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rtsay1

MTS:All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis. (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models. (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics. (c) Finally, the MTS package also performs forecasting using diffusion index , transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.

Maintained by Ruey S. Tsay. Last updated 3 years ago.

cpp

6 stars 6.52 score 272 scripts 6 dependents

dgerlanc

backtest:Exploring Portfolio-Based Conjectures About Financial Instruments

The backtest package provides facilities for exploring portfolio-based conjectures about financial instruments (stocks, bonds, swaps, options, et cetera).

Maintained by Daniel Gerlanc. Last updated 10 years ago.

20 stars 5.52 score 33 scripts

quantsch

Strategy:Generic Framework to Analyze Trading Strategies

Users can build and test customized quantitative trading strategies. Some quantitative trading strategies are already implemented, e.g. various moving-average filters with trend following approaches. The implemented class called "Strategy" allows users to access several methods to analyze performance figures, plots and backtest the strategies. Furthermore, custom strategies can be added, a generic template is available. The custom strategies require a certain input and output so they can be called from the Strategy-constructor.

Maintained by Julian Busch. Last updated 8 years ago.

4.18 score 30 scripts

convfunctimeseries

NTS:Nonlinear Time Series Analysis

Simulation, estimation, prediction procedure, and model identification methods for nonlinear time series analysis, including threshold autoregressive models, Markov-switching models, convolutional functional autoregressive models, nonlinearity tests, Kalman filters and various sequential Monte Carlo methods. More examples and details about this package can be found in the book "Nonlinear Time Series Analysis" by Ruey S. Tsay and Rong Chen, John Wiley & Sons, 2018 (ISBN: 978-1-119-26407-1).

Maintained by Xialu Liu. Last updated 2 years ago.

2 stars 2.94 score 48 scripts

mjfullness

BEKKs:Multivariate Conditional Volatility Modelling and Forecasting

Methods and tools for estimating, simulating and forecasting of so-called BEKK-models (named after Baba, Engle, Kraft and Kroner) based on the fast Berndt–Hall–Hall–Hausman (BHHH) algorithm described in Hafner and Herwartz (2008) <doi:10.1007/s00184-007-0130-y>. For an overview, we refer the reader to Fülle et al. (2024) <doi:10.18637/jss.v111.i04>.

Maintained by Markus J. Fülle. Last updated 4 months ago.

openblascppopenmp

1.00 score 10 scripts

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