Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models
AbstractRecently, 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 40330.
Date of creation: Jan 2012
Date of revision: May 2012
Volatility; Stock Returns; ARCH; Fractional Integration; MLP; Neural Networks;
Find related papers by JEL classification:
- F30 - International Economics - - International Finance - - - General
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
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- 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.
- 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.
- Conrad, Christian & Rittler, Daniel & Rotfuß, Waldemar, 2010.
"Modeling and Explaining the Dynamics of European Union Allowance Prices at High-Frequency,"
0497, University of Heidelberg, Department of Economics.
- Conrad, Christian & Rittler, Daniel & Rotfuß, Waldemar, 2012. "Modeling and explaining the dynamics of European Union Allowance prices at high-frequency," Energy Economics, 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.
- 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.
- 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.
- 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.
- 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.
- Franc Klaassen, 2002. "Improving GARCH volatility forecasts with regime-switching GARCH," Empirical Economics, Springer, vol. 27(2), pages 363-394.
- Diebold, Francis X. & Inoue, Atsushi, 2001.
"Long memory and regime switching,"
Journal of Econometrics,
Elsevier, vol. 105(1), pages 131-159, November.
- 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.
- 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.
- 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.
- Hillebrand, Eric, 2005. "Neglecting parameter changes in GARCH models," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 121-138.
- John Stachurski & Vance Martin, 2008.
"Computing the Distributions of Economic Models via Simulation,"
Econometric Society, vol. 76(2), pages 443-450, 03.
- John Stachurski, 2006. "Computing the Distributions of Economic Models Via Simulation," KIER Working Papers 615, Kyoto University, Institute of Economic Research.
- 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.
- 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 S119-36, Suppl. De.
- Liudas Giraitis & Remigijus Leipus & Peter M Robinson & Donatas Surgailis, 2003.
"LARCH, Leverage and Long Memory,"
STICERD - Econometrics Paper Series
/2003/460, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- 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.
- Zaffaroni, Paolo, 2004. "STATIONARITY AND MEMORY OF ARCH([infty infinity]) MODELS," Econometric Theory, Cambridge University Press, vol. 20(01), pages 147-160, February.
- 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.
- González-Rivera Gloria, 1998. "Smooth-Transition GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(2), pages 1-20, July.
- 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.
- Zhongfang He & John M. Maheu, 2009.
"Real Time Detection of Structural Breaks in GARCH Models,"
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, revised Jan 2009.
- 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.
- 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.
- Zhang, Yue-Jun & Wei, Yi-Ming, 2010. "An overview of current research on EU ETS: Evidence from its operating mechanism and economic effect," Applied Energy, Elsevier, vol. 87(6), pages 1804-1814, June.
- 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.
- 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.
- repec:wop:humbsf:1998-86 is not listed on IDEAS
- 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.
- 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.
- Tim Bollerslev, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
EERI Research Paper Series
EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Yang, Minxian, 2000. "Some Properties Of Vector Autoregressive Processes With Markov-Switching Coefficients," Econometric Theory, Cambridge University Press, vol. 16(01), pages 23-43, February.
- 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.
- Carol Alexander & Emese Lazar, 2008. "Markov Switching GARCH Diffusion," ICMA Centre Discussion Papers in Finance icma-dp2008-01, Henley Business School, Reading University.
- 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.
- 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, . "RATS programs to estimate Hamilton-Susmel Markov Switching ARCH model," Statistical Software Components RTZ00083, Boston College Department of Economics.
- 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.
- 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 265-75, July.
- 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, . "RATS program to replicate Bollerslev-Mikkelson(1996) FIEGARCH models," Statistical Software Components RTZ00173, Boston College Department of Economics.
- Campbell, John Y & Grossman, Sanford J & Wang, Jiang, 1993.
"Trading Volume and Serial Correlation in Stock Returns,"
The Quarterly Journal of Economics,
MIT Press, vol. 108(4), pages 905-39, November.
- 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.
- Wang, Jiang & Grossman, Sanford & Campbell, John, 1993. "Trading Volume and Serial Correlation in Stock Returns," Scholarly Articles 3128710, Harvard University Department of Economics.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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