Download or read book Portfolio Optimization Under Expected Shortfall written by and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bias Variance Trade Off in Portfolio Optimization Under Expected Shortfall with L2 Regularization written by Gabor Papp and published by . This book was released on 2017 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: The optimization of a large random portfolio under the Expected Shortfall risk measure with an l2 regularizer is carried out by analytical calculation. The regularizer reins in the large sample fluctuations and the concomitant divergent estimation error, and eliminates the phase transition where this error would otherwise blow up. In the data-dominated region, where the number of different assets in the portfolio is much less than the length of the available time series, the regularizer plays a negligible role, while in the opposite limit (which occurs much more frequently in practice), where the size of samples is comparable to, or even smaller than the number of assets, the optimum is almost entirely determined by the regularizer. Our results show that the transition region between these two extremes is relatively narrow, and it is only here that one can meaningfully speak of a trade-off between fluctuations and bias. The cause of both the anomalously large fluctuations and the limited usefulness of regularization is the unboundedness of Expected Shortfall as a loss function, a property it shares with all the other coherent measures, but also with downside risk measures in general, including Value at Risk.
Download or read book Empirical Analysis of Value at Risk and Expected Shortfall in Portfolio Selection Problem written by Liyuan Ding and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Safety first criterion and mean-shortfall criterion both explore cases of assets allocation with downside risk. In this paper, I compare safety first portfolio selection problem and mean-shortfall portfolio optimization problem, considering risk averse investors in practice. Safety first portfolio selection uses Value at Risk (VaR) as a risk measure, and mean-shortfall portfolio optimization uses expected shortfall as a risk measure, respectively. VaR is estimated by implementing extreme theory using a semi-parametric method. Expected shortfall is estimated by two nonparametric methods: a natural estimation and a kernel-weighted estimation. I use daily data on three international stock indices, ranging from January 1986 to February 2012, to provide empirical evidence in asset allocations and illustrate the performances of safety first and mean-shortfall with their risk measures. Also, the historical data has been divided in two ways. One is truncated at year 1998 and explored the performance during tech boom and financial crisis. the mean-shortfall portfolio optimization with the kernel-weighted method performed better than the safety first criterion, while the safety first criterion was better than the mean-shortfall portfolio optimization with the natural estimation method. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/148430
Download or read book Portfolio Optimization and Performance Analysis written by Jean-Luc Prigent and published by CRC Press. This book was released on 2007-05-07 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: In answer to the intense development of new financial products and the increasing complexity of portfolio management theory, Portfolio Optimization and Performance Analysis offers a solid grounding in modern portfolio theory. The book presents both standard and novel results on the axiomatics of the individual choice in an uncertain framework, cont
Download or read book Portfolio Optimization Under Volatility and Shortfall Constraints written by Dieter Kalin and published by . This book was released on 1995 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computational Methods in Decision Making Economics and Finance written by Erricos John Kontoghiorghes and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 626 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computing has become essential for the modeling, analysis, and optimization of systems. This book is devoted to algorithms, computational analysis, and decision models. The chapters are organized in two parts: optimization models of decisions and models of pricing and equilibria.
Download or read book Financial Risk Modelling and Portfolio Optimization with R written by Bernhard Pfaff and published by John Wiley & Sons. This book was released on 2016-08-16 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial Risk Modelling and Portfolio Optimization with R, 2nd Edition Bernhard Pfaff, Invesco Global Asset Allocation, Germany A must have text for risk modelling and portfolio optimization using R. This book introduces the latest techniques advocated for measuring financial market risk and portfolio optimization, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book. This edition has been extensively revised to include new topics on risk surfaces and probabilistic utility optimization as well as an extended introduction to R language. Financial Risk Modelling and Portfolio Optimization with R: Demonstrates techniques in modelling financial risks and applying portfolio optimization techniques as well as recent advances in the field. Introduces stylized facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalized hyperbolic distribution, volatility modelling and concepts for capturing dependencies. Explores portfolio risk concepts and optimization with risk constraints. Is accompanied by a supporting website featuring examples and case studies in R. Includes updated list of R packages for enabling the reader to replicate the results in the book. Graduate and postgraduate students in finance, economics, risk management as well as practitioners in finance and portfolio optimization will find this book beneficial. It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study.
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 Stochastic Optimization written by Stanislav Uryasev and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic programming approaches have been successfully used in a number of areas such as energy and production planning, telecommunications, and transportation. Recently, the practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications including financial modeling, risk management, and probabilistic risk analysis. Major topics in this volume include: (1) advances in theory and implementation of stochastic programming algorithms; (2) sensitivity analysis of stochastic systems; (3) stochastic programming applications and other related topics. Audience: Researchers and academies working in optimization, computer modeling, operations research and financial engineering. The book is appropriate as supplementary reading in courses on optimization and financial engineering.
