ctv finance

CRAN Task View: Empirical Finance

CRAN Task View Empirical Finance

Maintainer: Dirk Eddelbuettel
Date: 2024-11-09

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 r view("Econometrics"), r view("Optimization"),
r view("Robust"), and r view("TimeSeries") Task Views.

The ctv package supports these Task Views. Its functions install.views and update.views allow,
respectively, installation or update of packages from a given Task View; the option coreOnly can
restrict operations to packages labeled as core below.

Contributions are always welcome and encouraged, either via e-mail to the
maintainer or by submitting an issue or pull request in the
GitHub repository linked above. See the Contributing
page in the CRAN Task
Views repo for details.

Standard regression models

A detailed overview of the available regression methodologies is provided by the
r view("Econometrics") task view. This is complemented by the r view("Robust") task view, which
focuses on more robust and resistant methods.
Linear models such as ordinary least squares (OLS) can be estimated by lm() (from by the stats
package contained in the basic R distribution). Maximum Likelihood (ML) estimation can be
undertaken with the standard optim() function. Many other suitable methods are listed in the
r view("Optimization") view. Non-linear least squares can be estimated with the nls()
function, as well as with nlme() from the r pkg("nlme") package.
For the linear model, a variety of regression diagnostic tests are provided by the r pkg("car"),
r pkg("lmtest"), r pkg("strucchange"), r pkg("urca", priority = "core"), and
r pkg("sandwich") packages. The r pkg("Rcmdr") package provide user interfaces that may be
of interest as well.

Time series

A detailed overview of tools for time series analysis can be found in the r view("TimeSeries")
task view. Below a brief overview of the most important methods in finance is given.
Classical time series functionality is provided by the arima() and KalmanLike() commands in
the basic R distribution.

The r pkg("timsac") package provides a variety of more advanced estimation
methods; r pkg("fracdiff") can estimate fractionally integrated series; r pkg("longmemo")
covers related material.
For volatility modeling, the standard GARCH(1,1) model can be estimated with the garch()
function in the r pkg("tseries", priority = "core") package. Rmetrics (see below) contains the
r pkg("fGarch", priority = "core") package which has additional models. The r pkg("rugarch", priority = "core") package can be used to model a variety of univariate GARCH models with
extensions such as ARFIMA, in-mean, external regressors and various other specifications; with
methods for fit, forecast, simulation, inference and plotting are provided too. The
r pkg("rmgarch") builds on it to provide the ability to estimate several multivariate GARCH
models. The r pkg("betategarch") package can estimate and simulate the Beta-t-EGARCH model by
Harvey. The r pkg("bayesGARCH") package can perform Bayesian estimation of a GARCH(1,1) model
with Student’s t innovations. For multivariate models, the r pkg("gogarch") package provides
functions for generalized orthogonal GARCH models. The r pkg("gets") package (which was
preceded by a related package AutoSEARCH) provides automated general-to-specific model selection
of the mean and log-volatility of a log-ARCH-X model. The r pkg("lgarch") package can estimate
and fit log-GARCH models. The r pkg("garchx") package estimate GARCH models with leverage and
external covariates. The r pkg("bmgarch") package fits several multivariate GARCH models in a
Bayesian setting.
Unit root and cointegration tests are provided by r pkg("tseries"), and r pkg("urca"). The
Rmetrics packages r pkg("timeSeries", priority = "core") and r pkg("fMultivar", priority = "core") contain a number of estimation functions for ARMA, GARCH, long memory models, unit
roots and more. The r pkg("CADFtest") package implements the Hansen unit root test.

The r pkg("vars") package offer estimation, diagnostics, forecasting and error decomposition
of VAR and SVAR model in a classical framework.
The r pkg("dyn") and r pkg("dynlm") packages are suitable for dynamic (linear) regression
models.
Several packages provide wavelet analysis functionality: r pkg("wavelets"),
r pkg("waveslim"), r pkg("wavethresh"). Some methods from chaos theory are provided by the
package r pkg("tseriesChaos"). r pkg("tsDyn") adds time series analysis based on dynamical
systems theory.
The r pkg("forecast") package adds functions for forecasting problems.
The r pkg("stochvol") package implements Bayesian estimation of stochastic volatility using
Markov Chain Monte Carlo, and r pkg("factorstochvol") extends this to the multivariate case.
The r pkg("MSGARCH") package adds methods to fit (by Maximum Likelihood or Bayesian),
simulate, and forecast various Markov-Switching GARCH processes.
The r pkg("DriftBurstHypothesis") package estimates a t-test statistics for the explosive
drift burst hypothesis (Christensen, Oomen and Reno, 2018).
Package r pkg("lmForc") various in-sample, out-of-sample, pseudo-out-of-sample and benchmark
linear model forecast tests.

