MODELLING VOLATILITY AND FORECASTING BIST100 RETURN BY USING ANFIS

Year 2018, , 325 - 345, 30.12.2018

Hakan Pabuçcu , Nurdan Değirmenci

https://doi.org/10.31679/adamakademi.394549

Cited By: 4

Abstract

Stock market volatility is considered as an important issue in financial literature and is defined as sudden instability that occurs in the price of any security. Volatility also represents the uncertainty that affects investors' decision-making processes in financial markets in the face of possible variations. In many countries, especially in emerging financial markets, both investors and policy makers are often confronted with increasing risk and uncertainty problems. Accordingly, considering volatility is crucial for investors to predict the return of financial assets, especially in long-term investment decisions. Volatility, which expresses the variability of any financial asset, has a very important place in the estimation of the return. In this study, the volatility of the Turkish stock market was estimated through the GARCH models using the Stock Exchange Istanbul100 (BIST100) index. Whether the stock market index has an asymmetric effect is investigated using the EGARCH model. It is very difficult to predict the uncertainty and chaotic behavior of the BIST100 index by traditional methods. For this reason, the fuzzy logic and neural network hybrid model, which is widely used in the model of uncertainty, has been applied to estimate the stock return in the study. The dataset used in the study includes daily stock closing prices for the period 2009-2017. As a result of the extensive literature survey, no studies have been found on the use of fuzzy logic based approaches in estimating and continuing volatility. For this reason, it is thought that working has an original value.

Keywords

Volatility, GARCH, ANFIS

VOLATİLİTENİN MODELLENMESİ VE ANFIS MODEL İLE BIST100 GETİRİ TAHMİNİ

Abstract

Hisse senedi piyasası volatilitesi finans literatüründe önemli bir konu olarak ele alınmakta ve herhangi bir menkul kıymetin fiyatında meydana gelen ani değişkenlik olarak tanımlanmaktadır. Volatilite, ortaya çıkabilecek olası değişkenlikler doğrultusunda finansal piyasalarda yatırımcıların karar alma süreçlerini etkileyen belirsizliği de temsil etmektedir. Birçok ülkede, özellikle gelişmekte olan finansal piyasalarda gerek yatırımcılar gerekse politika yapıcılar artan risk ve belirsizlik problemleri ile sıkça karşılaşmaktadır. Buna bağlı olarak volatilitenin dikkate alınması özellikle yatırımcıların uzun dönemli yatırım kararlarında finansal varlıkların getirilerini tahmin edebilmeleri için oldukça önemlidir. Herhangi bir finansal varlığa ait getirinin değişkenliğini ifade eden volatilite, finansal varlıkların getirilerini tahmin etmede de çok önemli bir yere sahiptir. Bu çalışmada Borsa İstanbul100 (BIST100) endeksi kullanılarak Türkiye hisse senedi piyasası volatilitesi ve hisse senedi piyasa endeksinin asimetrik etki gösterip göstermediği GARCH-EGARCH modelleri kullanılarak araştırılmıştır. BIST100 endeksinin barındırdığı belirsizlik ve kaotik (düzensiz) davranışları geleneksel yöntemlerle tahmin etmek bir başka ifade ile riski yönetmek oldukça güç olmaktadır. Bu sebeple, çalışmada hisse senedi getirisinin tahmin edilmesi için belirsizliği modellemede yaygın olarak kullanılan bulanık mantık ve sinir ağı hibrit modeli uygulanmıştır. Çalışmada kullanılan veri seti 2009-2017 dönemine ilişkin günlük hisse senedi kapanış fiyatlarını kapsamaktadır. Yapılan kapsamlı literatür araştırması sonucu volatilitenin tahmini ve devamında bulanık mantık temelli yaklaşımların kullanıldığı bir çalışmaya rastlanmamıştır. Bu sebeple çalışmanın özgün bir değere sahip olduğu düşünülmektedir.