Download or read book Risk and Portfolio Analysis written by Henrik Hult and published by Springer Science & Business Media. This book was released on 2012-07-20 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: Investment and risk management problems are fundamental problems for financial institutions and involve both speculative and hedging decisions. A structured approach to these problems naturally leads one to the field of applied mathematics in order to translate subjective probability beliefs and attitudes towards risk and reward into actual decisions. In Risk and Portfolio Analysis the authors present sound principles and useful methods for making investment and risk management decisions in the presence of hedgeable and non-hedgeable risks using the simplest possible principles, methods, and models that still capture the essential features of the real-world problems. They use rigorous, yet elementary mathematics, avoiding technically advanced approaches which have no clear methodological purpose and are practically irrelevant. The material progresses systematically and topics such as the pricing and hedging of derivative contracts, investment and hedging principles from portfolio theory, and risk measurement and multivariate models from risk management are covered appropriately. The theory is combined with numerous real-world examples that illustrate how the principles, methods, and models can be combined to approach concrete problems and to draw useful conclusions. Exercises are included at the end of the chapters to help reinforce the text and provide insight. This book will serve advanced undergraduate and graduate students, and practitioners in insurance, finance as well as regulators. Prerequisites include undergraduate level courses in linear algebra, analysis, statistics and probability.
Download or read book Comparative Analyses of Expected Shortfall and VaR written by Yasuhiro Yamai and published by . This book was released on 2001 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: Expected shortfall is compared with Value-at-Risk (VaR) in three aspects: estimation errors, decomposition into risk factors, and optimization. Advantages and disadvantages of expected shortfall over VaR are shown, and that expected shortfall is easily decomposed (needing a larger size of sample than VaR for the same level of accuracy) and optimized, while VaR is not.
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 Financial Risk Modelling and Portfolio Optimization with R written by Bernhard Pfaff and published by John Wiley & Sons. This book was released on 2016-08-22 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial Risk Modelling and Portfolio Optimization with R, 2nd Edition Bernhard Pfaff, Invesco Global Asset Allocation, Germany A must have text for risk modelling and portfolio optimization using R. This book introduces the latest techniques advocated for measuring financial market risk and portfolio optimization, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book. This edition has been extensively revised to include new topics on risk surfaces and probabilistic utility optimization as well as an extended introduction to R language. Financial Risk Modelling and Portfolio Optimization with R: Demonstrates techniques in modelling financial risks and applying portfolio optimization techniques as well as recent advances in the field. Introduces stylized facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalized hyperbolic distribution, volatility modelling and concepts for capturing dependencies. Explores portfolio risk concepts and optimization with risk constraints. Is accompanied by a supporting website featuring examples and case studies in R. Includes updated list of R packages for enabling the reader to replicate the results in the book. Graduate and postgraduate students in finance, economics, risk management as well as practitioners in finance and portfolio optimization will find this book beneficial. It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study.
Download or read book Theory of Financial Risk and Derivative Pricing written by Jean-Philippe Bouchaud and published by Cambridge University Press. This book was released on 2003-12-11 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Risk control and derivative pricing have become of major concern to financial institutions, and there is a real need for adequate statistical tools to measure and anticipate the amplitude of the potential moves of the financial markets. Summarising theoretical developments in the field, this 2003 second edition has been substantially expanded. Additional chapters now cover stochastic processes, Monte-Carlo methods, Black-Scholes theory, the theory of the yield curve, and Minority Game. There are discussions on aspects of data analysis, financial products, non-linear correlations, and herding, feedback and agent based models. This book has become a classic reference for graduate students and researchers working in econophysics and mathematical finance, and for quantitative analysts working on risk management, derivative pricing and quantitative trading strategies.
Download or read book Portfolio Optimization Under Shortfall Constraints written by Martin L. Leibowitz and published by . This book was released on 1987 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The VAR Implementation Handbook Chapter 15 Risk Measures and Their Applications in Asset Management written by Greg N. Gregoriou and published by McGraw Hill Professional. This book was released on 2009-02-19 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: The following is a chapter from The VaR Implementation Handbook, which examines the latest strategies for measuring, managing, and modeling risk across a variety of applications. Packed with the insights, methods, and models that make experienced professionals competitive all over the world, this comprehensive guide features cutting-edge research and findings from some of the industry's most respected academics, practitioners, and consultants.
Download or read book On Relation between Expected Regret and Conditional Value at Risk written by Carlos E. Testuri and published by . This book was released on 2000 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: The paper compares portfolio optimization approaches with expected regret and Conditional Value-at-Risk (CVaR) utility functions. The expected regret is defined as an average portfolio underperformance comparing to a fixed target of some benchmark portfolio. By definition, CVaR is the mean of the worst x% portfolio losses in a specified time period. CVaR is also called Mean Excess Loss or Expected Shortfall. Recently, it was demonstrated that the optimization of CVaR can be performed using linear programming. We formally prove that a portfolio, which minimizes CVaR, can be obtained by doing a sensitivity analysis with respect to the threshold in the expected regret. An optimal portfolio in CVaR sense is also optimal in the expected regret sense for some threshold in the regret function. The inverse statement is also valid, i.e., if a portfolio minimizes the expected regret, this portfolio can be found by doing a sensitivity analysis with respect to the CVaR confidence level. A portfolio, optimal in expected regret sense, is also optimal in CVaR sense for some confidence level. The relation of the expected regret and CVaR minimization approaches is explained with a numerical example.
