Portfolio Selection

In this paper we develop a framework for the study of financial equilibrium in the case of sectors in the economy, each of which is faced with two objectives/criteria in his portfolio selection decision making. In particular, we first present the bicriteria decision model of an individual financial sector, who seeks an optimal portfolio composition, given that the wishes to

This paper studies an application of a Darwinian theory of portfolio selection to stocks listed in the Dow Jones Industrial Average (DJIA). We analyze numerically the long-run outcome of the competition of fix-mix portfolio rules in a stock market with actual DJIA dividends. In the model seemingly rational strategies can do very poorly against seemingly irrational strategies. Moreover, the interaction of strategies can lead to stochastic time series of asset prices that do not converge. The simulations also show that the evolutionary portfolio rule discovered in Hens and Schenk-Hopp´e (2004) will eventually hold total market wealth in competition with fix-mix portfolio rules derived from mean-variance optimization, maximum growth theory and behavioral finance. According to this evolutionary rule, portfolio weights should be proportional to the expected relative dividends of the assets. As an implication asset prices converge to expected relative dividends.

Abstract: In this paper, we propose a methodology to value the portfolio choices based on the prediction of future returns where the dependence structure of joint returns and the behavior of single returns are estimated separately. In particular, we assume the ...

Since asset returns have been recognized as not normally distributed, the avenue of research regarding portfolio higher moments soon emerged. To account for uncertainty and vagueness of portfolio returns as well as of higher moment risks, we proposed a new portfolio selection model employing fuzzy sets in this paper. A fuzzy multi-objective linear programming (MOLP) for portfolio optimization is formulated using marginal impacts of assets on portfolio higher moments, which are modelled by trapezoidal fuzzy numbers. Through a consistent centroid-based ranking of fuzzy numbers, the fuzzy MOLP is transformed into an MOLP that is then solved by the maximin method. By taking portfolio higher moments into account, the approach enables investors to optimize not only the normal risk (variance) but also the asymmetric risk (skewness) and the risk of fat-tails (kurtosis). An illustrative example demonstrates the efficiency of the proposed methodology comparing to previous portfolio optimization models.

Although the concept of entropy is originated from thermodynamics, its concepts and relevant principles, especially the principles of maximum entropy and minimum cross-entropy, have been extensively applied in finance. In this paper, we review the concepts and principles of entropy, as well as their applications in the field of finance, especially in portfolio selection and asset pricing. Furthermore, we review the effects of the applications of entropy and compare them with other traditional and new methods.

We gauge the economic value of multivariate covariance estimators by assessing the risk-return performance of the resulting mean-variance efficient portfolios. A dynamic asset allocation framework is deployed, where the multivariate covariance forecasts compete against simpler nonparametric rivals widely used in the industry. A two-layer portfolio construction process for global asset allocation is developed to overcome the problem of handling large

We develop a multivariate generalization of the Markov–switching GARCH model introduced by Haas, Mittnik, and Paolella (2004b) and derive its fourth–moment structure. An application to international stock markets illustrates the relevance of accounting for volatility regimes from both a statistical and economic perspective, including out–of–sample portfolio selection and computation of Value–at–Risk.

Modern Portfolio Theory (MPT) is based upon the classical Markowitz model which uses variance as a risk measure. A generalization of this approach leads to mean-risk models, in which a return distribution is characterized by the expected value of return (desired to be large) and a “risk” value (desired to be kept small). Portfolio choice is made by solving an optimization problem, in which the portfolio risk is minimized and a desired level of expected return is specified as a constraint. The need to penalize different undesirable aspects of the return distribution led to the proposal of alternative risk measures, notably those penalizing only the downside part (adverse) and not the upside (potential). The downside risk considerations constitute the basis of the Post Modern Portfolio Theory (PMPT). Examples of such risk measures are lower partial moments, Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). We revisit these risk measures and the resulting mean-risk models. We discuss alternative models for portfolio selection, their choice criteria and the evolution of MPT to PMPT which incorporates: utility maximization and stochastic dominance. Copyright © 2009 Wilmott Magazine Ltd

The aim of this paper is to compare two asset allocation methods for a pension scheme during the decumulation phase in the simplified portfolio selection between a risky asset following a geometric Brownian motion and a riskless asset. The two asset allocation criteria are the ruin probability of the insurance company and the optimization of the economic capital. We first

This paper analyzes discrete time portfolio selection models with Lévy processes. We first implement portfolio models under the hypotheses the vector of log-returns follow or a multivariate Variance Gamma model or a Multivariate Normal Inverse Gaussian model or a Brownian Motion. In particular, we propose an ex-ante and an ex-post empirical comparisons by the point of view of different investors. Thus, we compare portfolio strategies considering different term structure scenarios and different distributional assumptions when unlimited short sales are allowed.

In this paper, we review some fuzzy linear programming methods and techniques from a practical point of view. In the first part, the general history and the approach of fuzzy mathematical programming are introduced. Using a numerical example, some models of fuzzy linear programming are described. In the second part of the paper, fuzzy mathematical programming approaches are compared to stochastic programming ones. The advantages and disadvantages of fuzzy mathematical programming approaches are exemplified in the setting of an optimal portfolio selection problem. Finally, some newly developed ideas and techniques in fuzzy mathematical programming are briefly reviewed.

In spite of a large number of multi-criteria models applied to solve the problem of optimal portfolio selection and a large number of market criteria and accounting criteria proposed for these models, the problem of portfolio containing securities from different industries has not yet been adequately solved. Namely, neither can stocks of companies from different industries be compared using the same criteria nor can the weight of a particular criteria be equal for them all. Therefore this paper develops a new two-step model that will overcome the shortcomings of the previously used models. The model is divided into two different but related pillars: the choice of different industries to form the overall portfolio and the choice of portfolio for each industry. The multi-criteria model used in this paper is a modified multi-criteria programming model based on the PROMETHEE II approach. The selected model has been applied at the Zagreb Stock Exchange (ZSE) as a real case.