https://oldena.lpnu.ua/handle/ntb/52493| Title: | Forecasting the Oil Price with a Periodic Regression ARFIMA-GARCH Process |
| Authors: | Ambach, Daniel Ambach, Oleksandra |
| Affiliation: | Department for Data Science smava GmbH Berlin FirmenCenter Grundung und Nachfolge Berliner Sparkasse |
| Bibliographic description (Ukraine): | Ambach D. Forecasting the Oil Price with a Periodic Regression ARFIMA-GARCH Process / Daniel Ambach, Oleksandra Ambach // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 212–217. — (Dynamic Data Mining & Data Stream Mining). |
| Bibliographic description (International): | Ambach D. Forecasting the Oil Price with a Periodic Regression ARFIMA-GARCH Process / Daniel Ambach, Oleksandra Ambach // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 212–217. — (Dynamic Data Mining & Data Stream Mining). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | long-memory forecasting oil-price ARFIMA periodic model |
| Number of pages: | 6 |
| Page range: | 212-217 |
| Start page: | 212 |
| End page: | 217 |
| Abstract: | This article provides a new periodic time series model to predict the oil price. Moreover, the approach discusses short-term forecasting of the oil price. Hence, we discuss the model fit and the out-of-sample performance. Finally, we derive further enhancements and improvements for further research. |
| URI: | https://ena.lpnu.ua/handle/ntb/52493 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| URL for reference material: | http://ena.lp.edu.ua |
| References (Ukraine): | [1] Akaike, H. (1974), A new look at the statistical model identification, Automatic Control, IEEE Transactions on, 19(6), pp. 716–723. [2] Baillie, R.T. (1996), Long memory processes and fractional integration in econometrics, Journal of econometrics, 73(1), pp. 5–59. [3] Baillie, R.T., Chung, C.F., and Tieslau, M.A. (1996), Analysing inflation by the fractionally integrated ARFIMA–GARCH model, Journal of applied econometrics, pp. 23–40. 216 Lviv Polytechnic National University Institutional Repository http://ena.lp.edu.ua [4] Bollerslev, T. (1986), Generalized autoregressive conditional heteroskedasticity, Journal of econometrics, 31(3), pp. 307–327. [5] Box, G.E. and Pierce, D.A. (1970), Distribution of residual autocorrelations in autoregressive-integrated moving average time series models, Journal of the American statistical Association, 65(332), pp. 1509–1526. [6] Breusch, T.S. and Pagan, A.R. (1979), A simple test for heteroscedasticity and random coefficient variation, Econometrica: Journal of the Econometric Society, pp. 1287–1294. [7] Brockwell, P.J. and Davis, R.A. (2009), Time series: theory and methods, Springer, New York. [8] Brockwell, P.J. and Davis, R.A. (2013), Time series: theory and methods, Springer Science & Business Media. [9] Durbin, J. and Watson, G.S. (1951), Testing for serial correlation in least squares regression. II, Biometrika, 38(1/2), pp. 159–177. [10] Engle, R.F. (1982), Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica: Journal of the Econometric Society, pp. 987–1007. [11] Fahrmeir, L., Kneib, T., and Lang, S. (2007a), Regression: Modelle, Methoden und Anwendungen, Springer-Verlag. [12] Fahrmeir, L., Kunstler, R., Pigeot, I., and Tutz, G. (2007b), ¨ Statistik: Der Weg zur Datenanalyse, Springer-Verlag. [13] Fisher, T.J. and Gallagher, C.M. (2012), New weighted portmanteau statistics for time series goodness of fit testing, Journal of the American Statistical Association, 107(498), pp. 777–787. [14] Goldfeld, S.M. and Quandt, R.E. (1965), Some tests for homoscedasticity, Journal of the American statistical Association, 60(310), pp. 539–547. [15] Greenwald, B.C., Stiglitz, J.E., and Weiss, A. (1984), Informational imperfections in the capital market and macro-economic fluctuations. [16] Haslett, J. and Raftery, A.E. (1989), Space-time modelling with longmemory dependence: Assessing Ireland’s wind power resource, Applied Statistics, 30(1), pp. 1–50. [17] Ishida, I., Watanabe, T., et al. (2009), Modeling and Forecasting the Volatility of the Nikkei 225 realized Volatility using the ARFIMAGARCH model, Global COE Hi-Stat Discussion Paper, 32. [18] Kalkman, J., Pfeiffer, W., and Pereira, S. (2013), Are we running out of oil? [19] Kane, I.L. and Yusof, F. (2013), Assessment of Risk of Rainfall Events with a Hybrid of ARFIMA-GARCH, Modern Applied Science, 7(12), p. 78. [20] Koopman, S.J., Ooms, M., and Carnero, M.A. (2007), Periodic seasonal Reg-ARFIMA–GARCH models for daily electricity spot prices, Journal of the American Statistical Association, 102(477), pp. 16–27. [21] Leite, A., Rocha, A., and Silva, M. (2009), Long memory and volatility in HRV: an ARFIMA-GARCH approach, Computers in Cardiology, 2009, IEEE, pp. 165–168. [22] Mandelbrot, B.B. and Van Ness, J.W. (1968), Fractional Brownian motions, fractional noises and applications, SIAM review, 10(4), pp. 422–437. [23] Palm, F.C. (1996), 7 GARCH models of volatility, Handbook of statistics, 14, pp. 209–240. [24] Schwarz, G. et al. (1978), Estimating the dimension of a model, The annals of statistics, 6(2), pp. 461–464. [25] Shapiro, S.S. and Wilk, M.B. (1965), An analysis of variance test for normality (complete samples), Biometrika, 52(3/4), pp. 591–611. [26] Shumway, R.H. and Stoffer, D.S. (2010), Time series analysis and its applications: with R examples, Springer Science & Business Media. [27] White, H. (1980), A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity, Econometrica: Journal of the Econometric Society, pp. 817–838. |
