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MLP/RBF Ağ Mimarileriyle Hibrit MGARCH-ANN Model Performans Karşılaştırması: Petrol Fiyat Oynaklığı

Year 2020, 20th International Symposium on Econometrics, Operations Research and Statistics EYI 2020 Special Issue, 78 - 93, 02.07.2020

Abstract

Ticari mallar olarak ifade edilen emtia, sanayi metalleri, değerli metaller, tarımsal ürünler ve enerji ürünleri gibi birçok alt gruba ayrılmaktadır. Yüksek işlem hacimli enstrümanlar arasında olan ve birincil enerji tüketiminde ilk sırada yer alan petrolün fiyatındaki oynaklığının artmasıyla ortaya çıkan belirsizlik, tüketicilerin ve üreticilerin harcama, tasarruf ve yatırım kararlarını değiştirmesine ve potansiyel olarak kaynakların yeniden tahsis edilmesine neden olmaktadır. Yüksek frekanslı serilerde zamana göre değişen ve kümelenme eğilimi gösteren oynaklığın modellenmesinde çoğunlukla Genelleştirilmiş Otoregresif Koşullu Değişen Varyans (GARCH) tipi modellerden faydalanılmaktadır. Bununla birlikte, doğrusal yapının yanında eğrisel yapıyı da modelleyebilen Yapay Sinir Ağları (ANN), GARCH-tipi modellere iyi bir seçenek olarak ortaya çıkmaktadır. Çalışma kapsamında, literatürde henüz yaygın olarak kullanılmayan, sistem olarak tahmin edilen çok değişkenli GARCH tipi modellerden elde edilen oynaklık değerlerinin ANN’de çıktı katmanı olarak yer almasıyla elde edilen hibrit model (Tip-II) yapısı kullanılarak Eylül-1992 ve Temmuz-2019 dönemleri itibariyle petrol fiyatlarındaki oynaklık yapısı incelenmektedir. Hibrit modeller ile elde edilen tahminler karşılaştırıldığında, en iyi performans değerlerine çok değişkenli GARCH-tipi model sınıfına ait olan Dinamik Koşullu Korelasyon Modeli (DCC-MGARCH) ve Çok Katmanlı Algılayıcılı Modeller(MLP) tarafından oluşturulan model yapısı ile ulaşıldığı belirlenmektedir.

