Download or read book A Comparison of Risk Measures for Portfolio Optimization With Cardinality Constraints written by Henrique Ramos and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ever since the introduction of Markowitz's classical quadratic programming problem, transforming portfolio optimization into a linear programming (LP) problem has drawn much attention from researchers and practitioners, given the tractability of LP. However, using non-linear risk measures and including real features and frictions may pose a challenge. In this paper, we solve the optimization problem of minimum portfolio risk for seven measures using linear programming under cardinality constraints. The risk measures used are Expected Loss, Expected Loss Deviation, Expected Shortfall, Shortfall Deviation Risk, Expectile Value at Risk, Deviation Expectile Value at Risk, and Maximum Loss. We assess the out-of-sample performance of seven risk-optimized portfolios with a maximum size of 20 assets for S &P 100 components from 2010 to mid-2020. After subtracting transaction costs, the Expected Loss Deviation portfolios have shown superior performance in terms of diversification and risk, the Maximum Loss portfolios have presented higher Sharpe ratio, the Expected Loss portfolios have higher absolute returns, Sortino and STARR ratios, and the Expected Shortfall portfolios have presented lower Beta coefficients than the benchmark and other risk-based portfolios. All portfolios present significant alpha after adjusting for several risk factors. The main results hold for subperiod analysis, different cardinalities, and other rebalancing periods. Our results show that superior performance can be achieved with simple linearized optimization models with lower market exposure measure by the CAPM beta.
Download or read book Linear and Mixed Integer Programming for Portfolio Optimization written by Renata Mansini and published by Springer. This book was released on 2015-06-10 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents solutions to the general problem of single period portfolio optimization. It introduces different linear models, arising from different performance measures, and the mixed integer linear models resulting from the introduction of real features. Other linear models, such as models for portfolio rebalancing and index tracking, are also covered. The book discusses computational issues and provides a theoretical framework, including the concepts of risk-averse preferences, stochastic dominance and coherent risk measures. The material is presented in a style that requires no background in finance or in portfolio optimization; some experience in linear and mixed integer models, however, is required. The book is thoroughly didactic, supplementing the concepts with comments and illustrative examples.
Download or read book Dynamic Mean variance Portfolio Optimization with Value at Risk Constraint in Continuous time written by Dian Yu and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper studies the dynamic mean-risk portfolio optimization problem with variance and Value-at-Risk(VaR) as the risk measures in recognizing the importance of incorporating different risk measures in the portfolio management model. Using the martingale approach and combining it with the quantile optimization technique, we provide the solution framework for this problem and show that the optimal terminal wealth may have different patterns under a general market setting. When the market parameters are deterministic, we develop the closed-form solution for this problem. Examples are provided to illustrate the solution procedure of our method and demonstrate the beneft of our dynamic portfolio model comparing with its static counterpart.
Download or read book Portfolio Optimization with Quantile based Risk Measures written by Gerardo José Lemus Rodriguez and published by . This book was released on 1999 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Portfolio Optimization and Management written by Frank J. Fabozzi and published by John Wiley & Sons. This book was released on 2007-04-27 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for Robust Portfolio Optimization and Management "In the half century since Harry Markowitz introduced his elegant theory for selecting portfolios, investors and scholars have extended and refined its application to a wide range of real-world problems, culminating in the contents of this masterful book. Fabozzi, Kolm, Pachamanova, and Focardi deserve high praise for producing a technically rigorous yet remarkably accessible guide to the latest advances in portfolio construction." --Mark Kritzman, President and CEO, Windham Capital Management, LLC "The topic of robust optimization (RO) has become 'hot' over the past several years, especially in real-world financial applications. This interest has been sparked, in part, by practitioners who implemented classical portfolio models for asset allocation without considering estimation and model robustness a part of their overall allocation methodology, and experienced poor performance. Anyone interested in these developments ought to own a copy of this book. The authors cover the recent developments of the RO area in an intuitive, easy-to-read manner, provide numerous examples, and discuss practical considerations. I highly recommend this book to finance professionals and students alike." --John M. Mulvey, Professor of Operations Research and Financial Engineering, Princeton University
Download or read book Asymmetric Risk Measures and Tracking Models for Portfolio Optimization Under Uncertainty written by A. J. King and published by . This book was released on 1992 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Portfolio Optimization in a Downside Risk Framework written by Lars Huelin and published by LAP Lambert Academic Publishing. This book was released on 2011-04 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present study examines how downside risk measures perform in an investment management context compared to variance or standard deviation. To our knowledge, this paper is the first to include several acknowledged downside risk measures in a thorough analysis where their different properties are compared with those of variance Risk is an essential factor to consider when investing in the capital markets. The question of how one should define and manage risk is one that has gained a lot of attention and remains a popular topic in both the academic and professional world. This study considers six different downside risk measures and tests their relationship with the cross-section of returns as well as their performance in portfolio optimization compared to variance. The first part of the analysis suggests that the conditional drawdown-at-risk explains the cross-section of returns the best across methodologies and data frequency. Conditional valueat- risk explains the daily returns the best but the worst in monthly returns. Variance, together with semivariance, perform average in both data frequencies. The second part of the analysis concludes that conditional value-at-risk and conditional drawdown-at-risk are the two superior risk measures whereas semivariance is the worst performing risk measure - mainly caused by the poor performance during bull markets. Again, variance performs average compared to the downside risk measures in most aspects of this analysis. Overall, this thesis shows that the choice of risk measure has a significant effect on the portfolio optimization process. The analysis suggests that some downside risk measures outperform variance while others fail to do so. This suggest that downside risk can be a better tool in investment management than variance.