| References (International): | [1] Akaike, H. (1974), A new look at the statistical model identification, Automatic Control, IEEE Transactions on, 19(6), pp. 716–723. [2] Baillie, R.T. (1996), Long memory processes and fractional integration in econometrics, Journal of econometrics, 73(1), pp. 5–59. [3] Baillie, R.T., Chung, C.F., and Tieslau, M.A. (1996), Analysing inflation by the fractionally integrated ARFIMA–GARCH model, Journal of applied econometrics, pp. 23–40. 216 Lviv Polytechnic National University Institutional Repository http://ena.lp.edu.ua [4] Bollerslev, T. (1986), Generalized autoregressive conditional heteroskedasticity, Journal of econometrics, 31(3), pp. 307–327. [5] Box, G.E. and Pierce, D.A. (1970), Distribution of residual autocorrelations in autoregressive-integrated moving average time series models, Journal of the American statistical Association, 65(332), pp. 1509–1526. [6] Breusch, T.S. and Pagan, A.R. (1979), A simple test for heteroscedasticity and random coefficient variation, Econometrica: Journal of the Econometric Society, pp. 1287–1294. [7] Brockwell, P.J. and Davis, R.A. (2009), Time series: theory and methods, Springer, New York. [8] Brockwell, P.J. and Davis, R.A. (2013), Time series: theory and methods, Springer Science & Business Media. [9] Durbin, J. and Watson, G.S. (1951), Testing for serial correlation in least squares regression. II, Biometrika, 38(1/2), pp. 159–177. [10] Engle, R.F. (1982), Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica: Journal of the Econometric Society, pp. 987–1007. [11] Fahrmeir, L., Kneib, T., and Lang, S. (2007a), Regression: Modelle, Methoden und Anwendungen, Springer-Verlag. [12] Fahrmeir, L., Kunstler, R., Pigeot, I., and Tutz, G. (2007b), ¨ Statistik: Der Weg zur Datenanalyse, Springer-Verlag. [13] Fisher, T.J. and Gallagher, C.M. (2012), New weighted portmanteau statistics for time series goodness of fit testing, Journal of the American Statistical Association, 107(498), pp. 777–787. [14] Goldfeld, S.M. and Quandt, R.E. (1965), Some tests for homoscedasticity, Journal of the American statistical Association, 60(310), pp. 539–547. [15] Greenwald, B.C., Stiglitz, J.E., and Weiss, A. (1984), Informational imperfections in the capital market and macro-economic fluctuations. [16] Haslett, J. and Raftery, A.E. (1989), Space-time modelling with longmemory dependence: Assessing Ireland’s wind power resource, Applied Statistics, 30(1), pp. 1–50. [17] Ishida, I., Watanabe, T., et al. (2009), Modeling and Forecasting the Volatility of the Nikkei 225 realized Volatility using the ARFIMAGARCH model, Global COE Hi-Stat Discussion Paper, 32. [18] Kalkman, J., Pfeiffer, W., and Pereira, S. (2013), Are we running out of oil? [19] Kane, I.L. and Yusof, F. (2013), Assessment of Risk of Rainfall Events with a Hybrid of ARFIMA-GARCH, Modern Applied Science, 7(12), p. 78. [20] Koopman, S.J., Ooms, M., and Carnero, M.A. (2007), Periodic seasonal Reg-ARFIMA–GARCH models for daily electricity spot prices, Journal of the American Statistical Association, 102(477), pp. 16–27. [21] Leite, A., Rocha, A., and Silva, M. (2009), Long memory and volatility in HRV: an ARFIMA-GARCH approach, Computers in Cardiology, 2009, IEEE, pp. 165–168. [22] Mandelbrot, B.B. and Van Ness, J.W. (1968), Fractional Brownian motions, fractional noises and applications, SIAM review, 10(4), pp. 422–437. [23] Palm, F.C. (1996), 7 GARCH models of volatility, Handbook of statistics, 14, pp. 209–240. [24] Schwarz, G. et al. (1978), Estimating the dimension of a model, The annals of statistics, 6(2), pp. 461–464. [25] Shapiro, S.S. and Wilk, M.B. (1965), An analysis of variance test for normality (complete samples), Biometrika, 52(3/4), pp. 591–611. [26] Shumway, R.H. and Stoffer, D.S. (2010), Time series analysis and its applications: with R examples, Springer Science & Business Media. [27] White, H. (1980), A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity, Econometrica: Journal of the Econometric Society, pp. 817–838. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2018_Ambach_D-Forecasting_the_Oil_Price_212-217.pdf | 374.53 kB | Adobe PDF | View/Open | |
| 2018_Ambach_D-Forecasting_the_Oil_Price_212-217__COVER.png | 548.75 kB | image/png | View/Open |
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