Finance

The Rmetrics suite of packages comprises
r pkg("fAssets", priority = "core"),
r pkg("fBasics", priority = "core"),
r pkg("fBonds", priority = "core"),
r pkg("timeDate", priority = "core") (formerly: fCalendar),
r pkg("fCopulae", priority = "core"),
r pkg("fExtremes", priority = "core"),
r pkg("fGarch"),
r pkg("fImport", priority = "core"),
r pkg("fNonlinear", priority = "core"),
r pkg("fPortfolio", priority = "core"),
r pkg("fRegression", priority = "core"),
r pkg("timeSeries") (formerly: fSeries),
r pkg("fTrading", priority = "core"), and contains a very large number of relevant functions for
different aspect of empirical and computational finance.
The r pkg("RQuantLib") package provides several option-pricing functions as well as some
fixed-income functionality from the QuantLib project to R. The r pkg("RcppQuantuccia")
provides a smaller subset of QuantLib functionality as a header-only library; at current only
some calendaring functionality is exposed.
The r pkg("quantmod") package offers a number of functions for quantitative modelling in
finance as well as data acquisition, plotting and other utilities.
The r pkg("backtest") offers tools to explore portfolio-based hypotheses about financial
instruments. The r pkg("pa") package offers performance attribution functionality for equity
portfolios.
The r pkg("PerformanceAnalytics", priority = "core") package contains a large number of
functions for portfolio performance calculations and risk management.
The r pkg("TTR") contains functions to construct technical trading rules in R.
The r pkg("sde") package provides simulation and inference functionality for stochastic
differential equations.
The r pkg("vrtest") package contains a number of variance ratio tests for the weak-form of the
efficient markets hypothesis.
The r pkg("gmm") 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 model.
The r pkg("BurStFin") and r pkg("BurStMisc") package has a collection of function for
Finance including the estimation of covariance matrices.

The r pkg("parma") package provides support for portfolio allocation and risk management
applications.

The r pkg("SharpeR") package contains a collection of tools for analyzing significance of
trading strategies, based on the Sharpe ratio and overfit of the same.
The r pkg("RND") package implements various functions to extract risk-neutral densities from
option prices.
The r pkg("LSMonteCarlo") package can price American Options via the Least Squares Monte Carlo
method.
The r pkg("BenfordTests") package provides seven statistical tests and support functions for
determining if numerical data could conform to Benford’s law.
The r pkg("OptHedging") package values call and put option portfolio and implements an optimal
hedging strategy.
The r pkg("markovchain") package provides functionality to easily handle and analyse discrete
Markov chains.
The r pkg("tvm") package models provides functions for time value of money such as cashflows
and yield curves.
The r pkg("MarkowitzR") package provides functions to test the statistical significance of
Markowitz portfolios.
The r pkg("pbo") package models the probability of backtest overfitting, performance
degradation, probability of loss, and the stochastic dominance when analysing trading
strategies.
The r pkg("OptionPricing") package implements efficient Monte Carlo algorithms for the price
and the sensitivities of Asian and European Options under Geometric Brownian Motion.

The r pkg("restimizeapi") package interfaces the API at www.estimize.com which provides
crowd-sourced earnings estimates.
The r pkg("credule") package is another pricer for credit default swaps.
The r pkg("obAnalytics") package analyses and visualizes information from events in limit
order book data.
The r pkg("derivmkts") package adds a set of pricing and expository functions useful in
teaching derivatives markets.

The r pkg("ragtop") package prices equity derivatives under an extension to Black and Scholes
supporting default under a power-law link price and hazard rate.
The r pkg("InfoTrad") packages estimates PIN and extends it to different factorization and
estimation algorithms.
The r pkg("FinancialMath") package contains financial math and derivatives pricing functions
as required by the actuarial exams by the Society of Actuaries and Casualty Actuarial Society
‘Financial Mathematics’ exam.
The r pkg("tidyquant") package re-arranges functionality from several other key packages for
use in the so-called tidyverse.
The r pkg("BCC1997") prices European options under the Bakshi, Cao anc Chen (1997) model for
stochastic volatility, stochastic rates and random jumps.
The r pkg("Sim.DiffProc") package provides functions to simulate and analyse multidimensional
Itô and Stratonovitch stochastic calculus for continuous-time models.
The r pkg("BLModel") package computes the posterior distribution in a Black-Litterman model
from a prior distribution given by asset returns and continuous distribution of views given by
an external function.