Download or read book Applying Particle Swarm Optimization written by Burcu Adıgüzel Mercangöz and published by Springer Nature. This book was released on 2021-05-13 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains the theoretical structure of particle swarm optimization (PSO) and focuses on the application of PSO to portfolio optimization problems. The general goal of portfolio optimization is to find a solution that provides the highest expected return at each level of portfolio risk. According to H. Markowitz’s portfolio selection theory, as new assets are added to an investment portfolio, the total risk of the portfolio’s decreases depending on the correlations of asset returns, while the expected return on the portfolio represents the weighted average of the expected returns for each asset. The book explains PSO in detail and demonstrates how to implement Markowitz’s portfolio optimization approach using PSO. In addition, it expands on the Markowitz model and seeks to improve the solution-finding process with the aid of various algorithms. In short, the book provides researchers, teachers, engineers, managers and practitioners with many tools they need to apply the PSO technique to portfolio optimization.
Download or read book Portfolio Optimization with Tail Risk Measures and Non normal Returns written by Minfeng Zhu and published by . This book was released on 2010 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Relaxed Optimization Approach for Cardinality Constrained Portfolios written by Jize Zhang and published by . This book was released on 2019 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: A cardinality-constrained portfolio caps the number of stocks to be traded across and within groups or sectors. These limitations arise from real-world scenarios faced by fund managers, who are constrained by transaction costs and client preferences as they seek to maximize return and limit risk. We develop a new approach to solve cardinality-constrained portfolio optimization problems, extending both Markowitz and conditional value at risk (CVaR) optimization models with cardinality constraints. We derive a continuous relaxation method for the NP-hard objective, which allows for very efficient algorithms with standard convergence guarantees for nonconvex problems. For smaller cases, where brute force search is feasible to compute the globally optimal cardinality-constrained portfolio, the new approach finds the best portfolio for the cardinality-constrained Markowitz model and a very good local minimum for the cardinality-constrained CVaR model. For higher dimensions, where brute-force search is prohibitively expensive, we find feasible portfolios that are nearly as efficient as their non-cardinality constrained counterparts.
Download or read book Portfolio Optimization with Quasiconvex Risk Measures written by Elisa Mastrogiacomo and published by . This book was released on 2013 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we focus on the portfolio optimization problem associated to a quasiconvex risk measure (satisfying some additional assumptions). For coherent/convex risk measures, the portfolio optimization problem has been already studied by Gaivoronski and Pflug (2005), Rockafellar and Uryasev (2000) and Ruszczynski and Shapiro (2006), among others. Following the approach of Ruszczynski and Shapiro (2006) but by means of quasiconvex analysis and notions of subdifferentiability, we characterize optimal solutions of the portfolio problem associated to quasiconvex risk measures. The shape of the efficient frontier in the mean-risk space and some particular cases are also investigated.
