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Book Synopsis Optimizing Marginal Conditional Stochastic Dominance Portfolios by : Gleb Gertsman
Download or read book Optimizing Marginal Conditional Stochastic Dominance Portfolios written by Gleb Gertsman and published by . This book was released on 2018 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt: Marginal Conditional Stochastic Dominance (MCSD) states the probabilistic conditions under which, given a specific portfolio, one risky asset is marginally preferred to another by all risk-averse investors. Furthermore, by increasing the share of dominating assets and reducing the share of dominated assets one can improve the portfolio performance for all these investors. We use this standard MCSD model sequentially to build optimal portfolios that are then compared to the optimal portfolios obtained from Chow's MCSD statistical test model. These portfolios are furthermore compared to the portfolios obtained from the recently developed Almost Marginal Conditional Stochastic Dominance (AMCSD) model. The AMCSD model restricts the class of risk-averse investors by not including extreme case utility functions and reducing the incidence of unrealistic behavior under uncertainty. For each model, an algorithm is developed to manage the various dynamic portfolios traded on the New York, Frankfurt, London, and Tel Aviv stock exchanges during the years 2000-2012. The results show how the various MCSD optimal portfolios provide valid investment alternatives to stochastic dominance optimization.MCSD and AMCSD investment models dramatically improve the initial portfolios and accumulate higher returns while the strategy derived from Chow's statistical test performed poorly and did not yield any positive return.
Book Synopsis Marginal Conditional Stochastic Dominance, Statistical Inference and Measuring Portfolio Performance by : K. Victor Chow
Download or read book Marginal Conditional Stochastic Dominance, Statistical Inference and Measuring Portfolio Performance written by K. Victor Chow and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A simple statistical test is developed for marginal conditional stochastic dominance (MCSD). The MCSD is an extension of second degree stochastic dominance. As such, without specification of the return-generating process, it can rank securities according to marginal changes of return distributions conditionally to the distribution of the market proxy, thereby, proving a powerful technique for measuring portfolio performance. Although the MCSD test is asymptotic and conservative, under both the hypotheses of homoscedasticity and heteroscedasticity, it has power to detect the dominance alternative for samples with more than 300 observations. For an illustration, the MCSD test is applied to international equity markets. The test is able to show that nine of twenty-eight equity markets are dominated by the world market.
Book Synopsis Portfolio Optimization with Stochastic Dominance Constraints by : Darinka Dentcheva
Download or read book Portfolio Optimization with Stochastic Dominance Constraints written by Darinka Dentcheva and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Study of Portfolio Optimization Considering the Third-Order Stochastic Dominance and Skewness by : 陳證安
Download or read book Study of Portfolio Optimization Considering the Third-Order Stochastic Dominance and Skewness written by 陳證安 and published by . This book was released on 2006 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Optimal Portfolios written by Ralf Korn and published by World Scientific. This book was released on 1997 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: The focus of the book is the construction of optimal investment strategies in a security market model where the prices follow diffusion processes. It begins by presenting the complete Black-Scholes type model and then moves on to incomplete models and models including constraints and transaction costs. The models and methods presented will include the stochastic control method of Merton, the martingale method of Cox-Huang and Karatzas et al., the log optimal method of Cover and Jamshidian, the value-preserving model of Hellwig etc.
Download or read book Stochastic Dominance written by Haim Levy and published by Springer Science & Business Media. This book was released on 2006-08-25 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is devoted to investment decision-making under uncertainty. The book covers three basic approaches to this process: the stochastic dominance approach; the mean-variance approach; and the non-expected utility approach, focusing on prospect theory and its modified version, cumulative prospect theory. Each approach is discussed and compared. In addition, this volume examines cases in which stochastic dominance rules coincide with the mean-variance rule and considers how contradictions between these two approaches may occur.