The r pkg("PortfolioOptim") can solve both small and large sample portfolio optimization.

The r pkg("DtD") package computes the distance to default per Merton’s model.
The r pkg("PeerPerformance") package analyzes performance of investments funds relative to its
peers in a pairwise manner that is robust to false discoveries.
The r pkg("crseEventStudy") package provides another event-study tool to analyse abnormal
return in long-horizon events.
The r pkg("simfinapi") package provides R access to SimFin fundamental
financial statement data (given an API key).
The r pkg("NFCP") package models commodity prices via an n-factor term structure estimation.
The r pkg("LSMRealOptions") package uses least-squares Monte Carlo to value American and Real
options.
The r pkg("AssetCorr") package estimates intra- and inter-cohort correlations from default
data in a Vasicek credit portfolio model.
The r pkg("ichimoku") package provides tools for creating and visualising Ichimoku Kinko Hyo
strategies, and provides an interface to the OANDA fxTrade API for retrieving historical and
live streaming price data (which requires free registration).
The r pkg("greeks") package calculate sensitivities of financial option prices for European
and Asian and American options in the Black Scholes model.
The r pkg("RTL") (Risk Tool Library) package offers a collection of functions and metadata to
complement core packages in finance and commodities, including futures expiry tables.
The r pkg("GARCHSK") package estimates GARCHSK and GJRSK models allowing for time-varying
volatility, skewness and kurtosis.
The r pkg("bidask") package offers a novel procedure to estimate bid-ask spreads from OHLC
data, and implements other reference models.
The r pkg("strand") package adds a framework for discrete (share-level) simulations of
investment strategies.
The r pkg("HDShOP") package constructs shrinkage estimators of high-dimensional mean-variance
portfolios and performs high-dimensional tests on optimality, and the r pkg("DOSPortfolio")
package uses it to constructs dynamic optimal shrinkage estimators for the weights of the global
minimum variance portfolio.

The r pkg("SVDNF") package implements a discrete nonlinear filter to find filtering distribution
and maximum likelihood parameter estimates for stochastic volatility models with jumps.
The r pkg("fHMM") package implements hidden Markov models and their hierarchical extension
for the detection and characterization of financial market regimes.
The r pkg("epo") package offers enhanced portfolio optimization (EPO) as described in Pedersen
et al (2021).
The r pkg("BayesianFactorZoo") package provides a novel Bayesian framework for analysing
linear asset pricing models as in Bryzgalova et al (2013).
The r pkg("cryptoQuotes") package provides a streamlined access to cryptocurrency OHLC-V market data and sentiment indicators with granularity varying from seconds to months.