Keywords

Volatilite, GARCH, ANFIS

References

• AL-Najjar, D. M. (2016). Modelling and Estimation of Volatility Using ARCH/GARCH Models in Jordan’s Stock Market. Asian Journal of Finance & Accounting, 8(1), 152–167. http://doi.org/10.5296/ajfa.v8i1.9129.
• Bayramoğlu, T., Pabuçcu, H., & Çelebi Boz, F. (2017). Türkiye İçin Anfıs Modeli İle Birincil Enerji Talep Tahmini. Ege Akademik Bakis (Ege Academic Review), 17(3), 431–446. http://doi.org/10.21121/eab.2017328408.
• Binner, J. M., Gazely, A. M., & Chen, S.-H. (2002). Financial innovation and Divisia monetary indices in Taiwan: A neural network approach. The European Journal of Finance, 8(2), 238–247.
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
• Co, H. C., & Boosarawongse, R. (2007). Forecasting Thailand’s rice export: Statistical techniques vs. artificial neural networks. Computers & Industrial Engineering, 53(4), 610–627.
• Değirmenci, N. & Akay, A. (2017). Finansal Verilerin ARIMA ve ARCH Modelleriyle Öngö-rüsü: Türkiye Örneği. Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 12(3), 15-36.
• Donaldson, R. G., & Kamstra, M. (1997). An artificial neural network-GARCH model for international stock return volatility. Journal of Empirical Finance, 4(1), 17–46. http://doi.org/10.1016/S0927-5398(96)00011-4.
• Dutta, A. (2014). Modelling volatility: symmetric or asymmetric garch models? Journal of Statistics: Advances in Theory and Applications, 12(2), 99–108.
• Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987–1007.
• Fidrmuc, J., & Horváth, R. (2008). Volatility of exchange rates in selected new EU members: Evidence from daily data. Economic Systems, 32(1), 103–118.
• Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779–1801.
• Gökçe, A. (2001). İstanbul Menkul Kıymetler Borsası Getirilerindeki Volatilitenin ARCH Teknikleri ile Ölçülmesi. Gazi Üniversitesi İ.İ.B.F. Dergisi, 1, 35–58.
• Gupta, S., & Kashyap, S. (2016). Modelling volatility and forecasting of exchange rate of British pound sterling and Indian rupee. Journal of Modelling in Management, 11(2), 389–404. http://doi.org/10.1108/JM2-04-2014-0029.
• Hirota, K., & Pedrycz, W. (1994). A distributed model of fuzzy set connectives. Fuzzy Sets and Systems, 68(2), 157–170. http://doi.org/http://dx.doi.org/10.1016/0165-0114(94)90042-6.
• Hirota, K., & Pedrycz, W. (1994). Or/and Neuron in Modeling Fuzzy Set Connectives. Ieee Transactions on Fuzzy Systems, 2(2), 151–161. http://doi.org/10.1109/91.277963.
• Hsieh, D. A. (1989). Testinf for Nonlinear Dependence in Daily Foreign Exchange Rates. The Journal of Business, 62(3), 339–368.
• Hyup Roh, T. (2007). Forecasting the volatility of stock price index. Expert Systems with Applications, 33(4), 916–922. http://doi.org/10.1016/j.eswa.2006.08.001.
• Jang, J.-S. R. (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference system. IEEE Transactions on Systems, Man and Cybernetics, 23(3), 665–685. http://doi.org/10.1109/21.256541.
• Jang, J.-S. R. (1996). Input selection for ANFIS learning. In Proceedings of IEEE 5th International Fuzzy Systems (Vol. 2, pp. 1493–1499). http://doi.org/10.1109/FUZZY.1996.552396.
• Jang, J.-S. R., Sun, C., & Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence (1st ed.). New Jersey: NJ:Prentice Hall.
• Kandel, A. (1991). Fuzzy expert systems. CRC press.
• Koutmos, G. (2012). Modeling interest rate volatility: an extended EGARCH approach. Managerial Finance, 38(6), 628–635.
• Kumar, M. (2013). Returns and volatility spillover between stock prices and exchange rates: Empirical evidence from IBSA countries. International Journal of Emerging Markets, 8(2), 108–128.
