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Financial risk modelling and portfolio optimization with R /
Introduces the latest techniques advocated for measuring financial market risk and portfolio optimisation, 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 Optimisation with R: Demonstrates...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Chichester, West Sussex, UK :
John Wiley & Sons,
2013.
|
| Colección: | Statistics in practice.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Pfaff, Bernhard. | |
| 245 | 1 | 0 | |a Financial risk modelling and portfolio optimization with R / |c Bernhard Pfaff. |
| 260 | |a Chichester, West Sussex, UK : |b John Wiley & Sons, |c 2013. | ||
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Statistics in practice | |
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 505 | 0 | 0 | |g Machine generated contents note: |g pt. I |t MOTIVATION -- |g 1. |t Introduction -- |t Reference -- |g 2. |t A brief course in R -- |g 2.1. |t Origin and development -- |g 2.2. |t Getting help -- |g 2.3. |t Working with R -- |g 2.4. |t Classes, methods and functions -- |g 2.5. |t The accompanying package FRAPO -- |t References -- |g 3. |t Financial market data -- |g 3.1. |t Stylized facts on financial market returns -- |g 3.1.1. |t Stylized facts for univariate series -- |g 3.1.2. |t Stylized facts for multivariate series -- |g 3.2. |t Implications for risk models -- |t References -- |g 4. |t Measuring risks -- |g 4.1. |t Introduction -- |g 4.2. |t Synopsis of risk measures -- |g 4.3. |t Portfolio risk concepts -- |t References -- |g 5. |t Modern portfolio theory -- |g 5.1. |t Introduction -- |g 5.2. |t Markowitz portfolios -- |g 5.3. |t Empirical mean-variance portfolios -- |t References -- |g pt. II |t RISK MODELLING -- |g 6. |t Suitable distributions for returns -- |g 6.1. |t Preliminaries -- |g 6.2. |t The generalized hyperbolic distribution -- |g 6.3. |t The generalized lambda distribution -- |g 6.4. |t Synopsis of R packages for the GHD -- |g 6.4.1. |t The package fBasics -- |g 6.4.2. |t The package GeneralizedHyperbolic -- |g 6.4.3. |t The package ghyp -- |g 6.4.4. |t The package QRM -- |g 6.4.5. |t The package SkewHyperbolic -- |g 6.4.6. |t The package VarianceGamma -- |g 6.5. |t Synopsis of R packages for GLD -- |g 6.5.1. |t The package Davies -- |g 6.5.2. |t The package fBasics -- |g 6.5.3. |t The package gld -- |g 6.5.4. |t The package lmomco -- |g 6.6. |t Applications of the GHD to risk modelling -- |g 6.6.1. |t Fitting stock returns to the GHD -- |g 6.6.2. |t Risk assessment with the GHD -- |g 6.6.3. |t Stylized facts revisited -- |g 6.7. |t Applications of the GLD to risk modelling and data analysis -- |g 6.7.1. |t VaR for a single stock -- |g 6.7.2. |t Shape triangle for FTSE 100 constituents -- |t References -- |g 7. |t Extreme value theory -- |g 7.1. |t Preliminaries -- |g 7.2. |t Extreme value methods and models -- |g 7.2.1. |t The block maxima approach -- |g 7.2.2. |t rth largest order models -- |g 7.2.3. |t The peaks-over-threshold approach -- |g 7.3. |t Synopsis of R packages -- |g 7.3.1. |t The package evd -- |g 7.3.2. |t The package evdbayes -- |g 7.3.3. |t The package evir -- |g 7.3.4. |t The package fExtremes -- |g 7.3.5. |t The packages ismev and extRemes -- |g 7.3.6. |t The package POT -- |g 7.3.7. |t The package QRM -- |g 7.3.8. |t The package Renext -- |g 7.4. |t Empirical applications of EVT -- |g 7.4.1. |t Section outline -- |g 7.4.2. |t Block maxima model for Siemens -- |g 7.4.3. |t r block maxima model for BMW -- |g 7.4.4. |t POT method for Boeing -- |t References -- |g 8. |t Modelling volatility -- |g 8.1. |t Preliminaries -- |g 8.2. |t The class of ARCH models -- |g 8.3. |t Synopsis of R packages -- |g 8.3.1. |t The package bayesGARCH -- |g 8.3.2. |t The package ccgarch -- |g 8.3.3. |t The package fGarch -- |g 8.3.4. |t The package gogarch -- |g 8.3.5. |t The packages rugarch and rmgarch -- |g 8.3.6. |t The package tseries -- |g 8.4. |t Empirical application of volatility models -- |t References -- |g 9. |t Modelling dependence -- |g 9.1. |t Overview -- |g 9.2. |t Correlation, dependence and distributions -- |g 9.3. |t Copulae -- |g 9.3.1. |t Motivation -- |g 9.3.2. |t Correlations and dependence revisited -- |g 9.3.3. |t Classification of copulae -- |g 9.4. |t Synopsis of R packages -- |g 9.4.1. |t The package BLCOP -- |g 9.4.2. |t The packages copula and nacopula -- |g 9.4.3. |t The package fCopulae -- |g 9.4.4. |t The package gumbel -- |g 9.4.5. |t The package QRM -- |g 9.5. |t Empirical applications of copulae -- |g 9.5.1. |t GARCH-copula model -- |g 9.5.2. |t Mixed copula approaches -- |t References -- |g pt. III |t PORTFOLIO OPTIMIZATION APPROACHES -- |g 10. |t Robust portfolio optimization -- |g 10.1. |t Overview -- |g 10.2. |t Robust statistics -- |g 10.2.1. |t Motivation -- |g 10.2.2. |t Selected robust estimators -- |g 10.3. |t Robust optimization -- |g 10.3.1. |t Motivation -- |g 10.3.2. |t Uncertainty