https://github.com/eddelbuettel/ctv-finance

CRAN Task View: Empirical Finance
https://github.com/eddelbuettel/ctv-finance

cran finance r task-view

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CRAN Task View: Empirical Finance

• Host: GitHub
• URL: https://github.com/eddelbuettel/ctv-finance
• Owner: eddelbuettel
• Created: 2014-11-29T01:57:16.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2024-11-09T13:22:18.000Z (about 1 month ago)
• Last Synced: 2024-11-17T05:07:12.691Z (25 days ago)
• Topics: cran, finance, r, task-view
• Language: R
• Size: 254 KB
• Stars: 56
• Watchers: 16
• Forks: 31
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: Contributing.md

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README

        

## CRAN Task View Empirical Finance

Maintainer: [Dirk Eddelbuettel](https://dirk.eddelbuettel.com)  

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)](https://cran.r-project.org). 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](mailto:[email protected]) or by submitting an issue or pull request in the

GitHub repository linked above. See the [Contributing

page](https://github.com/cran-task-views/ctv/blob/main/Contributing.md) in the [CRAN Task

Views](https://github.com/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](https://SimFin.com) 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")](https://press.princeton.edu/books/hardcover/9780691166278/quantitative-risk-management).

-   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")`.

### Links

-   [Rmetrics contains a wealth of R code for Finance](http://www.rmetrics.org)

-   [Quantlib is a C++ library for quantitative finance](http://www.quantlib.org)

-   [Documentation for the Bloomberg API accessed by Rblpapi](http://www.bloomberglabs.com//)

-   [Mailing list: R Special Interest Group Finance](https://stat.ethz.ch/mailman/listinfo/R-SIG-Finance/)

-   [MSCI indexes data](http://www.msci.com/)

-   [French/Fama data](http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html)

-   [Wilshire indexes data](http://www.wilshire.com/Indexes/calculator/)

-   [Rene Carmona](http://orfe.princeton.edu/~rcarmona/maindownload.html)

-   [Eric Zivot](http://faculty.washington.edu/ezivot/splus.htm)

-   [R Code for Ruppert's 'Statistics and Finance'](http://christopherggreen.github.io/RuppertStatisticsFinance2004/)

-   [Guy Yollin](http://www.r-programming.org/papers)

-   [Textbook "Tidy Finance with R" with many empirical finance applications](http://www.tidy-finance.org//)