References

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  • Baffour, A. A., Feng, J., & Taylor, E. K. (2019). A hybrid artificial neural network-GJR modeling approach to forecasting currency exchange rate volatility. Neurocomputing, 365, 285-301.
  • Block, H. D. (1962). The perceptron: A model for brain functioning. Reviews of Modern Physics, 34(1), 123.
  • Caprio, J. & Clark, P.B. (1981). Oil price shocks in a portfolio-balance model. International Finance Discussion Papers, 181, 1-24.
  • Chatterjee, S., Sarkar, S., Hore, S., Dey, N., Ashour, A. S., & Balas, V. E. (2017). Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Computing and Applications, 28(8), 2005-2016.
  • Chaudhuri, K. & Daniel, B. C. (1998). Long-run equilibrium real exchange rates and oil prices. Economic Letters, 56, 231-238.
  • Cheong, C. W. (2009). Modeling and forecasting crude oil markets using ARCH-type models. Energy policy, 37(6), 2346-2355.
  • Çam, S., Balli, E., & Sigeze, Ç. (2017). Petrol Fiyatlarındaki Oynaklığın ARCH/GARCH Modelleri ve Yapay Sinir Ağları Algoritması İle Tahmini. Uluslararası Yönetim İktisat ve İşletme Dergisi, 13(5), 588-597.
  • Elmas, Ç. (2018). Yapay Zeka Uygulamaları, Seçkin Yayıncılık, Ankara
  • Engle, R.F. (2001). Dynamic conditional correlation: A simple class of multivariate GARCH models. Journal of Business & Economic Statistics, 20(3), 339-350.
  • Fang, L., Chen, B., Yu, H., & Qian, Y. (2018). The importance of global economic policy uncertainty in predicting gold futures market volatility: A GARCH‐MIDAS approach. Journal of Futures Markets, 38(3), 413-422.
  • Güloğlu, B., & Akman, A. (2007). Türkiye’de döviz kuru oynaklığının SWARCH yöntemi ile analizi. Finans Politik & Ekonomik Yorumlar, 44(512), 43-51.
  • Ignácio, L. V. R., Ribeiro, L. G. A., da Veiga, C. P., & Bittencourt, J. T. (2017). The use of artificial intelligence for forecasting oil prices. Espacios, 38, 1-27.
  • Joy, M., 2011, Gold and the US Dollar: Hedge or Haven? Finance Research Letters, Vol. 8, Issue. 3, pp. 120-131.
  • Kaftan, İ. (2010). Batı Türkiye Gravite ve Deprem Katalog Verilerinin Yapay Sinir Ağları ile Değerlendirilmesi. Doktora Tezi, Dokuz Eylül Üniversitesi, Fen Bilimleri Enstitüsü, İzmir.
  • Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing, 11(2), 2664-2675.
  • Kristjanpoller, W., & Minutolo, M. C. (2016). Forecasting volatility of oil price using an artificial neural network-GARCH model. Expert Systems with Applications, 65, 233-241.
  • Kristjanpoller, W., & Hernández, E. (2017). Volatility of main metals forecasted by a hybrid ANN-GARCH model with regressors. Expert Systems with Applications, 84, 290-300.
  • Krugman, P. (1980). Oil and the dollar. National Bureau of Economic Research Working Paper Series, 554, 0-18.
  • Kuncheva, L. I., & Whitaker, C. J. (2003). Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine learning, 51(2), 181-207.
  • Kuper, G. H. (2002). Measuring oil price volatility. Available at SSRN 316480.
  • Lardic, S., & Mignon, V. (2008). Oil prices and economic activity: An asymmetric cointegration approach. Energy Economics, 30(3), 847-855.
  • Lasheras, F. S., de Cos Juez, F. J., Sánchez, A. S., Krzemień, A., & Fernández, P. R. (2015). Forecasting the COMEX copper spot price by means of neural networks and ARIMA models. Resources Policy, 45, 37-43.
  • Lu, X., Que, D., & Cao, G. (2016). Volatility forecast based on the hybrid artificial neural network and GARCH-type models. Procedia Computer Science, 91, 1044-1049.
  • Maghyereh, A. (2006). Oil price shocks and emerging stock markets: A generalized VAR approach. In Global stock markets and portfolio management (pp. 55-68). Palgrave Macmillan, London.
  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.
  • Minsky, M. L., Papert, S. A., & Perceptrons, F. (1969). The MIT Press: Cambridge. Mass.(Rev. Edition, 1988).
  • Moody, J., & Darken, C. J. (1989). Fast learning in networks of locally-tuned processing units. Neural computation, 1(2), 281-294.
  • Orskaug, E. (2009). Multivariate DCC-GARCH model -with various error distributions. Norwegian Computing Center.
  • Parisi, A., Parisi, F., & Díaz, D. (2008). Forecasting gold price changes: Rolling and recursive neural network models. Journal of Multinational financial management, 18(5), 477-487.
  • Poyraz, E., & Didin, A. G. S. (2008). Altın Fiyatlarındaki Değişimin Döviz Kuru, Döviz Rezervi Ve Petrol Fiyatlarından Etkilenme Derecelerinin Çoklu Faktör Modeli İle Değerlendirilmesi. Süleyman Demirel Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 13(2), 93-104.
  • Ramyar, S., & Kianfar, F. (2019). Forecasting crude oil prices: A comparison between artificial neural networks and vector autoregressive models. Computational Economics, 53(2), 743-761.
  • Rezaeianzadeh, M., Tabari, H., Yazdi, A. A., Isik, S., & Kalin, L. (2014). Flood flow forecasting using ANN, ANFIS and regression models. Neural Computing and Applications, 25(1), 25-37.
  • Rosenblatt, F. (1962). Principles of neurodynamics. perceptrons and the theory of brain mechanisms (No. VG-1196-G-8). Cornell Aeronautical Lab Inc Buffalo NY.
  • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.
  • Silvennoinen, A., & Teräsvirta, T. (2007). Multivariate GARCH models. Working Paper Series İn Economics and Finance, 669.
  • Terzioğlu, M. K. (2018). Effects of Inflation Uncertainty on Economic Policies: Inflation-Targeting Regime. Management From An Emergıng Market Perspectıve, 235.
  • Wang, Y., & Wu, C. (2012). Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?. Energy Economics, 34(6), 2167-2181.
  • Yılmaz M. (2012), Jeodezik Nokta Hız Kestiriminde Yapay Sinir Ağlarının Kullanılabilirliği, Doktora Tezi, Afyon Kocatepe Üniversitesi, Fen Bilimleri Enstitüsü
  • Yu, L., Wang, S., & Lai, K. K. (2007). Foreign-exchange-rate forecasting with artificial neural networks. Springer Science & Business Media.
Year 2020, 20th International Symposium on Econometrics, Operations Research and Statistics EYI 2020 Special Issue, 78 - 93, 02.07.2020

