BEKKs: An R Package for Estimation of Conditional Volatility of Multivariate Time Series
Dublin Core
Title
BEKKs: An R Package for Estimation of Conditional Volatility of Multivariate Time Series
Subject
BEKK model, multivariate GARCH, leverage effect, value-at-risk, impulse response functions, R.
Description
We describe the R package BEKKs, which implements the estimation and diagnostic
analysis of a prominent family of multivariate generalized autoregressive conditionally heteroskedastic (MGARCH) processes, the so-called BEKK models. Unlike existing software
packages, we make use of analytical derivatives implemented in efficient C++ code for nonlinear log-likelihood optimization. This allows fast parameter estimation even in higher
model dimensions N > 3. The baseline BEKK model is complemented with an asymmetric parameterization that allows for a flexible modeling of conditional (co)variances.
Furthermore, we provide the user with the simplified scalar and diagonal BEKK models
to deal with high dimensionality of heteroskedastic time series. The package is designed
in an object-oriented way featuring a comprehensive toolbox of methods to investigate
and interpret, for instance, volatility impulse response functions, risk estimation and forecasting (VaR) and a backtesting algorithm to compare the forecasting performance of
alternative BEKK models. For illustrative purposes, we analyze a bivariate ETF return
series (S&P, US treasury bonds) and a four-dimensional system comprising, in addition, a
gold ETF and changes of a log oil price by means of the suggested package. We find that
the BEKKs package is more than 100 times faster for time series systems of dimension
N > 3 than other existing packages.
analysis of a prominent family of multivariate generalized autoregressive conditionally heteroskedastic (MGARCH) processes, the so-called BEKK models. Unlike existing software
packages, we make use of analytical derivatives implemented in efficient C++ code for nonlinear log-likelihood optimization. This allows fast parameter estimation even in higher
model dimensions N > 3. The baseline BEKK model is complemented with an asymmetric parameterization that allows for a flexible modeling of conditional (co)variances.
Furthermore, we provide the user with the simplified scalar and diagonal BEKK models
to deal with high dimensionality of heteroskedastic time series. The package is designed
in an object-oriented way featuring a comprehensive toolbox of methods to investigate
and interpret, for instance, volatility impulse response functions, risk estimation and forecasting (VaR) and a backtesting algorithm to compare the forecasting performance of
alternative BEKK models. For illustrative purposes, we analyze a bivariate ETF return
series (S&P, US treasury bonds) and a four-dimensional system comprising, in addition, a
gold ETF and changes of a log oil price by means of the suggested package. We find that
the BEKKs package is more than 100 times faster for time series systems of dimension
N > 3 than other existing packages.
Creator
Markus J. Fülle
Source
https://www.jstatsoft.org/article/view/v111i04
Publisher
University of Göttingen
Date
November 2024
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Markus J. Fülle, “BEKKs: An R Package for Estimation of Conditional Volatility of Multivariate Time Series,” Repository Horizon University Indonesia, accessed May 10, 2025, https://repository.horizon.ac.id/items/show/8348.