Risk management

The packages r pkg("qrmtools") and r pkg("qrmdata") provide tools and data for standard
tasks in Quantitative Risk Management (QRM) and accompany the book of McNeil, Frey, Embrechts
(2005, 2015, “Quantitative Risk Management: Concepts, Techniques,
Tools”).
The Task View r view("ExtremeValue") regroups a number of relevant packages.
The r pkg("mvtnorm") package provides code for multivariate Normal and t-distributions.
The package r pkg("nvmix") provides functionality for multivariate normal variance mixtures
(including normal and t for non-integer degrees of freedom).
The Rmetrics packages r pkg("fPortfolio") and r pkg("fExtremes") also contain a number of
relevant functions.
The packages r pkg("copula") and r pkg("copulaData") cover a wide range of modeling tasks
for copulas.
The r pkg("actuar") package provides an actuarial perspective to risk management.
The r pkg("ghyp") package provides generalized hyberbolic distribution functions as well as
procedures for VaR, CVaR or target-return portfolio optimizations.
The r pkg("ChainLadder") package provides functions for modeling insurance claim reserves; and
the r pkg("lifecontingencies") package provides functions for financial and actuarial
evaluations of life contingencies.
The r pkg("ESG") package can be used to model for asset projection, a scenario-based
simulation approach.
The r pkg("riskSimul") package provides efficient simulation procedures to estimate tail loss
probabilities and conditional excess for a stock portfolios where log-returns are assumed to
follow a t-copula model with generalized hyperbolic or t marginals.
The r pkg("GCPM") package analyzes the default risk of credit portfolio using both analytical
and simulation approaches.
The r pkg("FatTailsR") package provides a family of four distributions tailored to
distribution with symmetric and asymmetric fat tails.
The r pkg("Dowd") package contains functions ported from the ‘MMR2’ toolbox offered in Kevin
Dowd’s book “Measuring Market Risk”.
The r pkg("PortRisk") package computes portfolio risk attribution.
The r pkg("NetworkRiskMeasures") package implements some risk measures for financial networks
such as DebtRank, Impact Susceptibility, Impact Diffusion and Impact Fluidity.
The r pkg("Risk") package computes 26 financial risk measures for any continuous distribution.
The r pkg("RiskPortfolios") package constructs risk-based portfolios as per the corresponding
papers by Ardia et al.
The r pkg("reinsureR") package models reinsurances a class Claims whose objective is to store
claims and premiums, on which different treaties can be applied.
The r pkg("RM2006") package estimates conditional covariance matrix using the RiskMetrics 2006
methodology described in Zumbach (2007).
The r pkg("cvar") package computes expected shortfall and value at risk for continuous
distributions.
r pkg("riskParityPortfolio") offers fast implementations for constructing risk-parity
portfolios.
The r pkg("monobin") package performs monotonic binning of numeric risk factor in credit
rating models (PD, LGD, EAD) development.
The r pkg("etrm") package contains a collection of functions to perform core tasks within
energy trading and risk management (ETRM).
Package r pkg("ufRisk") offers multiple Value at Risk and Expected Shortfall measures from both
parametric and semiparametrics models.
Packages r pkg("bondAnalyst") and r pkg("stockAnalyst") provide a number of, respectively,
bond pricing and fixed-income valuation functions and fundamental equity valuation function
corresponding to standard industry practices for risk and return.
Packages r pkg("bearishTrader"), r pkg("bullishTrader"), and r pkg("volatilityTrader")
support trading strategies and analysis for, respectively, directional views or volatility regimes.
Package r pkg("VaRES") computes both value at risk and expected shortfall for many parametric
distributions.

Books

The r pkg("NMOF") package provides functions, examples and data from Numerical Methods and
Optimization in Finance by Manfred Gilli, Dietmar Maringer and Enrico Schumann (2011),
including the different optimization heuristics such as Differential Evolution, Genetic
Algorithms, Particle Swarms, and Threshold Accepting.
The r pkg("FRAPO") package provides data sets and code for the book Financial Risk Modelling
and Portfolio Optimization with R by Bernhard Pfaff (2013).

Data and date management

The r pkg("zoo", priority = "core") and r pkg("timeDate") (part of Rmetrics) packages
provide support for irregularly-spaced time series. The r pkg("xts", priority = "core")
package extends r pkg("zoo") specifically for financial time series. See the
r view("TimeSeries") task view for more details.
r pkg("timeDate") also addresses calendar issues such as recurring holidays for a large number
of financial centers, and provides code for high-frequency data sets.

The r pkg("tis") package provides time indices and time-indexed series compatible
with Fame frequencies.

Packages r pkg("IBrokers") and r pkg("rib") provide access to the Interactive Brokers API
(but require an account to access the service).
The r pkg("data.table") package provides very efficient and fast access to in-memory data sets
such as asset prices.
The package r pkg("highfrequency") contains functionality to manage, clean and match
highfrequency trades and quotes data and enables users to calculate various liquidity measures,
estimate and forecast volatility, and investigate microstructure noise and intraday periodicity.
The r pkg("bizdays") package compute business days if provided a list of holidays.
The r pkg("TAQMNGR") package manages tick-by-tick (equity) transaction data performing
‘cleaning’, ‘aggregation’ and ‘import’ where cleaning and aggregation are performed according to
Brownlees and Gallo (2006).
The r pkg("Rblpapi") package offers efficient access to the Bloomberg API and allows bdp,
bdh, and bds queries as well as data retrieval both in (regular time-)bars and ticks (albeit
without subsecond resolution).
The r pkg("finreportr") package can download reports from the SEC Edgar database, and relies
on, inter alia, the r pkg("XBRL") package for parsing these reports.
The r pkg("GetTDData") package imports Brazilian government bonds data (such as LTN, NTN-B and
LFT ) from the Tesouro Direto website.

Data from Kenneth French’s website can be downloaded with package r pkg("frenchdata").
Individual datasets can also be downloaded with function French in package r pkg("NMOF").
Exchange data can be accessed (with a free API subscription) via package r pkg("freecurrencyapi").