sets and problem formulation -- |g 10.4. |t Synopsis of R packages -- |g 10.4.1. |t The package covRobust -- |g 10.4.2. |t The package fPortfolio -- |g 10.4.3. |t The package MASS -- |g 10.4.4. |t The package robustbase -- |g 10.4.5. |t The package robust -- |g 10.4.6. |t The package rrcov -- |g 10.4.7. |t The package Rsocp -- |g 10.5. |t Empirical applications -- |g 10.5.1. |t Portfolio simulation: Robust versus classical statistics -- |g 10.5.2. |t Portfolio back-test: Robust versus classical statistics -- |g 10.5.3. |t Portfolio back-test: Robust optimization -- |t References -- |g 11. |t Diversification reconsidered -- |g 11.1. |t Introduction -- |g 11.2. |t Most diversified portfolio -- |g 11.3. |t Risk contribution constrained portfolios -- |g 11.4. |t Optimal tail-dependent portfolios -- |g 11.5. |t Synopsis of R packages -- |g 11.5.1. |t The packages DEoptim and RcppDE -- |g 11.5.2. |t The package FRAPO -- |g 11.5.3. |t The package PortfolioAnalytics -- |g 11.6. |t Empirical applications -- |g 11.6.1. |t Comparison of approaches -- |g 11.6.2. |t Optimal tail-dependent portfolio against benchmark -- |g 11.6.3. |t Limiting contributions to expected shortfall -- |t References -- |g 12. |t Risk-optimal portfolios -- |g 12.1. |t Overview -- |g 12.2. |t Mean-VaR portfolios -- |g 12.3. |t Optimal CVaR portfolios -- |g 12.4. |t Optimal draw-down portfolios -- |g 12.5. |t Synopsis of R packages -- |g 12.5.1. |t The package fPortfolio -- |g 12.5.2. |t The package FRAPO -- |g 12.5.3. |t Packages for linear programming -- |g 12.5.4. |t The package PerformanceAnalytics -- |g 12.6. |t Empirical applications -- |g 12.6.1. |t Minimum-CVaR versus minimum-variance portfolios -- |g 12.6.2. |t Draw-down constrained portfolios -- |g 12.6.3. |t Back-test comparison for stock portfolio -- |t References -- |g 13. |t Tactical asset allocation -- |g 13.1. |t Overview -- |g 13.2. |t Survey of selected time series models -- |g 13.2.1. |t Univariate time series models -- |g 13.2.2. |t Multivariate time series models -- |g 13.3. |t Black-Litterman approach -- |g 13.4. |t Copula opinion and entropy pooling -- |g 13.4.1. |t Introduction -- |g 13.4.2. |t The COP model -- |g 13.4.3. |t The EP model -- |g 13.5. |t Synopsis of R packages -- |g 13.5.1. |t The package BLCOP -- |g 13.5.2. |t The package dse -- |g 13.5.3. |t The package fArma -- |g 13.5.4. |t The package forecast -- |g 13.5.5. |t The package MSBVAR -- |g 13.5.6. |t The package PairTrading -- |g 13.5.7. |t The packages urca and vars -- |g 13.6. |t Empirical applications -- |g 13.6.1. |t Black-Litterman portfolio optimization -- |g 13.6.2. |t Copula opinion pooling -- |g 13.6.3. |t Protection strategies -- |t References -- |g Appendix |t A Package overview -- |g A.1. |t Packages in alphabetical order -- |g A.2. |t Packages ordered by topic -- |t References -- |g Appendix B |t Time series data -- |g B.1. |t Date-time classes -- |g B.2. |t The ts class in the base package stats -- |g B.3. |t Irregular-spaced time series -- |g B.4. |t The package timeSeries -- |g B.5. |t The package zoo -- |g B.6. |t The packages tframe and xts -- |t References -- |g Appendix C |t Back-testing and reporting of portfolio strategies -- |g C.1. |t R packages for back-testing -- |g C.2. |t R facilities for reporting -- |g C.3. |t Interfacing databases -- |t References -- |g Appendix D |t Technicalities. |
| 520 | |a Introduces the latest techniques advocated for measuring financial market risk and portfolio optimisation, 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 Optimisation with R: Demonstrates techniques in modelling financial risks and applying portfolio optimisation techniques as well as recent advances in the field. Introduces stylised facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalised hyperbolic distribution, volatility modelling and concepts for capturing dependencies. Explores portfolio risk concepts and optimisation 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 optimisation will find this book beneficial. It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Financial risk |x Mathematical models. | |
| 650 | 0 | |a Portfolio management. | |
| 650 | 0 | |a R (Computer program language) | |
| 650 | 6 | |a Risque financier |x Modèles mathématiques. | |
| 650 | 6 | |a Gestion de portefeuille. | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Finance. |2 bisacsh | |
| 650 | 7 | |a Portfolio management |2 fast | |
| 650 | 7 | |a R (Computer program language) |2 fast | |
| 650 | 7 | |a Portföljförvaltning. |2 sao | |
| 650 | 7 | |a R (programspråk) |2 sao | |
| 776 | 0 | 8 | |i Print version: |a Pfaff, Bernhard. |t Financial risk modelling and portfolio optimization with R. |d Hoboken, N.J. : Wiley, 2013 |z 9780470978702 |w (DLC) 2012030904 |
| 830 | 0 | |a Statistics in practice. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781118477120/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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