Forecasting linear time series models with heteroskedastic errors in a Bayesian approach
A study was conducted to compare the forecasting performance of four models, namely Stochastic Volatility (SV), Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Autoregressive with GARCH errors (AR-GARCH) and Autoregressive with SV errors (AR-SV).Bayesian approach and Markov Chain...
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| Format: | Conference or Workshop Item |
| Language: | English |
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2015
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| Online Access: | https://repo.uum.edu.my/id/eprint/15663/1/PID211.pdf |
| _version_ | 1803627014849036288 |
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| author | Amiri, Esmail |
| author_facet | Amiri, Esmail |
| author_sort | Amiri, Esmail |
| collection | UUM |
| description | A study was conducted to compare the forecasting performance of four models, namely Stochastic Volatility (SV), Generalized Autoregressive
Conditional Heteroskedasticity (GARCH), Autoregressive with GARCH errors (AR-GARCH) and Autoregressive with SV errors (AR-SV).Bayesian approach and Markov Chain Monte Carlo (MCMC) simulation methods are applied to estimate the parameters of the models and their predictive
densities; using three time series data (daily Euro/US Dollar, British Pound/US Dollar and Iranian Rial/US Dollar exchange rates).Out-ofsample analysis through cumulative predictive Bayes factors clearly showed that modeling regression residuals heteroskedastic, substantially improves predictive performance, especially in turbulent times.A direct comparison
of SV and vanilla GARCH(1,1) indicated that the former performs better in terms of predictive accuracy |
| first_indexed | 2024-07-04T05:59:20Z |
| format | Conference or Workshop Item |
| id | uum-15663 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T05:59:20Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | uum-156632016-04-27T01:14:34Z https://repo.uum.edu.my/id/eprint/15663/ Forecasting linear time series models with heteroskedastic errors in a Bayesian approach Amiri, Esmail QA75 Electronic computers. Computer science A study was conducted to compare the forecasting performance of four models, namely Stochastic Volatility (SV), Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Autoregressive with GARCH errors (AR-GARCH) and Autoregressive with SV errors (AR-SV).Bayesian approach and Markov Chain Monte Carlo (MCMC) simulation methods are applied to estimate the parameters of the models and their predictive densities; using three time series data (daily Euro/US Dollar, British Pound/US Dollar and Iranian Rial/US Dollar exchange rates).Out-ofsample analysis through cumulative predictive Bayes factors clearly showed that modeling regression residuals heteroskedastic, substantially improves predictive performance, especially in turbulent times.A direct comparison of SV and vanilla GARCH(1,1) indicated that the former performs better in terms of predictive accuracy 2015-08-11 Conference or Workshop Item PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/15663/1/PID211.pdf Amiri, Esmail (2015) Forecasting linear time series models with heteroskedastic errors in a Bayesian approach. In: 5th International Conference on Computing and Informatics (ICOCI) 2015, 11-13 August 2015, Istanbul, Turkey. http://www.icoci.cms.net.my/proceedings/2015/TOC.html |
| spellingShingle | QA75 Electronic computers. Computer science Amiri, Esmail Forecasting linear time series models with heteroskedastic errors in a Bayesian approach |
| title | Forecasting linear time series models with heteroskedastic errors in a Bayesian approach |
| title_full | Forecasting linear time series models with heteroskedastic errors in a Bayesian approach |
| title_fullStr | Forecasting linear time series models with heteroskedastic errors in a Bayesian approach |
| title_full_unstemmed | Forecasting linear time series models with heteroskedastic errors in a Bayesian approach |
| title_short | Forecasting linear time series models with heteroskedastic errors in a Bayesian approach |
| title_sort | forecasting linear time series models with heteroskedastic errors in a bayesian approach |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/15663/1/PID211.pdf |
| work_keys_str_mv | AT amiriesmail forecastinglineartimeseriesmodelswithheteroskedasticerrorsinabayesianapproach |