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LEADER 00000cam a2200877 a 4500
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020 1118477138|q(electronic bk.)
020 9781118477120|q(electronic bk.)
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082 00 332.0285/5133
082 00 332.0285/5133|223
099 eBook O’Reilly for Public Libraries
100 1 Pfaff, Bernhard.
245 10 Financial risk modelling and portfolio optimization with R
/|cBernhard Pfaff.|h[O'Reilly electronic resource]
260 Chichester, West Sussex, UK :|bJohn Wiley & Sons,|c2013.
300 1 online resource
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
490 1 Statistics in practice
504 Includes bibliographical references and index.
505 00 |gMachine generated contents note:|gpt. I|tMOTIVATION --
|g1.|tIntroduction --|tReference --|g2.|tA brief course in
R --|g2.1.|tOrigin and development --|g2.2.|tGetting help
--|g2.3.|tWorking with R --|g2.4.|tClasses, methods and
functions --|g2.5.|tThe accompanying package FRAPO --
|tReferences --|g3.|tFinancial market data --|g3.1.
|tStylized facts on financial market returns --|g3.1.1.
|tStylized facts for univariate series --|g3.1.2.
|tStylized facts for multivariate series --|g3.2.
|tImplications for risk models --|tReferences --|g4.
|tMeasuring risks --|g4.1.|tIntroduction --|g4.2.
|tSynopsis of risk measures --|g4.3.|tPortfolio risk
concepts --|tReferences --|g5.|tModern portfolio theory --
|g5.1.|tIntroduction --|g5.2.|tMarkowitz portfolios --
|g5.3.|tEmpirical mean-variance portfolios --|tReferences
--|gpt. II|tRISK MODELLING --|g6.|tSuitable distributions
for returns --|g6.1.|tPreliminaries --|g6.2.|tThe
generalized hyperbolic distribution --|g6.3.|tThe
generalized lambda distribution --|g6.4.|tSynopsis of R
packages for the GHD --|g6.4.1.|tThe package fBasics --
|g6.4.2.|tThe package GeneralizedHyperbolic --|g6.4.3.
|tThe package ghyp --|g6.4.4.|tThe package QRM --|g6.4.5.
|tThe package SkewHyperbolic --|g6.4.6.|tThe package
VarianceGamma --|g6.5.|tSynopsis of R packages for GLD --
|g6.5.1.|tThe package Davies --|g6.5.2.|tThe package
fBasics --|g6.5.3.|tThe package gld --|g6.5.4.|tThe
package lmomco --|g6.6.|tApplications of the GHD to risk
modelling --|g6.6.1.|tFitting stock returns to the GHD --
|g6.6.2.|tRisk assessment with the GHD --|g6.6.3.
|tStylized facts revisited --|g6.7.|tApplications of the
GLD to risk modelling and data analysis --|g6.7.1.|tVaR
for a single stock --|g6.7.2.|tShape triangle for FTSE 100
constituents --|tReferences --|g7.|tExtreme value theory -
-|g7.1.|tPreliminaries --|g7.2.|tExtreme value methods and
models --|g7.2.1.|tThe block maxima approach --|g7.2.2.
|trth largest order models --|g7.2.3.|tThe peaks-over-
threshold approach --|g7.3.|tSynopsis of R packages --
|g7.3.1.|tThe package evd --|g7.3.2.|tThe package evdbayes
--|g7.3.3.|tThe package evir --|g7.3.4.|tThe package
fExtremes --|g7.3.5.|tThe packages ismev and extRemes --
|g7.3.6.|tThe package POT --|g7.3.7.|tThe package QRM --
|g7.3.8.|tThe package Renext --|g7.4.|tEmpirical
applications of EVT --|g7.4.1.|tSection outline --|g7.4.2.
|tBlock maxima model for Siemens --|g7.4.3.|tr block
maxima model for BMW --|g7.4.4.|tPOT method for Boeing --
|tReferences --|g8.|tModelling volatility --|g8.1.
|tPreliminaries --|g8.2.|tThe class of ARCH models --
|g8.3.|tSynopsis of R packages --|g8.3.1.|tThe package
bayesGARCH --|g8.3.2.|tThe package ccgarch --|g8.3.3.|tThe
package fGarch --|g8.3.4.|tThe package gogarch --|g8.3.5.
|tThe packages rugarch and rmgarch --|g8.3.6.|tThe package
tseries --|g8.4.|tEmpirical application of volatility
models --|tReferences --|g9.|tModelling dependence --
|g9.1.|tOverview --|g9.2.|tCorrelation, dependence and
distributions --|g9.3.|tCopulae --|g9.3.1.|tMotivation --
|g9.3.2.|tCorrelations and dependence revisited --|g9.3.3.
