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 Optimization Methods in Finance written by Gerard Cornuejols and published by Cambridge University Press. This book was released on 2006-12-21 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization models play an increasingly important role in financial decisions. This is the first textbook devoted to explaining how recent advances in optimization models, methods and software can be applied to solve problems in computational finance more efficiently and accurately. Chapters discussing the theory and efficient solution methods for all major classes of optimization problems alternate with chapters illustrating their use in modeling problems of mathematical finance. The reader is guided through topics such as volatility estimation, portfolio optimization problems and constructing an index fund, using techniques such as nonlinear optimization models, quadratic programming formulations and integer programming models respectively. The book is based on Master's courses in financial engineering and comes with worked examples, exercises and case studies. It will be welcomed by applied mathematicians, operational researchers and others who work in mathematical and computational finance and who are seeking a text for self-learning or for use with courses.
Download or read book Robustness of Conditional Value at risk CVaR when Measuring Market Risk Across Different Asset Classes written by and published by . This book was released on 2010 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Robust VaR and CVaR Optimization Under Joint Ambiguity in Distributions Means and Covariances written by Somayyeh Lotfi and published by . This book was released on 2018 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop robust models for optimization of the VaR and CVaR risk measures with a minimum expected return constraint under joint ambiguity in distribution, mean returns, and covariance matrix. We formulate models for ellipsoidal, polytopic, and interval ambiguity sets of the means and covariances. The models unify and/or extend several existing models. We also show how to overcome the well-known conservatism of robust optimization models by proposing an algorithm and a heuristic for constructing joint ellipsoidal ambiguity sets from point estimates given by multiple securities analysts. Using a controlled experiment we show how the well-known sensitivity of CVaR to mis-specifications of the first four moments of the distribution is alleviated with the robust models. Finally, applying the model to the active management of portfolios of sovereign CDS from Eurozone core and periphery, and Central, Eastern and South-Eastern Europe countries, we illustrate that investment strategies using robust optimization models perform well out-of-sample, even during the euro zone crisis. We consider both buy-and-hold and active management strategies.
Download or read book Distributionally Robust Optimization and its Applications in Power System Energy Storage Sizing written by Rui Xie and published by Springer Nature. This book was released on with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Necessary Conditions for an Extremum written by B.N. Pshenichnyi and published by CRC Press. This book was released on 2020-08-17 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a theory of necessary conditions for an extremum, including formal conditions for an extremum and computational methods. It states the general results of the theory and shows how these results can be particularized to specific problems.
Download or read book Optimization of Conditional Value at risk written by Stanislav Uryasev and published by . This book was released on 1999 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Closed Form Solutions for Worst Case Law Invariant Risk Measures with Application to Robust Portfolio Optimization written by Jonathan Li and published by . This book was released on 2016 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: Worst-case risk measures refer to the calculation of the largest value for risk measures when only partial information of the underlying distribution is available. For the popular risk measures such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), it is now known that their worst-case counterparts can be evaluated in closed form when only the first two moments are known for the underlying distribution. These results are remarkable since they not only simplify the use of worst-case risk measures but also provide great insight into the connection between the worst-case risk measures and existing risk measures. We show in this paper that somewhat surprisingly similar closed-form solutions also exist for the general class of law invariant coherent risk measures, which consists of spectral risk measures as special cases that are arguably the most important extensions of CVaR. We shed light on the one-to-one correspondence between a worst-case law invariant risk measure and a worst-case CVaR (and a worst-case VaR), which enables one to carry over the development of worst-case VaR in the context of portfolio optimization to the worst-case law invariant risk measures immediately.
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 Conditional Value at Risk Optimization for Credit Risk Using Asset Value Models written by Matthias Heidrich and published by . This book was released on 2012 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Risk Management written by M. A. H. Dempster and published by Cambridge University Press. This book was released on 2002-01-10 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of derivative products in risk management has spread from commodities, stocks and fixed income items, to such virtual commodities as energy, weather and bandwidth. All this can give rise to so-called volatility and there has been a consequent development in formal risk management techniques to cover all types of risk: market, credit, liquidity, etc. One of these techniques, Value at Risk, was developed specifically to help manage market risk over short periods. Its success led, somewhat controversially, to its take up and extension to credit risk over longer time-scales. This extension, ultimately not successful, led to the collapse of a number of institutions. The present book, which was originally published in 2002, by some of the leading figures in risk management, examines the complex issues that concern the stability of the global financial system by presenting a mix of theory and practice.
Download or read book Robust Optimization written by Aharon Ben-Tal and published by Princeton University Press. This book was released on 2009-08-10 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.
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 2016-08-16 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 Robust Portfolio Optimization with Multivariate Copulas written by Fernando B. Sabino da Silva and published by . This book was released on 2018 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using data from the S&P 500 stocks from 1990 to 2015, we address the uncertainty of distribution of assets' returns in Conditional Value-at-Risk (CVaR) minimization model by applying multidimensional mixed Archimedean copula function and obtaining its robust counterpart. We implement a dynamic investing viable strategy where the portfolios are optimized using three different length of rolling calibration windows. The out-of-sample performance is evaluated and compared against two benchmarks: a multidimensional Gaussian copula model and a constant mix portfolio. Our empirical analysis shows that the Mixed Copula-CVaR approach generates portfolios with better downside risk statistics for any rebalancing period and it is more profitable than the Gaussian Copula-CVaR and the 1/N portfolios for daily and weekly rebalancing. To cope with the dimensionality problem we select a set of assets that are the most diversified, in some sense, to the S&P 500 index in the constituent set. The accuracy of the VaR forecasts is determined by how well they minimize a capital requirement loss function. In order to mitigate data-snooping problems, we apply a test for superior predictive ability to determine which model significantly minimizes this expected loss function. We find that the minimum average loss of the mixed Copula-CVaR approach is smaller than the average performance of the Gaussian Copula-CVaR.
Download or read book Robustness Analysis in Decision Aiding Optimization and Analytics written by Michael Doumpos and published by Springer. This book was released on 2016-07-12 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad coverage of the recent advances in robustness analysis in decision aiding, optimization, and analytics. It offers a comprehensive illustration of the challenges that robustness raises in different operations research and management science (OR/MS) contexts and the methodologies proposed from multiple perspectives. Aside from covering recent methodological developments, this volume also features applications of robust techniques in engineering and management, thus illustrating the robustness issues raised in real-world problems and their resolution within advances in OR/MS methodologies. Robustness analysis seeks to address issues by promoting solutions, which are acceptable under a wide set of hypotheses, assumptions and estimates. In OR/MS, robustness has been mostly viewed in the context of optimization under uncertainty. Several scholars, however, have emphasized the multiple facets of robustness analysis in a broader OR/MS perspective that goes beyond the traditional framework, seeking to cover the decision support nature of OR/MS methodologies as well. As new challenges emerge in a “big-data'” era, where the information volume, speed of flow, and complexity increase rapidly, and analytics play a fundamental role for strategic and operational decision-making at a global level, robustness issues such as the ones covered in this book become more relevant than ever for providing sound decision support through more powerful analytic tools.
