Financial Risk Modelling and Portfolio Optimization with R
Financial Risk Modelling and Portfolio Optimization with R

Author: Bernhard Pfaff

Publisher: John Wiley & Sons

Published: 2016-08-16

Total Pages: 448

ISBN-13: 1119119685

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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.

Financial Risk Modelling and Portfolio Optimization with R
Financial Risk Modelling and Portfolio Optimization with R

Author: Bernhard Pfaff

Publisher: John Wiley & Sons

Published: 2012-11-05

Total Pages: 309

ISBN-13: 111847712X

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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. 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. Enables the reader to replicate the results in the book using R code. Is accompanied by a supporting website featuring examples and case studies in R. 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.

Financial Risk Modelling and Portfolio Optimization with R
Financial Risk Modelling and Portfolio Optimization with R

Author: Bernhard Pfaff

Publisher: John Wiley & Sons

Published: 2016-08-22

Total Pages: 448

ISBN-13: 1119119677

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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.

Portfolio Risk Analysis
Portfolio Risk Analysis

Author: Gregory Connor

Publisher: Princeton University Press

Published: 2010-03-15

Total Pages: 400

ISBN-13: 1400835291

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Portfolio risk forecasting has been and continues to be an active research field for both academics and practitioners. Almost all institutional investment management firms use quantitative models for their portfolio forecasting, and researchers have explored models' econometric foundations, relative performance, and implications for capital market behavior and asset pricing equilibrium. Portfolio Risk Analysis provides an insightful and thorough overview of financial risk modeling, with an emphasis on practical applications, empirical reality, and historical perspective. Beginning with mean-variance analysis and the capital asset pricing model, the authors give a comprehensive and detailed account of factor models, which are the key to successful risk analysis in every economic climate. Topics range from the relative merits of fundamental, statistical, and macroeconomic models, to GARCH and other time series models, to the properties of the VIX volatility index. The book covers both mainstream and alternative asset classes, and includes in-depth treatments of model integration and evaluation. Credit and liquidity risk and the uncertainty of extreme events are examined in an intuitive and rigorous way. An extensive literature review accompanies each topic. The authors complement basic modeling techniques with references to applications, empirical studies, and advanced mathematical texts. This book is essential for financial practitioners, researchers, scholars, and students who want to understand the nature of financial markets or work toward improving them.

Numerical Methods and Optimization in Finance
Numerical Methods and Optimization in Finance

Author: Manfred Gilli

Publisher: Academic Press

Published: 2019-08-16

Total Pages: 638

ISBN-13: 0128150653

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Computationally-intensive tools play an increasingly important role in financial decisions. Many financial problems-ranging from asset allocation to risk management and from option pricing to model calibration-can be efficiently handled using modern computational techniques. Numerical Methods and Optimization in Finance presents such computational techniques, with an emphasis on simulation and optimization, particularly so-called heuristics. This book treats quantitative analysis as an essentially computational discipline in which applications are put into software form and tested empirically. This revised edition includes two new chapters, a self-contained tutorial on implementing and using heuristics, and an explanation of software used for testing portfolio-selection models. Postgraduate students, researchers in programs on quantitative and computational finance, and practitioners in banks and other financial companies can benefit from this second edition of Numerical Methods and Optimization in Finance.

Risk Analysis and Portfolio Modelling
Risk Analysis and Portfolio Modelling

Author: Elisa Luciano

Publisher: MDPI

Published: 2019-10-16

Total Pages: 224

ISBN-13: 3039216244

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Financial Risk Measurement is a challenging task, because both the types of risk and the techniques evolve very quickly. This book collects a number of novel contributions to the measurement of financial risk, which address either non-fully explored risks or risk takers, and does so in a wide variety of empirical contexts.

Stochastic Optimization
Stochastic Optimization

Author: Stanislav Uryasev

Publisher: Springer Science & Business Media

Published: 2013-03-09

Total Pages: 438

ISBN-13: 1475765940

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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.

Risk and Portfolio Analysis
Risk and Portfolio Analysis

Author: Henrik Hult

Publisher: Springer Science & Business Media

Published: 2012-07-20

Total Pages: 343

ISBN-13: 146144103X

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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.

Linear and Mixed Integer Programming for Portfolio Optimization
Linear and Mixed Integer Programming for Portfolio Optimization

Author: Renata Mansini

Publisher: Springer

Published: 2015-06-10

Total Pages: 131

ISBN-13: 3319184822

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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.