Download or read book A Modified Mean variance conditional Value at Risk Model of Multi objective Portfolio Optimization with an Application in Finance written by Younes Elahi and published by . This book was released on 2014 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Operations Research Proceedings 2011 written by Diethard Klatte and published by Springer Science & Business Media. This book was released on 2012-06-07 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains a selection of refereed papers presented at the “International Conference on Operations Research (OR 2011)” which took place at the University of Zurich from August 30 to September 2, 2011. The conference was jointly organized by the German speaking OR societies from Austria (ÖGOR), Germany (GOR) and Switzerland (SVOR) under the patronage of SVOR. More than 840 scientists and students from over 50 countries attended OR 2011 and presented 620 papers in 16 parallel topical streams, as well as special award sessions. The conference was designed according to the understanding of Operations Research as an interdisciplinary science focusing on modeling complex socio-technical systems to gain insight into behavior under interventions by decision makers. Dealing with “organized complexity” lies in the core of OR and designing useful support systems to master the challenge of system management in complex environment is the ultimate goal of our professional societies. To this end, algorithmic techniques and system modeling are two fundamental competences which are also well-balanced in these proceedings.

Download or read book Gauging Risk with Higher Moments written by Gyöngyi Bugár and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 INFORMS Annual Meeting written by Institute for Operations Research and the Management Sciences. National Meeting and published by . This book was released on 2006 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The VaR Modeling Handbook Practical Applications in Alternative Investing Banking Insurance and Portfolio Management written by Greg N. Gregoriou and published by McGraw Hill Professional. This book was released on 2010-02-22 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: Value-at-Risk (VaR) is a powerful tool for assessing market risk in real time—a critical insight when making trading and hedging decisions. The VaR Modeling Handbook is the most complete, up-to-date reference on the subject for today’s savvy investors, traders, portfolio managers, and other asset and risk managers. Unlike market risk metrics such as the Greeks, or beta, which are applicable to only certain asset categories and sources of market risk, VaR is applicable to all liquid assets, making it a reliable indicator of total market risk. For this reason, among many others, VaR has become the dominant method for estimating precisely how much money is at risk each day in the financial markets. The VaR Modeling Handbook is a profound volume that delivers practical information on measuring and modeling risk specifically focused on alternative investments, banking, and the insurance sector. The perfect primer to The VaR Implementation Handbook (McGraw- Hill), this foundational resource features The experience of 40 internationally recognized experts Useful perspectives from a wide range of practitioners, researchers, and academics Coverage on applying VaR to hedge fund strategies, microcredit loan portfolios, and economic capital management approaches for insurance companies Each illuminating chapter in The VaR Modeling Handbook presents a specific topic, complete with an abstract and conclusion for quick reference, as well as numerous illustrations that exemplify covered material. Practitioners can gain in-depth, cornerstone knowledge of VaR by reading the handbook cover to cover or take advantage of its user-friendly format by using it as a go-to resource in the real world. Financial success in the markets requires confident decision making, and The VaR Modeling Handbook gives you the knowledge you need to use this state-of-the-art modeling method to successfully manage financial risk.

Download or read book Computational Intelligence Applications in Business Intelligence and Big Data Analytics written by Vijayan Sugumaran and published by CRC Press. This book was released on 2017-06-26 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms. This book sets its focus on CI based architectures, modeling, case studies and applications in big data analytics, and business intelligence. The intended audiences of this book are scientists, professionals, researchers, and academicians who deal with the new challenges and advances in the specific areas mentioned above. Designers and developers of applications in these areas can learn from other experts and colleagues through this book.

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 Multi Period Portfolio Optimization Model with Cone Constraints and Discrete Decisions written by Ümit Saglam and published by . This book was released on 2019 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study, we consider multi-period portfolio optimization model that is formulated as a mixed-integer second-order cone programming problems (MISOCPs). The Markowitz (1952) mean/variance framework has been extended by including transaction costs, conditional value-at-risk (CVaR), diversification-by-sector and buy-in thresholds constraints. The model is obtained using a binary scenario tree that is constructed with monthly returns of the stocks from the S&P 500. We solve these models with a MATLAB based Mixed Integer Linear and Nonlinear Optimizer (MILANO). Numerical results show that we can solve small to medium-sized instances successfully, and we provide a substantial improvement in runtimes using warmstarts in outer approximation algorithm.