Book Synopsis Revisiting Almost Marginal Conditional Stochastic Dominance by : 蔡安玫
Download or read book Revisiting Almost Marginal Conditional Stochastic Dominance written by 蔡安玫 and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Portfolio Construction Based on Stochastic Dominance and Empirical Likelihood by : Thierry Post
Download or read book Portfolio Construction Based on Stochastic Dominance and Empirical Likelihood written by Thierry Post and published by . This book was released on 2018 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study develops a portfolio optimization method based on the Stochastic Dominance (SD) decision criterion and the Empirical Likelihood (EL) estimation method. SD and EL share a distribution-free assumption framework which allows for dynamic and non-Gaussian multivariate return distributions. The SD/EL method can be implemented using a two-stage procedure which first elicits the implied probabilities using Convex Optimization and subsequently constructs the optimal portfolio using Linear Programming. The solution asymptotically dominates the benchmark and optimizes the goal function in probability, for a class of weakly dependent processes. A Monte Carlo simulation experiment illustrates the improvement in estimation precision using a set of conservative moment conditions about common factors in small samples. In an application to equity industry momentum strategies, SD/EL yields important out-of-sample performance improvements relative to heuristic diversification, Mean-Variance optimization, and a simple 'plug-in' approach.
Book Synopsis Linear Algorithm for Portfolio Optimization with Third-Order Stochastic Dominance by : Yi Fang
Download or read book Linear Algorithm for Portfolio Optimization with Third-Order Stochastic Dominance written by Yi Fang and published by . This book was released on 2018 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a novel linear approximation of expected utility. The approximation guides us as we transfer the traditional quadratic dependence of third-order stochastic dominance (TSD) into an equivalent linear system. The finding also shows a dual relationship between traditional low partial moment condition and the efficient condition of Post (2003). Based on the transformation, we develop a linear algorithm of TSD. Furthermore, we refine the "superconvex" TSD of Post and Kopa (2017) and introduce a corresponding linear system. The portfolio optimization performances of various criteria are also investigated.
Book Synopsis Portfolio Choice Based on Third-Degree Stochastic Dominance by : Thierry Post
Download or read book Portfolio Choice Based on Third-Degree Stochastic Dominance written by Thierry Post and published by . This book was released on 2016 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop an optimization method for constructing investment portfolios that dominate a given benchmark index in terms of third-degree stochastic dominance. Our approach relies on the properties of the semivariance function, a refinement of an existing 'super-convex' dominance condition and quadratic constrained programming. We apply our method to historical stock market data using an industry momentum strategy. Our enhanced portfolio generates important performance improvements compared with alternatives based on mean-variance dominance and second-degree stochastic dominance. Relative to the CSRP all-share index, our portfolio increases average out-of-sample return by almost seven percentage points per annum without incurring more downside risk, using quarterly rebalancing and without short selling.
Book Synopsis Portfolio Selection by Second Order Stochastic Dominance Based on the Risk Aversion Degree of Investors by : Leili Javanmardi
Download or read book Portfolio Selection by Second Order Stochastic Dominance Based on the Risk Aversion Degree of Investors written by Leili Javanmardi and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Portfolio Rules for Conditional Stochastic Dominance by : Ephraim Clark
Download or read book Portfolio Rules for Conditional Stochastic Dominance written by Ephraim Clark and published by . This book was released on 2016 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we generalize the Clark-Jokung 50% portfolio theorem(Management Science, 1999) to an arbitrary threshold and we apply it to a wide and well-known family of distributions, the elliptical distributions (multivariate normal, Student t, multivariate exponential,...). We consider the specific case of a two-asset portfolio where the cumulative conditional expected outcome on one asset is greater or equal to the cumulative conditional expected outcome of the other asset.We show that when the joint distribution of the returns of the two assets follows an elliptical distribution, the conditions for 100alpha% portfolio theorem to hold are a higher expected return for the dominant asset and that the threshold 100alpha% is less than the percentage invested in the minimum-variance portfolio.
Book Synopsis Stochastic dominance in portfolio analysis and asset pricing by : Andrey M. Lizyayev
Download or read book Stochastic dominance in portfolio analysis and asset pricing written by Andrey M. Lizyayev and published by Rozenberg Publishers. This book was released on 2010 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Making Inefficient Market Indices Efficient by : Ephraim Clark
Download or read book Making Inefficient Market Indices Efficient written by Ephraim Clark and published by . This book was released on 2009 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: The concept of efficient portfolios plays an important role in modern financial theory and practice. Although there is an extensive and growing literature that focuses on testing portfolio efficiency, outside of mean-variance optimization, which has several serious shortcomings, no systematic methodology for building efficient portfolios from inefficient indices has been developed. This paper addresses this issue. It uses the concept of Marginal Conditional Stochastic Dominance and a generalization of the 50% Portfolio Rule to develop a tractable and parsimonious methodology for constructing an efficient portfolio from a given, inefficient index. Because the Stochastic Dominance (SD) approach considers the entire probability distributions of asset returns, the resulting portfolios are efficient with respect to all risk-averse, non-satiable investors regardless of the form of their utility functions or the distributions of asset returns.
