Almost expectation and excess dependence notions (original) (raw)
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Almost Stochastic Dominance and Efficient Investment Sets
American Journal of Operations Research, 2012
A major drawback of Mean-Variance and Stochastic Dominance investment criteria is that they may fail to determine dominance even in situations when all "reasonable" decision-makers would clearly prefer one alternative over another. Levy and Leshno [1] suggest Almost Stochastic Dominance (ASD) as a remedy. This paper develops algorithms for deriving the ASD efficient sets. Empirical application reveals that the improvement to the efficient sets implied by ASD is substantial (64% reduction for FSD). Direct expected utility maximization shows that investment portfolios excluded from the ASD efficient set would not have been chosen by any investors with reasonable preferences.
Note on the proportions of financial assets with dependent distributions in optimal portfolios
2002
We study the proportions of financial assets in optimal portfolios, where the portfolio is optimized by the maximization of expected utility. Our main goal is to investigate the magnitude of the proportions provided that the assets' distributions display a certain type of stochastic dominance. Our main question: "Why people buy more of an asset than of another one?" We introduce and study new notions of stochastic dominance. We derive new versions and generalizations of the results of . Our results apply to not necessarily independent returns as well. We give several examples.
Journal of Physics: Conference Series, 2017
Investors always seek an efficient portfolio which is a portfolio that has a maximum return on specific risk or minimal risk on specific return. Almost marginal conditional stochastic dominance (AMCSD) criteria can be used to form the efficient portfolio. The aim of this research is to apply the AMCSD criteria to form an efficient portfolio of bank shares listed in the LQ-45. This criteria is used when there are areas that do not meet the criteria of marginal conditional stochastic dominance (MCSD). On the other words, this criteria can be derived from quotient of areas that violate the MCSD criteria with the area that violate and not violate the MCSD criteria. Based on the data bank stocks listed on LQ-45, it can be stated that there are 38 efficient portfolios of 420 portfolios where each portfolio comprises of 4 stocks and 315 efficient portfolios of 1710 portfolios with each of portfolio has 3 stocks.
As the dependence structure is …xed, do more risky assets lead to more risky portfolios?
2002
As the dependence structure (i.e. the copula) among the assets is …xed, one might think that the riskier the assets, the riskier the portfolio. Surprisingly enough, this conjecture turns out to be false even for coherent risk measures and normal returns. We show that two conditions are able to preserve risk ordering under the portfolio: convexity for the risk measure and conditional increasingness for the copula. Even- tually, conditional increasingness is checked for the most popular families of copulas used in …nancial modelling and actuarial sciences.
Stochastic dominance on optimal portfolio with one risk-less and two risky assets
Economics Bulletin, 2005
The paper provides restrictions on the investor's utility function which are sufficient for a dominating shift no decrease in the investment in the respective asset if there are one risk free asset and two risky assets in the portfolio. The analysis is then confined to portfolio in which the distributions of assets differ by a first−degree−stochastic dominance shift.
Portfolio analysis of stocks, bonds, and managed futures using compromise stochastic dominance
Journal of Futures Markets, 1991
Second degree stochastic dominance has been proposed also as a criterion (Levy and Sarnet, 1972). It is defined by Z,F,(r) = Z,Fo(r) far all r , with the strict inequality holding for at least one value of return, r. This report uses first degree dominance since first degree dominance implies second degree (Hadar and Rgssell, 1969).
Portfolio Selection Problems via the Bivariate Characterization of Stochastic Dominance Relations
Mathematical Finance, 1996
Stochastic dominance (SD) is a very useful tool in various areas of economics and finance. The purpose of this piper is to provide the results of SD relations developed in other areas such as applied probability which, we believe, are useful for many portfolio selection problems. In particular. the bivariate characterization of SD relations given by Shanthikumar and Yao (199 I) is a powerful tool for the demand and the shift effect problems in optitnal portliilios. The method enables one to extend many result< that hold for the case where the underlying assets are statistically independent to the dependent case directly.
Almost marginal conditional stochastic dominance
Journal of Banking & Finance, 2014
Marginal Conditional Stochastic Dominance (MCSD) developed by gives the conditions under which all risk-averse individuals prefer to increase the share of one risky asset over another in a given portfolio. In this paper, we extend this concept to provide conditions under which most (and not all) risk-averse investors behave in this way. Instead of stochastic dominance rules, almost stochastic dominance is used to assess the superiority of one asset over another in a given portfolio. Switching from MCSD to Almost MCSD (AMCSD) helps to reconcile common practices in asset allocation and the decision rules supporting stochastic dominance relations. A financial application is further provided to demonstrate that using AMCSD can indeed improve investment efficiency.
Stochastic dominance and risk measure: A decision-theoretic foundation for VaR and C-VaR
European Journal of Operational Research, 2010
Is it possible to obtain an objective and quantifiable measure of risk backed up by choices made by some specific groups of rational investors? To answer this question, in this paper we establish some behavior foundations for various types of VaR models, including VaR and conditional-VaR, as measures of downside risk.
Comparison of multivariate risks, Fr echet-bounds, and positive dependence
In this paper we extend some recent results on the comparison of multivariate risk vectors w.r.t. supermodular or related orderings. In particular we identify some function class which allows to conclude that positive (negative) dependent random vectors are more (less) risky than independent vectors w.r.t. these functions. We also state comparison criteria w.r.t. the directionally convex order for some classes of risk vectors which are modelled by functional inuence fac- tors. Finally we discuss Fr echet-bounds when multivariate marginals are given in particular w.r.t. -monotone functions. It turns out that comonotonic vectors in the case of multivariate marginals no longer yield necessarily the largest risks but even may in some cases be vec- tors which minimize risk.