Comparison of GARCH and SVRGARCH models: Example of gold return
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https://doi.org/10.53753/jame.2068Keywords:
Financial Market, GARCH Models, SVR-GARCH Model, Machine Learning, Time SeriesAbstract
Gold has been a precious resource for people on earth from the past to the present. It is used as both a value gain and jewelry, and is the focus of interest for people in terms of receiving attention and protecting its value. Especially recently, it has been the most favorite for investors due to its excess value increase and decrease which is constantly monitored. The study aimed to compare the predictive performance of the gold price return using the Support Vector Regression-GARCH hybrid models combined with the traditional volatility models. It has been examined whether the Support Vector Regression GARCH models would increase foresight performance. The study used data on the daily frequency between 01/01/2010–01/04/2023. Generalized Autoregressive Conditional Variable Variance, Glosten-Jaganthan-Runkle GARCH, Exponential GARCH and hybrid model Support Vector Regression -GARCH are utilized as prediction methods. For all methods, the gold series is divided into two groups as training and test data. The Root Mean Square Error values are compared as a model performance criterion. The RMSE values and graphics outputs have been concluded that the Support Vector Regression-GARCH model based on predicted linear, radial-based and polynomial kernel predicts more effectively than the GARCH models.
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References
ALBERG, D., SHALIT, H., VE YOSEF, R. (2008). Estimating Stock Market Volatility Using Asymmetric GARCH Models. Applied Financial Economics, 18(15), 1201-1208.
BEZERRA, P. C. S., & ALBUQUERQUE, P. H. M. (2017). Volatility forecasting via SVR–GARCH with mixture of Gaussian kernels. Computational Management Science, 14, 179-196.
BILDIRICI, M., & ERSIN, O. O. (2012). Support Vector Machine GARCH Model in Modelling Conditional Volatility and an Application to Turkish Financial Markets. In 13th International Conference on Econometrics, Operations Research, and Statistic, 24-26.
BIRAU, R., TRIVEDI, J., & ANTONESCU, M. (2015). Modeling S&P Bombay Stock Exchange BANKEX Index Volatility Patterns Using GARCH Model. Procedia Economics and Finance, 32, 520-525.
BOLLERSLEV, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
CIHANGIR, Ç. K., & UGURLU, E. (2017). Volatility in Gold Market: Model Recommendation for Turkey. İşletme Araştırmaları Dergisi, 9(3), 284-299.
CAGLAYAN, E., & DAYIOGLU, T. (2009). Döviz Kuru Getiri Volatilitesinin Koşullu Değişen Varyans Modelleri ile Öngörüsü. Istanbul University Econometrics and Statistics e-Journal, 9, 1-16.
ENGLE, R.F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of The Variance of UK Inflation. Econometrica, 50, 987–1008.
ENGLE, R. F. (1993), “Statistical Models for Financial Volatility”, Financial Analyst Journal, 49(1), 72-78.
ENGLE, R. (2001). GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics. Journal of Economic Perspectives, 15(4), 157-168.
ENGLE, R. F., & SOKALSKA, M. E. (2012). Forecasting Intraday Volatility in the Us Equity Market. Multiplicative Component Garch. Journal of Financial Econometrics, 10(1), 54-83.
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.
GOYARI, P., & JENA, P. K. (2010). Is the Recent Global Financial Crisis Causes Volatility in Gold and Crude Oil Futures Market in India?. In International Conference on Applied Economics–ICOAE, 353-361.
GURSOY, M., & BALABAN, M. (2014). Hisse Senedi Getirilerindeki Volatilitenin Tahminlenmesinde Destek Vektör Makinelerine Dayalı Garch Modellerinin Kullanımı. Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 5(8), 167-186.
KARABACAK, M., MECIK, O., & ERHAN, G. E. N.C. (2014). Koşullu Değişen Varyans Modelleri ile BİST 100 Endeks Getirisi ve Altın Getiri Serisi Volatilitesinin Tahmini. Uluslararası Alanya İşletme Fakültesi Dergisi, 6(1), 79- 90.
KATSIAMPA, P. (2017). Volatility Estimation for Bitcoin: A Comparison of GARCH Models, Economics Letters, 158, 3-6.
NELSON, D.B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica: Journal of The Econometric Society, 59(2), 347-370.
OU, P., & WANG, H. (2010). Financial Volatility Forecasting by Least Square Support Vector Machine Based on GARCH, EGARCH and GJR Models: Evidence from ASEAN Stock Markets. International Journal of Economics and Finance, 2(1), 51-64.
PENG, Y., ALBUQUERQUE, P. H. M., DE SÁ, J. M. C., PADULA, A. J. A., & MONTENEGRO, M. R. (2018). The Best of Two Worlds: Forecasting High Frequency Volatility for Cryptocurrencies and Traditional Currencies with Support Vector Regression. Expert Systems with Applications, 97, 177-192.
PÉREZ-CRUZ, F., AFONSO-RODRIGUEZ, J. A., & GINER, J. (2003). Estimating GARCH Models Using Support Vector Machines. Quantitative Finance, 3(3), 163.
SUN, H., & YU, B. (2020). Forecasting Financial Returns Volatility: A GARCH-SVR Model. Computational Economics, 55, 451-471.
TAY, F. E., & CAO, L. (2001). Application of Support Vector Machines in Financial Time Series Forecasting. Omega, 29(4), 309-317.
TAYLOR, S. (1986). Modeling Financial Time Series John Wiley & Sons. Great Britain.
THOMAS, S., & MITCHELL, H. (2005). GARCH Modeling of High-Frequency Volatility in Australia’s National Electricity Market. System, 1-39.
WANG, Y., & WU, C. (2012). Forecasting Energy Market Volatility Using GARCH Models: Can Multivariate Models Beat Univariate Models?. Energy Economics, 34(6), 2167-2181.
WANG, B., HUANG, H., & WANG, X. (2013). A Support Vector Machine Based MSM Model for Financial ShortTerm Volatility Forecasting. Neural Computing and Applications, 22, 21-28.
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