Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models
Recently, Donaldson and Kamstra (1997) proposed a class of NN-GARCH models which are extended to a class of NN-GARCH family by Bildirici and Ersin (2009). The study aims to analyze the nonlinear behavior and leptokurtic distribution in petrol prices by utilizing a newly developed family of econometric models that deal with these concepts by benefiting from both LSTAR type and ANN based nonlinearity. With this purpose, the study proposed several LSTAR-GARCH-NN family models. It is noted that the multilayer perceptron (MLP) neural network and LSTAR models have significant architectural similarities. Accordingly, linear GARCH, fractionally integrated FI-GARCH, asymmetric power APGARCH and fractionally integrated asymmetric power APGARCH models are augmented with a family of Neural Network models. The study has following contributions: i. STAR-GARCH and LSTAR-GARCH are extended to their fractionally integrated asymmetric power versions and STAR-ST-FIGARCH and STAR-ST-APGARCH, STAR-ST-FIAPGARCH models are developed and evaluated. ii. By extending these models with neural networks, LSTAR-LST-GARCH-MLP family models are developed and investigated. These models benefit from LSTAR type nonlinearity and NN based nonlinear NN-GARCH models to capture time varying volatility and nonlinearity in petrol prices. ANN augmented versions of LSTAR-LST-GARCH models are as follows: LSTAR-LST-GARCH-MLP, LSTAR-LST-FIGARCH-MLP, LSTAR-LST-APGARCH-MLP and LSTAR-LST-FIAPGARCH-MLP. Empirical findings are collected as follows. i. To model petrol prices, fractionally integrated and asymmetric power versions provided improvements among the GARCH family models in terms of forecasting. ii. LSTAR-LST-GARCH model family is promising and show significant gains in out-of-sample forecasting. iii. MLP-GARCH family provided similar results with the LSTAR-LST-GARCH family models, except for the MLP-FIGARCH and MLP-FIAPGARCH models. iv. Volatility clustering, asymmetry and nonlinearity characteristics of petrol prices are captured most efficiently with the LSTAR-LST-GARCH-MLP models benefiting from forecasting capabilities of neural network techniques, whereas, among the newly developed models, LSTAR-LST-APGARCH-MLP model provided the best performance overall.
|Date of creation:||Jan 2012|
|Date of revision:||May 2012|
|Contact details of provider:|| Postal: Ludwigstraße 33, D-80539 Munich, Germany|
Web page: https://mpra.ub.uni-muenchen.de
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Hamilton, James D. & Susmel, Raul, 1994.
"Autoregressive conditional heteroskedasticity and changes in regime,"
Journal of Econometrics,
Elsevier, vol. 64(1-2), pages 307-333.
- Tom Doan, "undated". "RATS programs to estimate Hamilton-Susmel Markov Switching ARCH model," Statistical Software Components RTZ00083, Boston College Department of Economics.
- Hillebrand, Eric, 2005. "Neglecting parameter changes in GARCH models," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 121-138.
- T. Ane & L. Ureche-Rangau, 2006. "Stock market dynamics in a regime-switching asymmetric power GARCH model," Post-Print hal-00170841, HAL.
- Lee, Junsoo & Degennaro, Ramon P, 2000. "Smooth Transition ARCH Models: Estimation and Testing," Review of Quantitative Finance and Accounting, Springer, vol. 15(1), pages 5-20, July.
- Bollerslev, Tim, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics,
Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Thomas Mikosch & Cătălin Stărică, 2004. "Nonstationarities in Financial Time Series, the Long-Range Dependence, and the IGARCH Effects," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 378-390, February.
- John Stachurski & Vance Martin, 2008.
"Computing the Distributions of Economic Models via Simulation,"
Econometric Society, vol. 76(2), pages 443-450, 03.
- John Stachurski & University of Melbourne, 2006. "Computing the Distributions of Economic Models via Simulation," Computing in Economics and Finance 2006 185, Society for Computational Economics.
- John Stachurski, 2005. "Computing the Distributions of Economic Models Via Simulation," Department of Economics - Working Papers Series 949, The University of Melbourne.
- John Stachurski, 2006. "Computing the Distributions of Economic Models Via Simulation," KIER Working Papers 615, Kyoto University, Institute of Economic Research.
- GARCIA,René & LUGER, Richard & RENAULT, Éric, 2001.
"Empirical Assessment of an Intertemporal Option Pricing Model with Latent variables,"
Cahiers de recherche
2001-10, Universite de Montreal, Departement de sciences economiques.
- Garcia, Rene & Luger, Richard & Renault, Eric, 2003. "Empirical assessment of an intertemporal option pricing model with latent variables," Journal of Econometrics, Elsevier, vol. 116(1-2), pages 49-83.
