Description, this is a book about portfolio optimization from the perspective of computational finance and financial engineering.
Explores portfolio risk concepts and optimization with risk constraints.
Let's test if it gives the same results: library(fPortfolio) #This is how you would usually do it defaultSpec - portfolioSpec setTargetReturn(defaultSpec) -.06 lppAssets - 100*T, c SBI "SPI "LMI "MPI lppData - portfolioData(data lppAssets, spec defaultSpec) port - efficientPortfolio(lppData, defaultSpec, constraints "LongOnly #Now.
We aim to process explorer pour mac provide first class documentation of the Rmetrics software environment.Financial Risk Modelling windows vista ultimate oem serial key and Portfolio Optimization with.References 36 4 Measuring risks.1 Introduction.2 Synopsis of risk measures.3 Portfolio risk concepts 42, references 44 5 Modern portfolio theory.1 Introduction.2 Markowitz portfolios.3 Empirical mean-variance portfolios 50, references.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.All our eBooks are sold here: /.In fPortfolio this is done in the portfolioData function.The Rmetrics Association has started in writing and publishing a series of eBooks.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.So a quick and dirty solution might be to write your own function that doesn't do the covariance estimation but takes as input your estimated mu and sigma.But it looks like you need to define your own fpfoliodata object.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.So here is my solution: myPortfolioData - function(mu, sigma, data, spec) if (is(data, "fpfoliodata return(data) stopifnot(class(data) "timeSeries data sort(data) nAssets ncol(data) names colnames(data) if (ll(names) names paste A 1:nAssets, sep Cov cov(data) rownames(Cov) - colnames(Cov) - names.data list(series data, nAssets nAssets, names names).statistics - list(mean.Thus the main emphasis is to briefly introduce the concepts and to give the reader a set of powerful tools to solve the problems in the field of portfolio optimization.Equal(port@portfolio, myPort@portfolio now with your numbers: mu - c(SBI0.05, SPI0.1, LMI0.075, MPI0.06) sigma - matrix(c(0.02657429,.01805751,.02048764,.02110555,.01805751,.03781108,.03859943,.02959261,.02048764,.03859943,.04606304,.03043146,.02110555,.02959261,.03043146,.03880064 4, 4, dimnameslist(names(mu names(mu) myLppData - myPortfolioData(mu, sigma, lppAssets, defaultSpec) myPort - efficientPortfolio(myLppData, defaultSpec, constraints.Up vote 1 down vote, i am not an expert in the fPortfolio package.
) sigma - matrix(c(0.015899554, -0.01274142,.009803865, -0.01588837,-0.012741418,.58461212, -0.014074691,.41159843,0.009803865, -0.01407469,.014951108, -0.02332223,-0.015888368,.41159843, -0.023322233,.53503263 4, 4, dimnameslist(names(mu names(mu) myLppData - myPortfolioData(mu, sigma, lppAssets, defaultSpec) myPort - efficientPortfolio(myLppData, defaultSpec, constraints "LongOnly all.
Graduate and postgraduate students in finance, economics, risk management as well as practitioners in finance and portfolio optimization will find this book beneficial.The first part, Chapters 1-10, is dedicated to the exploratory data analysis of financial assets, the second part, Chapters 11-14, to the framework of portfolio design, selection and optimization, the third part, Chapters 15-19, to the mean-variance portfolio approach, the fourth part, Chapters 20-23,.No comments for "Portfolio Optimization with R/Rmetrics".References 28 3 Financial market data.1 Stylized facts of financial market returns.1.1 Stylized facts for univariate series.1.2 Stylized facts for multivariate series.2 Implications for risk models.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.Includes updated list of R packages for enabling the reader to replicate the results in the book.This book divides roughly into five parts.
Is accompanied by a supporting website featuring examples and case studies.
It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study.