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 Application of a General Risk Management Model to Portfolio Optimization Problems with Elliptical Distributed Returns for Risk Neutral and Risk Averse Decision Makers written by B. Kaynar and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper portfolio problems with linear loss functions and multivariate elliptical distributed returns are studied. We consider two risk measures, Value-at-Risk and Conditional-Value-at-Risk, and two types of decision makers, risk neutral and risk averse. For Value-at-Risk, we show that the optimal solution does not change with the type of decision maker. However, this observation is not true for Conditional-Value-at-Risk. We then show for Conditional-Value-at-Risk that the objective function can be approximated by Monte Carlo simulation using only a univariate distribution. To solve the equivalent Markowitz model, we modify and implement a finite step algorithm. Finally, a numerical study is conducted.

Download or read book Optimal Portfolio with Options in a Framework of Mean CVaR written by Binh Dao and published by . This book was released on 2014 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: TheModern Portfolio Theory has its benchmark which is theMarkowitz's Mean-Variance portfolio optimisation. However the Markowitz's Mean-Variance optimisation of portfolios with options (even simple products such as call or put) is a particularly difficult task, as the asymmetric returns of options and financial products with embedded options require the use of asymmetric risk measures. Furthermore, returns of options are a positive function of the standard deviation of the return of the underlying assets; the variance becomes not an appropriate risk measure for portfolio with options. Recently, Value at Risk (VaR) and Conditional Value at Risk (CVaR) have gained acceptance in world financial markets as appropriate risk measures in risk management. Furthermore, CVaR has advantage to be a coherent risk measure. We analyse the problem of computing the optimal portfolios in a framework of Mean-CVaR. Simulation methods are considered the appropriate choices, especially when portfolios with options or instruments with embedded options are analyzed. We present the optimal frontier Mean-CVaR for an application of a portfolio composed of four correlated assets and two calls and two puts. Comparison is made with the Mean-Variance and Mean-Variance approach for this application portfolio.

Download or read book Conditional Value at Risk Robust Optimization written by P.A Nguyen and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose using the well-known conditional value at risk (CVaR) risk measure as a new methodology for incorporating robustness into portfolio optimization. Robustness in portfolio optimization can address the poor out-of-sample performance of the classical mean-variance optimization problems. Using many estimates of the covariance matrix and mean return vector, we incorporate robustness by finding a portfolio that performs well, on average, for a worst-case subset of these estimates, rather than for a single estimate. This becomes a bilevel integer program that we reformulate into a tractable form under appropriate conditions. We present numerical results that compares this CVaR robust method to a distributionally robust optimization approach that uses the Wasserstein metric to measure robustness. Theoretically, we extend the existing work of stochastic programming by linking the CVaR robustness to the sample average approximation. Specifically, we show that the CVaR robustness problem provides an upper bound, in expectation, to stochastic programming problems. We derive various asymptotic convergence results.

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 Does Mean CVaR Outperform Mean Variance Theoretical and Practical Perspectives written by Linh Xuan Diep Nguyen and published by . This book was released on 2018 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper provides a systematic investigation of the relative performance between the two mainstream portfolio optimisation methods: mean-variance and mean-Conditional Value-at-Risk (CVaR) from both theoretical and practical perspectives. Using portfolios representing the entire US stock market, we confirm the theoretical outperformance of mean-CVaR in the frictionless market. We explain the popularity of mean-variance in a practical investment context by showing that the superiority of mean-CVaR disappears when simple historical sample inputs and transaction costs are incorporated. We further reveal that the relative performance between the two optimisation methods is significantly influenced by the characteristics of the constituent stocks in the portfolio as well as the stock market condition. Our findings have important implications in shaping investment decisions.

Download or read book Probabilistic Constrained Optimization written by Stanislav Uryasev and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic and percentile/quantile functions play an important role in several applications, such as finance (Value-at-Risk), nuclear safety, and the environment. Recently, significant advances have been made in sensitivity analysis and optimization of probabilistic functions, which is the basis for construction of new efficient approaches. This book presents the state of the art in the theory of optimization of probabilistic functions and several engineering and finance applications, including material flow systems, production planning, Value-at-Risk, asset and liability management, and optimal trading strategies for financial derivatives (options). Audience: The book is a valuable source of information for faculty, students, researchers, and practitioners in financial engineering, operation research, optimization, computer science, and related areas.

Download or read book Portfolio Optimization Under Conditional Value at Risk Constraints written by Yannick Paukner and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: