spGARCH: An R-Package for Spatial and Spatiotemporal ARCH and GARCH models

Otto, Philipp

doi:10.32614/rj-2019-053

Cited by 10 publications

(2 citation statements)

References 32 publications

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“…Thus, for direct generalisation of GARCH or E-GARCH models like in Otto et al (2018); , difficult assumptions for the existence or invertibility of the process are required in the general case. In addition, existing software could directly be used with some adaptations for the spatiotemporal case (see Otto 2019).…”

Section: Model Specificationmentioning

confidence: 99%

A Multivariate Spatial and Spatiotemporal ARCH Model

Otto¹

2022

Preprint

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This paper introduces a multivariate spatiotemporal autoregressive conditional heteroscedasticity (ARCH) model based on a vec-representation. The model includes instantaneous spatial autoregressive spill-over effects in the conditional variance, as they are usually present in spatial econometric applications. Furthermore, spatial and temporal cross-variable effects are explicitly modelled. We transform the model to a multivariate spatiotemporal autoregressive model using a log-squared transformation and derive a consistent quasi-maximum-likelihood estimator (QMLE). For finite samples and different error distributions, the performance of the QMLE is analysed in a series of Monte-Carlo simulations. In addition, we illustrate the practical usage of the new model with a realworld example. We analyse the monthly real-estate price returns for three different property types in Berlin from 2002 to 2014. We find weak (instantaneous) spatial interactions, while the temporal autoregressive structure in the market risks is of higher importance. Interactions between the different property types only occur in the temporally lagged variables. Thus, we see mainly temporal volatility clusters and weak spatial volatility spill-overs.

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Section: Model Specificationmentioning

confidence: 99%

A Multivariate Spatial and Spatiotemporal ARCH Model

Otto¹

2022

Preprint

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“…Residuals that have a constant variance (homoscedasticity) are one of the fundamental presumptions in estimating the regression coefficients with OLS so that the estimation results have a low standard error [4]. Unfortunately, this criterion is often not met, especially when examining cross-sectional data.…”

Section: Introductionmentioning

confidence: 99%

Forecasting The Share Price of PT Merdeka Copper Gold Tbk By Using Arch-Garch Model

Nabilah Faisal,

Nababan,

Sutarman

et al. 2023

sitekin

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This study aims to obtain a forecasting model and compare PT Merdeka Copper Gold Tbk share price using the time series method, ARCH-GARCH model. The data used is historical data for the period December 2021 – December 2022. The initial steps are the stationarity test, identifying the ARIMA model, and checking the heteroscedasticity effect of the best ARIMA model. Then from this model, identify the ARCH-GARCH model. After the model has been formed, compare those models that have been assumed by using the smallest AIC and SBC values and checking the model's heteroscedasticity effect. The last step is forecasting for January 2023 using GARCH (1,0). The equation is .

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