CRAN Task View: Empirical Finance
|Contact:||Dirk.Eddelbuettel at R-project.org|
This CRAN Task View contains a list of packages useful for
empirical work in Finance, grouped by topic.
Besides these packages, a very wide variety of functions suitable for
empirical work in Finance is provided by both the basic R
system (and its set of recommended core packages), and a number of
other packages on the Comprehensive R Archive Network (CRAN).
Consequently, several of the other CRAN Task Views may contain suitable
packages, in particular the
Please send suggestions for additions and extensions for this task
view to the
task view maintainer
Standard regression models
A detailed overview of the available regression methodologies is
provided by the
task view. This is
complemented by the
which focuses on more robust
and resistant methods.
Linear models such as ordinary least squares (OLS) can be estimated
(from by the stats package contained in the basic R
distribution). Maximum Likelihood (ML) estimation can be undertaken
with the standard
function. Many other suitable methods
are listed in the
view. Non-linear least squares can
be estimated with the
function, as well as with
For the linear model, a variety of regression diagnostic tests
are provided by the
packages provide user
interfaces that may be of interest as well.
A detailed overview of tools for time series analysis can be found in
task view. Below a brief overview of the
most important methods in finance is given.
Classical time series functionality is provided
commands in the
basic R distribution.
packages provides a variety of more
advanced estimation methods;
estimate fractionally integrated series;
simulates fractional Levy
provide fractal time series modeling
For volatility modeling, the standard GARCH(1,1) model can be estimated with the
function in the
Rmetrics (see below) contains the
has additional models. The
Bayesian estimation of a GARCH(1,1) model with Student's t
innovations. For multivariate models, the
package can estimate (multivariate) Conditional Correlation GARCH
models whereas the
package provides functions for
generalized orthogonal GARCH models.
Unit root and cointegration tests are provided by
The Rmetrics packages
contain a number of estimation functions for
ARMA, GARCH, long memory models, unit roots and more.
package implements the Hansen unit root test.
implements autoregressive time series
decomposition in a Bayesian framework;
Bayesian estimation of vector autoregressive models. The
Bayesian and likelihood analysis of dynamic linear models (ie
linear Gaussian state space models).
package offer estimation, diagnostics,
forecasting and error decomposition of VAR and SVAR model in a
are suitable for dynamic (linear) regression
package can estimate dynamic model such as
ARMA, ARMA-GARCH, ACD and MEM.
Several packages provide wavelet analysis
wavethresh. Some methods from chaos
theory are provided by the package
adds time series analysis based on dynamical
package adds functions for
package provides functions for time series factor analysis.
The Rmetrics suite of packages comprises
packages contain a very large number of
relevant functions for different aspect of empirical and
package provides several option-pricing
functions as well as some fixed-income functionality from the
QuantLib project to R.
package offers a number of functions for
quantitative modelling in finance as well as data acqusition, plotting
and other utilities.
classes for equity portfolio management; the
builds a related simulation framework.
offers tools to
explore portfolio-based hypotheses about financial instruments.
packages provides functions for
single index, constant correlation and multigroup models.
package contains a large number
of functions for portfolio performance calculations and risk management.
contains functions to construct technical
trading rules in R. The
several test statistics for assessing the efficacy
of such rules, and the
functions to analyse and use such trading rules.
package can compute present values, cash
flows and other simple finance calculations.
package provides simulation and inference functionality
for stochastic differential equations.
packages contain methods for the estimation
of zero-coupon yield curves and spread curves based the parametric
Nelson and Siegel (1987) method with the Svensson (1994)
extension. The former package adds the McCulloch (1975) cubic
splines approach, the latter package adds the Diebold and Li approach.
package contains a number of variance ratio
tests for the weak-form of the efficient markets hypothesis.
package provides generalized method of moments
(GMM) estimations function that are often used when estimating the
parameters of the moment conditions implied by an asset pricing
package contains estimator based on random
matrix theory as well as shrinkage methods to remove sampling noise
when estimating sample covariance matrices.
package uses indirect inference to estimate
non-Gaussian stochastic volatility models.
package can be used to analyses market
microstructure effects and changes in the (limit-) order books.
package can be used to model the
Schwartz (1997) two-factor model for commodities markets.
package provides functions for
modelling portfolio returns as random variables, partly
based on the work of Markowitz (1952, 1959), with an emphasis on:
modelling portfolios as functions of weight; modelling returns with
empirical cumulative distribution functions; and considering quantile
Several packages provide functionality for
Extreme Value Theory models:
functions for Credit Risk modeling.
covers quantitative risk modelling.
package provides code for multivariate Normal and t-distributions.
The Rmetrics packages
also contain a number of relevant functions.
multivariate dependency structures using copula methods.
package provides an actuarial
perspective to risk management.
package provides generalized hyberbolic distribution
functions as well as procedures for VaR, CVaR or target-return
package provides functions for modeling
insurance claim reserves.
package provides an R companion to Tsay (2005),
Analysis of Financial Time Series
, 2nd ed. Wiley,
and includes data sets, functions and script files to work some
of the examples.
Data and date management
(part of Rmetrics) packages provide support for
irregularly-spaced time series. The
specifically for financial time series. See the
task view for more details.
calendar issues such as recurring holidays for a large number of
financial centers, and provides code for high-frequency data sets.
package can access Bloomberg data (but
requires a Bloomberg installations on a Windows PC).
package can access Fame time series databases (but
also requires a Fame backend). The
time indices and time-indexed series compatible with Fame
package provides a unifying interface for
several time series data base backends, and its SQL implementations
provide a database table design.
package provides access to the Interactive Brokers
API for data access (but requires an account to access the service).
package provides very efficient and fast
access to in-memory data sets such as asset prices.