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RiskPortfolios: Computation of risk-based portfolios in R
RiskPortfolios is an R package for constructing risk-based portfolios. It provides a set of functionalities to build mean-variance, minimum variance, inverse-volatility weighted (Leote De Carvalho, Lu, and Moulin (2012)), equal-risk-contribution (Maillard, Roncalli, and Teïletche (2010)), maximum diversification (Choueifaty and Coignard (2008)), and risk-efficient (Amenc et al. (2011)) portfolios. Optimization is achieved with the R packages quadprog (Weingessel (2013)) and nloptr (Ypma (2014)). Long or gross constraints can be added to the optimizer. As risk-based portfolios are mainly based on covariances, the package also provides a large set of covariance matrix estimators.
Bayesian estimation of the GARCH(1,1) model with Student-t innovations in R
This paper presents the R package bayesGARCH which provides functions for the Bayesian estimation of the parsimonious but effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling algorithm. The usage of the package is shown in an empirical application to exchange rate log-returns.
DEoptim: An R package for global optimization by Differential Evolution
This article describes the R package DEoptim, which implements the Differential Evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector. The implementation of Differential Evolution in DEoptim interfaces with C code for efficiency. The utility of the package is illustrated by case studies in fitting a Parratt model for X-ray reflectometry data and a Markov-Switching Generalized AutoRegressive Conditional Heteroskedasticity model for the returns of the Swiss Market Index.
Adaptive mixture of Student-t distributions as a flexible distribution for efficient simulation: The R package AdMit
This paper presents the R package AdMit which provides flexible functions to approximate a certain target distribution and to efficiently generate a sample of random draws from it, given only a kernel of the target density function. The core algorithm consists of the function AdMit which fits an adaptive mixture of Student-t distributions to the density of interest. Then, importance sampling or the independence chain Metropolis-Hastings algorithm is used to obtain quantities of interest for the target density, using the fitted mixture as the importance or candidate density. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling algorithm. The relevance of the package is shown in two examples. The first aims at illustrating in detail the use of the functions provided by the package in a bivariate bimodal distribution. The second shows the relevance of the adaptive mixture procedure through the Bayesian estimation of a mixture of ARCH model fitted to foreign exchange log-returns data. The methodology is compared to standard cases of importance sampling and the Metropolis-Hastings algorithm using a naive candidate and with the Griddy-Gibbs approach.
Differential Evolution with DEoptim: An application to non-convex portfolio optimization
The R package DEoptim implements the differential evolution algorithm. This algorithm is an evolutionary technique similar to genetic algorithms that is useful for the solution of global optimization problems. In this note we provide an introduction to the package and demonstrate its utility for financial applications by solving a non-convex optimization problem.
AdMit: Adaptive mixtures of Student-t distributions
This short note presents the R package AdMit which provides flexible functions to approximate a certain target distribution and it provides an efficient sample of random draws from it, given only a kernel of the target density function. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling algorithm. To illustrate the use of the package, we apply the AdMit methodology to a bivariate bimodal distribution. We describe the use of the functions provided by the package and document the ability and relevance of the methodology to reproduce the shape of non-elliptical distributions.