optimal designs for fitting mean and variance functions are derived for some prior distributions on the variance function parameters. The impact of the mean of the prior and of the uncertainty about this mean is analyzed. Simulation studies are performed to investigate whether the choice of design has a substantial impact on the efficiency of the mean and the variance function parameter estimation and whether the D-optimality criterion is appropriate irrespective of the method applied to estimate the variance function parameters.Functions;

Geometric Representation of the Mean-Variance-Skewness Portfolio Frontier Based upon the Shortage Function

The literature suggests that investors prefer portfolios based on mean, variance and skewness rather than portfolios based on mean-variance (MV) criteria solely. Furthermore, a small variety of methods have been proposed to determine mean-variance-skewness (MVS) optimal portfolios. Recently, the shortage function has been introduced as a measure of efficiency, allowing to characterize MVS optimalportfolios using non-parametric mathematical programming tools. While tracing the MV portfolio frontier has become trivial, the geometric representation of the MVS frontier is an open challenge. A hitherto unnoticed advantage of the shortage function is that it allows to geometrically represent the MVS portfolio frontier. The purpose of this contribution is to systematically develop geometric representations of the MVS portfolio frontier using the shortage function and related approaches.shortage function, efficient frontier, mean-variance-skewness efficiency

Is the Market Portfolio Efficient? A New Test to Revisit the Roll (1977) versus Levy and Roll (2010) Controversy

Levy and Roll (Review of Financial Studies, 2010) have recently revived the debate related to the market portfolio's efficiency suggesting that it may be mean-variance efficient after all. This paper develops an alternative test of portfolio mean-variance efficiency based on the realistic assumption that all assets are risky. The test is based on the vertical distance of a portfolio from the efficient frontier. Monte Carlo simulations show that our test outperforms the previous mean-variance efficiency tests for large samples since it produces smaller size distortions for comparable power. Our empirical application to the US equity market highlights that the market portfolio is not mean-variance efficient, and so invalidates the zerobeta CAPM.Efficient portfolio, mean-variance efficiency, efficiency test.

Market Efficiency of Oil Spot and Futures: A Mean-Variance and Stochastic Dominance Approach

This paper examines the market efficiency of oil spot and futures prices by using both mean-variance (MV) and stochastic dominance (SD) approaches. Based on the West Texas Intermediate crude oil data for the sample period 1989-2008, we find no evidence of any MV and SD relationships between oil spot and futures indices. This infers that there is no arbitrage opportunity between these two markets, spot and futures do not dominate one another, investors are indifferent to investing in spot or futures, and the spot and futures oil markets are efficient and rational. The empirical findings are robust to each sub-period before and after the crises for different crises, and also to portfolio diversification.Stochastic dominance; risk averter; oil futures market; market efficiency

Portfolio Inefficiency and the Cross-Section of Expected Returns

A plot of expected returns versus betas obeys virtually no relation to an inefficient index portfolio's mean-variance location. If the index portfolio is inefficient, then the coefficients and R- squared from an ordinary-least-squares regression of expected returns on betas can equal essentially any desired values. The mean-variance location of the index does determine the properties of a cross- sectional mean-beta relation fitted by generalized least squares (GLS). As the index portfolio moves closer to exact efficiency, the GLS mean-beta relation moves closer to the exact linear relation corresponding to an efficient portfolio with the same variance. The goodness-of-fit for the GLS regression is the index portfolio's squared relative efficiency, which measures closeness to efficiency in mean-variance space.

Portfolio Management: An investigation of the implications of measurement errors in stock prices on the creation, management and evaluation of stock portfolios, using stochastic simulations

In this paper, we investigate the implications of measurement errors in the daily published stock prices on the creation and management of efficient portfolios. Using stochastic simulation techniques and the Markowitz Mean Variance approach in the creation of the weights of the various stocks of a portfolio, we conclude that measurement errors have significant implications on the efficiency of the management of a stock portfolio.Markowitz Mean Variance, Measurement Errors in Returns, Stochastic Simulation.

On the efficiency of estimators in truncated height samples

We test the efficiency of estimators proposed for truncated height samples with a new data set of over 23,000 height observations covering nearly all conscripts in Drenthe, a province of the Netherlands, over the period 1826-1860. We find that the `best' estimator, truncated ML, in its unrestricted form overestimates the mean and underestimates the variance. If the variance is set to the population variance, the mean is underestimated. We question the normality assumption that is typically made in this literature. Our `population' is skewed, which might explain the poor performance of the estimators

Mean-Variance-Skewness Portfolio Performance Gauging: A General Shortage Function and Dual Approach

This paper proposes a nonparametric efficiency measurement approach for the static portfolio selection problem in mean-variance-skewness space. A shortage function is defined that looks for possible increases in return and skewness and decreases in variance. Global optimality is guaranteed for the resulting optimal portfolios. We also establish a link to a proper indirect mean-variance-skewness utility function. For computational reasons, the optimal portfolios resulting from this dual approach are only locally optimal. This framework permits to differentiate between portfolio efficiency and allocative efficiency, and a convexity efficiency component related to the difference between the primal, non-convex approach and the dual, convex approach. Furthermore, in principle, information can be retrieved about the revealed risk aversion and prudence of investors. An empirical section on a small sample of assets serves as an illustration.shortage function, efficient frontier, mean-variance-skewness, portfolios, risk aversion, prudence