Abstract

References

  • Amano, R. A. & van Norden, S. (1998). Oil prices and the rise and fall of the us real exchange rate. Journal of International Money and Finance, 17, 299-316.
  • Baffour, A. A., Feng, J., & Taylor, E. K. (2019). A hybrid artificial neural network-GJR modeling approach to forecasting currency exchange rate volatility. Neurocomputing, 365, 285-301.
  • Block, H. D. (1962). The perceptron: A model for brain functioning. Reviews of Modern Physics, 34(1), 123.
  • Caprio, J. & Clark, P.B. (1981). Oil price shocks in a portfolio-balance model. International Finance Discussion Papers, 181, 1-24.
  • Chatterjee, S., Sarkar, S., Hore, S., Dey, N., Ashour, A. S., & Balas, V. E. (2017). Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Computing and Applications, 28(8), 2005-2016.
  • Chaudhuri, K. & Daniel, B. C. (1998). Long-run equilibrium real exchange rates and oil prices. Economic Letters, 56, 231-238.
  • Cheong, C. W. (2009). Modeling and forecasting crude oil markets using ARCH-type models. Energy policy, 37(6), 2346-2355.
  • Çam, S., Balli, E., & Sigeze, Ç. (2017). Petrol Fiyatlarındaki Oynaklığın ARCH/GARCH Modelleri ve Yapay Sinir Ağları Algoritması İle Tahmini. Uluslararası Yönetim İktisat ve İşletme Dergisi, 13(5), 588-597.
  • Elmas, Ç. (2018). Yapay Zeka Uygulamaları, Seçkin Yayıncılık, Ankara
  • Engle, R.F. (2001). Dynamic conditional correlation: A simple class of multivariate GARCH models. Journal of Business & Economic Statistics, 20(3), 339-350.
  • Fang, L., Chen, B., Yu, H., & Qian, Y. (2018). The importance of global economic policy uncertainty in predicting gold futures market volatility: A GARCH‐MIDAS approach. Journal of Futures Markets, 38(3), 413-422.
  • Güloğlu, B., & Akman, A. (2007). Türkiye’de döviz kuru oynaklığının SWARCH yöntemi ile analizi. Finans Politik & Ekonomik Yorumlar, 44(512), 43-51.
  • Ignácio, L. V. R., Ribeiro, L. G. A., da Veiga, C. P., & Bittencourt, J. T. (2017). The use of artificial intelligence for forecasting oil prices. Espacios, 38, 1-27.
  • Joy, M., 2011, Gold and the US Dollar: Hedge or Haven? Finance Research Letters, Vol. 8, Issue. 3, pp. 120-131.
  • Kaftan, İ. (2010). Batı Türkiye Gravite ve Deprem Katalog Verilerinin Yapay Sinir Ağları ile Değerlendirilmesi. Doktora Tezi, Dokuz Eylül Üniversitesi, Fen Bilimleri Enstitüsü, İzmir.
  • Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing, 11(2), 2664-2675.
  • Kristjanpoller, W., & Minutolo, M. C. (2016). Forecasting volatility of oil price using an artificial neural network-GARCH model. Expert Systems with Applications, 65, 233-241.
  • Kristjanpoller, W., & Hernández, E. (2017). Volatility of main metals forecasted by a hybrid ANN-GARCH model with regressors. Expert Systems with Applications, 84, 290-300.
  • Krugman, P. (1980). Oil and the dollar. National Bureau of Economic Research Working Paper Series, 554, 0-18.
  • Kuncheva, L. I., & Whitaker, C. J. (2003). Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine learning, 51(2), 181-207.
  • Kuper, G. H. (2002). Measuring oil price volatility. Available at SSRN 316480.
  • Lardic, S., & Mignon, V. (2008). Oil prices and economic activity: An asymmetric cointegration approach. Energy Economics, 30(3), 847-855.