Download or read book Constrained Portfolio Optimization Under Minimax Risk Measure written by Chun Hung Chiu and published by . This book was released on 2000 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Portfolio Optimization Using Fundamental Indicators Based on Multi Objective EA written by Antonio Daniel Silva and published by Springer. This book was released on 2016-02-11 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents a new approach to portfolio composition in the stock market. It incorporates a fundamental approach using financial ratios and technical indicators with a Multi-Objective Evolutionary Algorithms to choose the portfolio composition with two objectives the return and the risk. Two different chromosomes are used for representing different investment models with real constraints equivalents to the ones faced by managers of mutual funds, hedge funds, and pension funds. To validate the present solution two case studies are presented for the SP&500 for the period June 2010 until end of 2012. The simulations demonstrates that stock selection based on financial ratios is a combination that can be used to choose the best companies in operational terms, obtaining returns above the market average with low variances in their returns. In this case the optimizer found stocks with high return on investment in a conjunction with high rate of growth of the net income and a high profit margin. To obtain stocks with high valuation potential it is necessary to choose companies with a lower or average market capitalization, low PER, high rates of revenue growth and high operating leverage
Download or read book Dynamic Mean Risk Portfolio Selection with Multiple Risk Measures in Continuous Time written by Jianjun Gao and published by . This book was released on 2014 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: Different risk measures emphasize different aspects of a random loss. If we examine the investment performance according to different spectra of the risk measures, any policy generated from a mean-risk portfolio model with a sole risk measure may not be a good choice. We study in this paper the dynamic portfolio selection problem with multiple risk measures in a continuous-time setting. More specifically, we investigate the dynamic mean-variance-CVaR (Conditional value at Risk) formulation and the dynamic mean-variance-SFP (Safety-First-Principle) formulation, and derive analytical solutions for both problems, when all the market parameters are deterministic. Combining a downside risk measure with the variance (the second order central moment) in a dynamic mean-risk portfolio selection model helps investors control both the symmetric central risk measure and the asymmetric downside risk at the tail part of the loss. We find that the optimal portfolio policy derived from our mean-multiple risk portfolio optimization model exhibits a feature of two-side threshold type, i.e., when the current wealth level is either below or above certain threshold, the optimal policy would dictate an increase in the allocation of the risky assets. Our numerical experiments using real market data further demonstrate that our dynamic mean-multiple risk portfolio models reduce significantly both the variance and the downside risk, when compared with the static buy-and-hold portfolio policy.
Download or read book The Use of Risk Measures and Its Applications in Portfolio Optimisation written by Resham Sivnarain and published by . This book was released on 2017 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, we study the application of risk measures to portfolio optimisation. A risk measure is a functional over the set of random portfolio returns mappings . We present the various risk measures in this dissertation within an axiomatic framework. Although Value-at-Risk (VaR) has been widely used, the Conditional-Value-at-Risk (CVaR) has become the more popular risk measure since it is a coherent and convex risk measure. We solve a CVaR based optimisation model that is used for portfolio optimisation and hedging a target portfolio. Additionally, we solve a CVaR based optimisation model with cost considerations included in the objective function. Further, we include alternative risk measures such as distortion, spectral, drawdown and coherent-distortion risk measures (CDRM) and develop optimisation problems for each risk measure as either the objective function or as a constraint in a linear programming problem. Since the 2008 crisis era, it has become important to note the universal agreement that financial assets have fat tails and that financial and investment managers must be able to account for it in their risk management strategies. We present fat-tail analysis for CVaR optimisation problems and perfom emperical risk analysis on the FTSE/JSE ALSI index.
Download or read book On a Portfolio Optimization Problem Under GARCH Type Models and Solvency Constraints written by Jiachen Wang and published by . This book was released on 2017 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis discusses the portfolio optimization problem under solvency constraints, based on S. Asanga et al. "Portfolio optimization under solvency constraints: a dynamical approach"[1]. Models of the gross return process and claim liability, and methods to solve the optimization problems are both borrowed from the paper. Under the background that insurance companies make profits mainly by investing the premiums and capital from shareholders on portfolio products, this thesis models the net loss of a company by the difference between gross return process of the investment and claim payments. The data used in this thesis are with the same period as the paper but downloaded from Yahoo, so there are subtle differences. The goal is to minimize the capital from shareholders under certain constraints built on three risk measures: ruin probability, conditional Value-at-Risk and expected policyholder deficit. We use univariate GARCH(1,1), Constant Conditional Correlation(CCC)-GARCH and Dynamic Conditional Correlation(DCC)-GARCH to model the gross return process considering volatility clustering and the covariance matrix of multiple assets in a portfolio. The claim's payments are modeled by a lognormal distribution to guarantee the convexity of the optimization problems. The approach to solving the constrained optimization problems is nonlinear interior point method and analyzing efficient frontier. This thesis seeks to solve it using r, by trying package 'nloptr' for constrained optimization, unfortunately, some functions have some bugs and others do not get a good result.
Download or read book Portfolio Optimization with Drawdown Constraints written by Alexei Valerievich Chekhlov and published by . This book was released on 2000 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt:
