The random coefficient autoregressive model with seasonal volatility innovations (RCA-SGARCH)
Abstract
This paper dealt with the autoregressive model when the coefficient is random. The residuals series of the model exhibit two behaviors, kurtosis and volatility. These volatilities are usually seasonal in the real financial data, which always uses GARCH models. So the use of RCA and GARCH models together will provide an appropriate framework to study and analysis of time-varying volatility as well as the presence of seasonal effects in financial series. Applying copper's daily economic close prices when the errors series are distributed, as usual, t_((3)) and t_((7)) distributions are achieved. Therefore, the RCA(1) model, when residuals follow the GARCH(1, 0)x(0, 1)_5 model together, is the appropriate model.
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PDFDOI: http://dx.doi.org/10.21533/pen.v10i4.3167
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Copyright (c) 2022 Halah Fadhil Hussein AL-Hakeem, Jawad Kadhim Khudhair Al-Musawi
This work is licensed under a Creative Commons Attribution 4.0 International License.
ISSN: 2303-4521
Digital Object Identifier DOI: 10.21533/pen
This work is licensed under a Creative Commons Attribution 4.0 International License