  • Lasheras, F. S., de Cos Juez, F. J., Sánchez, A. S., Krzemień, A., & Fernández, P. R. (2015). Forecasting the COMEX copper spot price by means of neural networks and ARIMA models. Resources Policy, 45, 37-43.
  • Lu, X., Que, D., & Cao, G. (2016). Volatility forecast based on the hybrid artificial neural network and GARCH-type models. Procedia Computer Science, 91, 1044-1049.
  • Maghyereh, A. (2006). Oil price shocks and emerging stock markets: A generalized VAR approach. In Global stock markets and portfolio management (pp. 55-68). Palgrave Macmillan, London.
  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.
  • Minsky, M. L., Papert, S. A., & Perceptrons, F. (1969). The MIT Press: Cambridge. Mass.(Rev. Edition, 1988).
  • Moody, J., & Darken, C. J. (1989). Fast learning in networks of locally-tuned processing units. Neural computation, 1(2), 281-294.
  • Orskaug, E. (2009). Multivariate DCC-GARCH model -with various error distributions. Norwegian Computing Center.
  • Parisi, A., Parisi, F., & Díaz, D. (2008). Forecasting gold price changes: Rolling and recursive neural network models. Journal of Multinational financial management, 18(5), 477-487.
  • Poyraz, E., & Didin, A. G. S. (2008). Altın Fiyatlarındaki Değişimin Döviz Kuru, Döviz Rezervi Ve Petrol Fiyatlarından Etkilenme Derecelerinin Çoklu Faktör Modeli İle Değerlendirilmesi. Süleyman Demirel Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 13(2), 93-104.
  • Ramyar, S., & Kianfar, F. (2019). Forecasting crude oil prices: A comparison between artificial neural networks and vector autoregressive models. Computational Economics, 53(2), 743-761.
  • Rezaeianzadeh, M., Tabari, H., Yazdi, A. A., Isik, S., & Kalin, L. (2014). Flood flow forecasting using ANN, ANFIS and regression models. Neural Computing and Applications, 25(1), 25-37.
  • Rosenblatt, F. (1962). Principles of neurodynamics. perceptrons and the theory of brain mechanisms (No. VG-1196-G-8). Cornell Aeronautical Lab Inc Buffalo NY.
  • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.
  • Silvennoinen, A., & Teräsvirta, T. (2007). Multivariate GARCH models. Working Paper Series İn Economics and Finance, 669.
  • Terzioğlu, M. K. (2018). Effects of Inflation Uncertainty on Economic Policies: Inflation-Targeting Regime. Management From An Emergıng Market Perspectıve, 235.
  • Wang, Y., & Wu, C. (2012). Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?. Energy Economics, 34(6), 2167-2181.
  • Yılmaz M. (2012), Jeodezik Nokta Hız Kestiriminde Yapay Sinir Ağlarının Kullanılabilirliği, Doktora Tezi, Afyon Kocatepe Üniversitesi, Fen Bilimleri Enstitüsü
  • Yu, L., Wang, S., & Lai, K. K. (2007). Foreign-exchange-rate forecasting with artificial neural networks. Springer Science & Business Media.
There are 40 citations in total.

Details

Primary Language Turkish
Journal Section Main Section
Authors

Nurcan Metin This is me 0000-0002-8761-6603

Kübra Karadağ 0000-0003-4631-7102

Mehmet Kenan Terzioğlu 0000-0002-6053-830X

Publication Date July 2, 2020
Published in Issue Year 2020 20th International Symposium on Econometrics, Operations Research and Statistics EYI 2020 Special Issue

Cite

APA Metin, N., Karadağ, K., & Terzioğlu, M. K. (2020). MLP/RBF Ağ Mimarileriyle Hibrit MGARCH-ANN Model Performans Karşılaştırması: Petrol Fiyat Oynaklığı. Ankara Hacı Bayram Veli Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi78-93.