- Garcia, R. & Luger, R. & Renault, E., 2001. "Empirical Assessment of an Intertemporal option Pricing Model with Latent variables," Cahiers de recherche 2001-10, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- René Garcia & Richard Luger & Eric Renault, 2000. "Empirical Assessment of an Intertemporal Option Pricing Model with Latent Variables," Working Papers 2000-56, Centre de Recherche en Economie et Statistique.
- Bollen, Nicolas P. B. & Gray, Stephen F. & Whaley, Robert E., 2000. "Regime switching in foreign exchange rates: Evidence from currency option prices," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 239-276.
- Bollerslev, Tim & Ole Mikkelsen, Hans, 1996.
"Modeling and pricing long memory in stock market volatility,"
Journal of Econometrics,
Elsevier, vol. 73(1), pages 151-184, July.
- Tom Doan, "undated". "RATS program to replicate Bollerslev-Mikkelson(1996) FIEGARCH models," Statistical Software Components RTZ00173, Boston College Department of Economics.
- Chan, Felix & Theoharakis, Billy, 2011. "Estimating m-regimes STAR-GARCH model using QMLE with parameter transformation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1385-1396.
- Zhongfang He & John M. Maheu, 2009.
"Real Time Detection of Structural Breaks in GARCH Models,"
Staff Working Papers
09-31, Bank of Canada.
- He, Zhongfang & Maheu, John M., 2010. "Real time detection of structural breaks in GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2628-2640, November.
- Zhongfang He & John M. Maheu, 2009. "Real Time Detection of Structural Breaks in GARCH Models," Working Paper Series 11_09, The Rimini Centre for Economic Analysis.
- Zhongfang He & John M Maheu, 2008. "Real Time Detection of Structural Breaks in GARCH Models," Working Papers tecipa-336, University of Toronto, Department of Economics.
- Conrad, Christian & Rittler, Daniel & Rotfuß, Waldemar, 2012.
"Modeling and explaining the dynamics of European Union Allowance prices at high-frequency,"
Elsevier, vol. 34(1), pages 316-326.
- Conrad, Christian & Rittler, Daniel & Rotfuß, Waldemar, 2010. "Modeling and explaining the dynamics of European Union allowance prices at high-frequency," ZEW Discussion Papers 10-038, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
- Conrad, Christian & Rittler, Daniel & Rotfuß, Waldemar, 2010. "Modeling and Explaining the Dynamics of European Union Allowance Prices at High-Frequency," Working Papers 0497, University of Heidelberg, Department of Economics.
- Francq, Christian & ZakoIÂ¨an, Jean-Michel, 2005. "The L2-structures of standard and switching-regime GARCH models," Stochastic Processes and their Applications, Elsevier, vol. 115(9), pages 1557-1582, September.
- Francis X. Diebold & Atsushi Inoue, 2000.
"Long Memory and Regime Switching,"
NBER Technical Working Papers
0264, National Bureau of Economic Research, Inc.
- Martin Sola & M Karansos & Zacharias Psaradakis, 2002.
"On the autocorrelation properties of Long Memory Garch Processes,"
Department of Economics Working Papers
025, Universidad Torcuato Di Tella.
- Menelaos Karanasos & Zacharias Psaradakis & Martin Sola, 2004. "On the Autocorrelation Properties of Long-Memory GARCH Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(2), pages 265-282, 03.
- Felix Chan & Michael McAleer, 2001.
"Estimating Smooth Transition Autoregressive Models with GARCH Errors in the Presence of Extreme Observations and Outliers,"
ISER Discussion Paper
0539, Institute of Social and Economic Research, Osaka University.
- Felix Chan & Michael McAleer, 2003. "Estimating smooth transition autoregressive models with GARCH errors in the presence of extreme observations and outliers," Applied Financial Economics, Taylor & Francis Journals, vol. 13(8), pages 581-592.
- Zhang, Yue-Jun & Wei, Yi-Ming, 2010.
"An overview of current research on EU ETS: Evidence from its operating mechanism and economic effect,"
Elsevier, vol. 87(6), pages 1804-1814, June.
- Yue-Jun Zhang & Yi-Ming Wei, 2009. "An overview of current research on EU ETS: Evidence from its operating mechanism and economic effect," CEEP-BIT Working Papers 3, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
- Zaffaroni, Paolo, 2004. "STATIONARITY AND MEMORY OF ARCH([infty infinity]) MODELS," Econometric Theory, Cambridge University Press, vol. 20(01), pages 147-160, February.
- Swanson, Norman R & White, Halbert, 1995. "A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 265-275, July.
- Jeanne, Olivier & Masson, Paul, 2000.
"Currency crises, sunspots and Markov-switching regimes,"
Journal of International Economics,
Elsevier, vol. 50(2), pages 327-350, April.
- Jeanne, Olivier & Masson, Paul R, 1998. "Currency Crises, Sunspots and Markov-Switching Regimes," CEPR Discussion Papers 1990, C.E.P.R. Discussion Papers.
