In this paper, a general overview on spatial and spatiotemporal ARCH models is provided. In particular, we distinguish between three different spatial ARCH-type models. In addition to the original definition of ?, we introduce an logarithmic spatial ARCH model in this paper. For this new model, maximum-likelihood estimators for the parameters are proposed. In addition, we consider a new complex-valued definition of the spatial ARCH process. Moreover, spatial GARCH models are briefly discussed. From a practical point of view, the use of the R-package spGARCH is demonstrated. To be precise, we show how the proposed spatial ARCH models can be simulated and summarize the variety of spatial models, which can be estimated by the estimation functions provided in the package. Eventually, we apply all procedures to a real-data example.
spGARCH, spdep, gstat, Stem, rugarch, Rsolnp, Rcpp, RcppEigen
Spatial, NumericalMathematics, SpatioTemporal, Finance, HighPerformanceComputing, Optimization, TimeSeries
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For attribution, please cite this work as
Otto, "spGARCH: An R-Package for Spatial and Spatiotemporal ARCH and GARCH models", The R Journal, 2019
BibTeX citation
@article{RJ-2019-053,
author = {Otto, Philipp},
title = {spGARCH: An R-Package for Spatial and Spatiotemporal ARCH and GARCH models},
journal = {The R Journal},
year = {2019},
note = {https://doi.org/10.32614/RJ-2019-053},
doi = {10.32614/RJ-2019-053},
volume = {11},
issue = {2},
issn = {2073-4859},
pages = {401-420}
}