• Li, H., & Hong, Y. (2011). Financial volatility forecasting with range-based autoregressive volatility model. Finance Research Letters, 8(2), 69–76.
• Li, M. Y. L., & Lin, H. W. W. (2003). Examining the volatility of Taiwan Stock Index returns via a three-volatility-regime Markov-switching ARCH model. Review of Quantitative Finance and Accounting, 21(2), 123–139. http://doi.org/10.1023/A:1024887315531.
• Maradiaga, D. I., Zapata, H. O., & Pujula, A. L. (2012). Exchange Rate Volatility in BRICS Countries. In Southern Agricultural Economics Association, Annual Meeting, February (pp. 4–7).
• Marshall, A., Musayev, T., Pinto, H., & Tang, L. (2012). Impact of news announcements on the foreign exchange implied volatility. Journal of International Financial Markets, Institutions and Money, 22(4), 719–737.
• Nazif Çatik, A., & Karaçuka, M. (2012). A comparative analysis of alternative univariate time series models in forecasting Turkish inflation. Journal of Business Economics and Management, 13(2), 275–293.
• Neely, C. J. (2009). Forecasting foreign exchange volatility: Why is implied volatility biased and inefficient? And does it matter? Journal of International Financial Markets, Institutions and Money, 19(1), 188–205.
• Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347–370.
• Özden, Ü. H. (2008). İMKB Bileşik 100 Endeksi Getiri Volatilitesinin Analizi. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 7(13), 339–350.
• Özkan, F. (2013). Comparing the forecasting performance of neural network and purchasing power parity: The case of Turkey. Economic Modelling, 31, 752–758.
• Pabuçcu, H. (2017). Time series forecasting with neural network and fuzzy logic. In Recent Studies in Economics (pp. 195–204). E-BWN.
• Pabuçcu, H., & Ayan, T. Y. (2017). The Development of an Alternative Method for the Sovereign Credit Rating System Based on Adaptive Neuro-Fuzzy Inference System. American Journal of Operations Research, 7(1), 41–55. http://doi.org/10.4236/ajor.2017.71003.
• Pabuçcu, H., & Pabuçcu, R. (2017). J Eğrisi Hipotezinin Geçerlilik Testi: ANFIS Model. In XVIII. Uluslararası Ekonometri Yöneylem Araştırması ve İstatistik Sempozyumu (pp. 188–189). Trabzon.
• Perwej, Y., & Perwej, A. (2012). Forecasting of Indian Rupee (INR)/US Dollar (USD) currency exchange rate using artificial neural network. International Journal of Computer Science, Engineering and Applications, 2(2), 41–52. http://doi.org/10.5121/ijcsea.2012.2204.
• Poon, S.-H., & Granger, C. W. J. (2003). Forecasting volatility in financial markets: A review. Journal of Economic Literature, 41(2), 478–539.
• Schnabl, G. (2008). Exchange rate volatility and growth in small open economies at the EMU periphery. Economic Systems, 32(1), 70–91.
• Şahin, Ö. (2016). Gün içi Fiyat Anomalisinin ARCH Ailesi Modelleri İle Test Edilmesi; Borsa İstanbul 100 Ve Kurumsal Yönetim Endeksi Üzerine Bir Uygulama. Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 19(36), 329–360.
• Tuna, K., & İsabetli, İ. (2014). Finansal Piyasalarda Volatilite ve Bist-100 Örneği. Kocaeli Üniversitesi Sosyal Bilimler Dergisi, 24(1), 21–31.
• Zhang, J., & Hu, W. (2013). Does realized volatility provide additional information? International Journal of Managerial Finance, 9(1), 70–87.
• Zou, H. F., Xia, G. P., Yang, F. T., & Wang, H. Y. (2007). An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting. Neurocomputing, 70(16), 2913–2923. http://doi.org/https://doi.org/10.1016/j.neucom.2007.01.009