Book Synopsis Stochastic Portfolio Theory by : E. Robert Fernholz
Download or read book Stochastic Portfolio Theory written by E. Robert Fernholz and published by Springer Science & Business Media. This book was released on 2002-04-12 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic portfolio theory is a mathematical methodology for constructing stock portfolios and for analyzing the effects induced on the behavior of these portfolios by changes in the distribution of capital in the market. Stochastic portfolio theory has both theoretical and practical applications: as a theoretical tool it can be used to construct examples of theoretical portfolios with specified characteristics and to determine the distributional component of portfolio return. This book is an introduction to stochastic portfolio theory for investment professionals and for students of mathematical finance. Each chapter includes a number of problems of varying levels of difficulty and a brief summary of the principal results of the chapter, without proofs.
Book Synopsis Stochastic Programming Models and Methods for Portfolio Optimization and Risk Management by : Rudabeh Meskarian
Download or read book Stochastic Programming Models and Methods for Portfolio Optimization and Risk Management written by Rudabeh Meskarian and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This project is focused on stochastic models and methods and their application in portfolio optimization and risk management. In particular it involves development and analysis of novel numerical methods for solving these types of problem. First, we study new numerical methods for a general second order stochastic dominance model where the underlying functions are not necessarily linear. Specifically, we penalize the second order stochastic dominance constraints to the objective under Slater's constraint qualification and then apply the well known stochastic approximation method and the level function methods to solve the penalized problem and present the corresponding convergence analysis. All methods are applied to some portfolio optimization problems, where the underlying functions are not necessarily linear all results suggests that the portfolio strategy generated by the second order stochastic dominance model outperform the strategy generated by the Markowitz model in a sense of having higher return and lower risk. Furthermore a nonlinear supply chain problem is considered, where the performance of the level function method is compared to the cutting plane method. The results suggests that the level function method is more efficient in a sense of having lower CPU time as well as being less sensitive to the problem size. This is followed by study of multivariate stochastic dominance constraints. We propose a penalization scheme for the multivariate stochastic dominance constraint and present the analysis regarding the Slater constraint qualification. The penalized problem is solved by the level function methods and a modified cutting plane method and compared to the cutting surface method proposed in [70] and the linearized method proposed in [4]. The convergence analysis regarding the proposed algorithms are presented. The proposed numerical schemes are applied to a generic budget allocation problem where it is shown that the proposed methods outperform the linearized method when the problem size is big. Moreover, a portfolio optimization problem is considered where it is shown that the a portfolio strategy generated by the multivariate second order stochastic dominance model outperform the portfolio strategy generated by the Markowitz model in sense of having higher return and lower risk. Also the performance of the algorithms is investigated with respect to the computation time and the problem size. It is shown that the level function method and the cutting plane method outperform the cutting surface method in a sense of both having lower CPU time as well as being less sensitive to the problem size. Finally, reward-risk analysis is studied as an alternative to stochastic dominance. Specifically, we study robust reward-risk ratio optimization. We propose two robust formulations, one based on mixture distribution, and the other based on the first order moment approach. We propose a sample average approximation formulation as well as a penalty scheme for the two robust formulations respectively and solve the latter with the level function method. The convergence analysis are presented and the proposed models are applied to Sortino ratio and some numerical test results are presented. The numerical results suggests that the robust formulation based on the first order moment results in the most conservative portfolio strategy compared to the mixture distribution model and the nominal model.
Book Synopsis Stochastic Dominance by : G. A. Whitmore
Download or read book Stochastic Dominance written by G. A. Whitmore and published by . This book was released on 1978 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical foundations of stochastic dominance; Portfolio applications: empirical studies; Portfolio applications: computational aspects; Applications to financial management and capital markets; Applications in economic theory and analysis.