- Nelson, Daniel B., 1992. "Filtering and forecasting with misspecified ARCH models I : Getting the right variance with the wrong model," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 61-90.
- Hentschel, Ludger, 1995. "All in the family Nesting symmetric and asymmetric GARCH models," Journal of Financial Economics, Elsevier, vol. 39(1), pages 71-104, September.
- Abhyankar, A & Copeland, L S & Wong, W, 1997. "Uncovering Nonlinear Structure in Real-Time Stock-Market Indexes: The S&P 500, the DAX, the Nikkei 225, and the FTSE-100," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 1-14, January.
- Zhang, Michael Yuanjie & Russell, Jeffrey R. & Tsay, Ruey S., 2001. "A nonlinear autoregressive conditional duration model with applications to financial transaction data," Journal of Econometrics, Elsevier, vol. 104(1), pages 179-207, August.
- Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 119-136, Suppl. De.
- Wang, Jiang & Grossman, Sanford & Campbell, John, 1993.
"Trading Volume and Serial Correlation in Stock Returns,"
3128710, Harvard University Department of Economics.
- John Y. Campbell & Sanford J. Grossman & Jiang Wang, 1993. "Trading Volume and Serial Correlation in Stock Returns," The Quarterly Journal of Economics, Oxford University Press, vol. 108(4), pages 905-939.
- John Y. Campbell & Sanford J. Grossman & Jiang Wang, 1992. "Trading Volume and Serial Correlation in Stock Returns," NBER Working Papers 4193, National Bureau of Economic Research, Inc.
- Gallant, A Ronald & Rossi, Peter E & Tauchen, George, 1992. "Stock Prices and Volume," Review of Financial Studies, Society for Financial Studies, vol. 5(2), pages 199-242.
- Nam, Kiseok & Pyun, Chong Soo & Arize, Augustine C., 2002. "Asymmetric mean-reversion and contrarian profits: ANST-GARCH approach," Journal of Empirical Finance, Elsevier, vol. 9(5), pages 563-588, December.
- Campbell, John Y. & Lo, Andrew W. & MacKinlay, A. Craig & Whitelaw, Robert F., 1998. "The Econometrics Of Financial Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 2(04), pages 559-562, December.
- Mubariz Hasanov & Tolga Omay, 2007. "Are the Transition Stock Markets Efficient? Evidence from Non-Linear Unit Root Tests," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 7(2), pages 1-12.
- Tse, Y. K. & Tsui, Albert K. C., 1997. "Conditional volatility in foreign exchange rates: Evidence from the Malaysian ringgit and Singapore dollar," Pacific-Basin Finance Journal, Elsevier, vol. 5(3), pages 345-356, July.
- Michael McKenzie & Heather Mitchell, 2002. "Generalized asymmetric power ARCH modelling of exchange rate volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 12(8), pages 555-564.
- David G. McMillan, 2003. "Non-linear Predictability of UK Stock Market Returns," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(5), pages 557-573, December.
- Franc Klaassen, 2002. "Improving GARCH volatility forecasts with regime-switching GARCH," Empirical Economics, Springer, vol. 27(2), pages 363-394.
- Liudas Giraitis, 2004. "LARCH, Leverage, and Long Memory," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(2), pages 177-210.
- Mubariz Hasanov & Tolga Omay, 2008. "Nonlinearities in emerging stock markets: evidence from Europe's two largest emerging markets," Applied Economics, Taylor & Francis Journals, vol. 40(20), pages 2645-2658.
- González-Rivera Gloria, 1998. "Smooth-Transition GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(2), pages 1-20, July.
- Gerlach, Richard & Tuyl, Frank, 2006. "MCMC methods for comparing stochastic volatility and GARCH models," International Journal of Forecasting, Elsevier, vol. 22(1), pages 91-107.
- Thomas Mikosch & Catalin Starica, 2004. "Non-stationarities in financial time series, the long range dependence and the IGARCH effects," Econometrics 0412005, EconWPA.
- Felix Chan & Michael McAleer, 2002. "Maximum likelihood estimation of STAR and STAR-GARCH models: theory and Monte Carlo evidence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 509-534.
- Carol Alexander & Emese Lazar, 2008. "Markov Switching GARCH Diffusion," ICMA Centre Discussion Papers in Finance icma-dp2008-01, Henley Business School, Reading University.
- Nelson, Daniel B., 1990. "Stationarity and Persistence in the GARCH(1,1) Model," Econometric Theory, Cambridge University Press, vol. 6(03), pages 318-334, September.
- Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
- Ane, Thierry & Ureche-Rangau, Loredana, 2006. "Stock market dynamics in a regime-switching asymmetric power GARCH model," International Review of Financial Analysis, Elsevier, vol. 15(2), pages 109-129.
When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:40330. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter)
If references are entirely missing, you can add them using this form.