|tClassification of copulae --|g9.4.|tSynopsis of R
packages --|g9.4.1.|tThe package BLCOP --|g9.4.2.|tThe
packages copula and nacopula --|g9.4.3.|tThe package
fCopulae --|g9.4.4.|tThe package gumbel --|g9.4.5.|tThe
package QRM --|g9.5.|tEmpirical applications of copulae --
|g9.5.1.|tGARCH-copula model --|g9.5.2.|tMixed copula
approaches --|tReferences --|gpt. III|tPORTFOLIO
OPTIMIZATION APPROACHES --|g10.|tRobust portfolio
optimization --|g10.1.|tOverview --|g10.2.|tRobust
statistics --|g10.2.1.|tMotivation --|g10.2.2.|tSelected
robust estimators --|g10.3.|tRobust optimization --
|g10.3.1.|tMotivation --|g10.3.2.|tUncertainty sets and
problem formulation --|g10.4.|tSynopsis of R packages --
|g10.4.1.|tThe package covRobust --|g10.4.2.|tThe package
fPortfolio --|g10.4.3.|tThe package MASS --|g10.4.4.|tThe
package robustbase --|g10.4.5.|tThe package robust --
|g10.4.6.|tThe package rrcov --|g10.4.7.|tThe package
Rsocp --|g10.5.|tEmpirical applications --|g10.5.1.
|tPortfolio simulation: Robust versus classical statistics
--|g10.5.2.|tPortfolio back-test: Robust versus classical
statistics --|g10.5.3.|tPortfolio back-test: Robust
optimization --|tReferences --|g11.|tDiversification
reconsidered --|g11.1.|tIntroduction --|g11.2.|tMost
diversified portfolio --|g11.3.|tRisk contribution
constrained portfolios --|g11.4.|tOptimal tail-dependent
portfolios --|g11.5.|tSynopsis of R packages --|g11.5.1.
|tThe packages DEoptim and RcppDE --|g11.5.2.|tThe package
FRAPO --|g11.5.3.|tThe package PortfolioAnalytics --
|g11.6.|tEmpirical applications --|g11.6.1.|tComparison of
approaches --|g11.6.2.|tOptimal tail-dependent portfolio
against benchmark --|g11.6.3.|tLimiting contributions to
expected shortfall --|tReferences --|g12.|tRisk-optimal
portfolios --|g12.1.|tOverview --|g12.2.|tMean-VaR
portfolios --|g12.3.|tOptimal CVaR portfolios --|g12.4.
|tOptimal draw-down portfolios --|g12.5.|tSynopsis of R
packages --|g12.5.1.|tThe package fPortfolio --|g12.5.2.
|tThe package FRAPO --|g12.5.3.|tPackages for linear
programming --|g12.5.4.|tThe package PerformanceAnalytics
--|g12.6.|tEmpirical applications --|g12.6.1.|tMinimum-
CVaR versus minimum-variance portfolios --|g12.6.2.|tDraw-
down constrained portfolios --|g12.6.3.|tBack-test
comparison for stock portfolio --|tReferences --|g13.
|tTactical asset allocation --|g13.1.|tOverview --|g13.2.
|tSurvey of selected time series models --|g13.2.1.
|tUnivariate time series models --|g13.2.2.|tMultivariate
time series models --|g13.3.|tBlack-Litterman approach --
|g13.4.|tCopula opinion and entropy pooling --|g13.4.1.
|tIntroduction --|g13.4.2.|tThe COP model --|g13.4.3.|tThe
EP model --|g13.5.|tSynopsis of R packages --|g13.5.1.
|tThe package BLCOP --|g13.5.2.|tThe package dse --
|g13.5.3.|tThe package fArma --|g13.5.4.|tThe package
forecast --|g13.5.5.|tThe package MSBVAR --|g13.5.6.|tThe
package PairTrading --|g13.5.7.|tThe packages urca and
vars --|g13.6.|tEmpirical applications --|g13.6.1.|tBlack-
Litterman portfolio optimization --|g13.6.2.|tCopula
opinion pooling --|g13.6.3.|tProtection strategies --
|tReferences --|gAppendix|tA Package overview --|gA.1.
|tPackages in alphabetical order --|gA.2.|tPackages
ordered by topic --|tReferences --|gAppendix B|tTime
series data --|gB.1.|tDate-time classes --|gB.2.|tThe ts
class in the base package stats --|gB.3.|tIrregular-spaced
time series --|gB.4.|tThe package timeSeries --|gB.5.|tThe
package zoo --|gB.6.|tThe packages tframe and xts --
|tReferences --|gAppendix C|tBack-testing and reporting of
portfolio strategies --|gC.1.|tR packages for back-testing
--|gC.2.|tR facilities for reporting --|gC.3.|tInterfacing
databases --|tReferences --|gAppendix D|tTechnicalities.
520 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.
588 0 Print version record and CIP data provided by publisher.
590 O'Reilly|bO'Reilly Online Learning: Academic/Public
Library Edition
650 0 Financial risk|xMathematical models.
650 0 Portfolio management.
650 0 R (Computer program language)
650 6 Risque financier|xModèles mathématiques.
650 6 Gestion de portefeuille.
650 6 R (Langage de programmation)
650 7 Portfolio management|2fast
650 7 R (Computer program language)|2fast
650 7 Portföljförvaltning.|2sao
650 7 R (programspråk)|2sao
776 08 |iPrint version:|aPfaff, Bernhard.|tFinancial risk
modelling and portfolio optimization with R.|dHoboken,
N.J. : Wiley, 2013|z9780470978702|w(DLC) 2012030904
830 0 Statistics in practice.
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
learning.oreilly.com/library/view/~/9781118477120/?ar
|zAvailable on O'Reilly for Public Libraries
938 Askews and Holts Library Services|bASKH|nBDZ0021995823
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