Ozun, Alper and Cifter, Atilla (2007): Nonlinear Combination of Financial Forecast with Genetic Algorithm.

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Abstract
Complexity in the financial markets requires intelligent forecasting models for return volatility. In this paper, historical simulation, GARCH, GARCH with skewed studentt distribution and asymmetric normal mixture GRJGARCH models are combined with Extreme Value Theory Hill by using artificial neural networks with genetic algorithm as the combination platform. By employing daily closing values of the Istanbul Stock Exchange from 01/10/1996 to 11/07/2006, Kupiec and Christoffersen tests as the backtesting mechanisms are performed for forecast comparison of the models. Empirical findings show that the fattails are more properly captured by the combination of GARCH with skewed studentt distribution and Extreme Value Theory Hill. Modeling return volatility in the emerging markets needs “intelligent” combinations of ValueatRisk models to capture the extreme movements in the markets rather than individual model forecast.
Item Type:  MPRA Paper 

Institution:  Marmara University 
Original Title:  Nonlinear Combination of Financial Forecast with Genetic Algorithm 
Language:  English 
Keywords:  Forecast combination; Artificial neural networks; GARCH models; Extreme value theory; Christoffersen test 
Subjects:  G  Financial Economics > G0  General C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models; Multiple Variables > C32  TimeSeries Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models 
Item ID:  2488 
Depositing User:  Atilla Cifter 
Date Deposited:  02. Apr 2007 
Last Modified:  12. Feb 2013 15:22 
References:  Alexander, C. and Lazar, E., (2005) “The Equity Index Skew, Market Crashes and Asymmetric Normal Mixture Garch,” ISMA Center, Mimeo Alexander, C. and Lazar, E., (2006) “Normal Mixture GARCH(1,1):Applications to Exchange Rate Modeling,” Journal of Applied Econometrics 21(3), pp.307336. Baillie, R. T. and Bollerslev, T., (1989) “The Message in Daily Exchange Rates: A ConditionalVariance Tale,” Journal of Business and Economic Statistics, 7, pp.297305 Bates, D. S., (1991) “The Crash of ’87: Was It Expected? The Evidence from Options Markets,” Journal of Finance, 46, pp.10091044 Bekaert, G., and Wu, G., (2000) “Asymmetric Volatility and Risk Equity Markets,” The Review of Financial Studies, 13(1), pp.142 Bollerslev, T., (1986) “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, 31, pp.307–327. Bollerslev, T. (1987) “A Conditional Heteroskedasticity Time Series Model for Speculative Prices and Rates of Return,” Review of Economic and Statistics. 69, pp.542547 Bollerslev, T., Chou, R.Y. and Kroner, K.F., (1992) “ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence,” Journal of Economics and Statistics, 69, pp.542547 Bollerslev, T. and Woolridge, J. M. (1992) “Quasimaximum Likelihood Estmation Inference in Dynamic Models with Timevarying Covariances,” Econometric Theory, 11, pp.143172 Chang, Lo, Chen and Huang (2006) “Journal of Information & Optimization Sciences,” 27 (2006), No. 3, pp. 615–628 Christoffersen, P.F. (1998) “Evaluating Interval Forecasts,” International Economic Review, 39, pp.841862. Christoffersen, P. F., and Jacobs, K., (2004) “Which Garch Model for Option Valuation?,” Management Science, 50, pp.12041221 Christoffersen, P. F., Heston, S. and Jacobs, K. (2004) “Option Valuation with Conditional Skewness,” Forthcoming in The Journal of Econometrics. Dickey, D. A., Fuller, W.A. (1981) “Likelihood ratio statistics for autoregressive time series with a unit root,” Econometrica, 49, pp.1057–1072. Doornik, J.A. (1999) “An Object Oriented Programming Language,” Timberlake Consultant, Third Ed. Engle, F., (1982) “Autoregressive Conditional Heteroscedasticity with Estimate of the Variance of United Kingdom Inflation,” Econometrica, 50, pp.9871007 Fernandez, Carmen, and Mark Stell. (1998) “On Bayesian Modeling of fat tails and skewness,” Journal of the American Statistical Association, 93, pp.359371 Glosten, L. R., Jagahannathan, R., and Runkle, D. E., (1993) “On the Relationship between the Expected Value and The Volatility of the Nominal Excess Return on Stocks,” Journal of Finance, 48, pp.17791801 HechtNielsen.R (1990) Neurocomputing, Wokingham, England: AddisonWesley Publishing Company Ltd. Hill, B. M. (1975) “A simple general approach to inference about the tail of a distribution,” Annals of Statistics, 3, 11631174. Holland, J. (1965) “Universal spaces: A basis for studies of adaptation,” In Automata Theory. Caianiello, E. R. (ed.) Academic Press. 21830. Hsieh, D. A. (1989) “Modeling Heteroskedasticity in Daily Foreign Exchange Rates,” Journal of Business and Economic Statistics, 7, pp.307317 Kupiec, P. H. (1995) “Techniques for Verifying the Accuracy of Risk Measurement Models,” Journal of Derivatives, winter, pp. 7384 Liu (2005) “ValueatRisk Model Combination Using Artificial Neural Networks,” Emory University Working Paper Series. Maasoumi, E., A. Khotanzad, and A. Abaye (1994) “Artificial Neural Networks for Some Macroeconomic Series : A First Report,” Econometric Reviews, 13, No. 1. McNeil, A. J., (1996) "Estimating The Tails of Loss Severity Distributions Using Extreme Value Theory, Mimeo. ETH Zentrum, Zürich. McNeil A. J. and Frey, R. (2000) “Estimation of tailrelated risk measures for Heteroscedastic Financial time series: An extreme value approach,” Journal of Empirical Finance, 7, 271300. Neftci, S., (2000) “Value at Risk Calculations, Extreme Events, and Tail Estimation,” The Journal of Derivatives, Spring 2000. Ozun, A., (2006) Theoretical Importance of Artificial Neural Networks For The Efficiency of Financial Markets, Proceedings of 5th International Finance Symposium: Integration in the Financial Markets, Vienna University&Marmara University with Cooperation of Istanbul Stock Exchange, 2526 May, 2006, Istanbul Palit, AK and Popovic, D. (2000) “Nonlinear Combination of Forecasts Using Artificial Neural Networks, Fuzzy Logic and NeuroFuzzy Approach,” FUZZIEEE, Vol. 2, pp. 566571, 2000. Pagan, A., (1996) “The Econometrics of Financial Markets,” Journal of Empirical Finance, 3, pp.15102. Palm, F., (1996) “Garch Models of Volatility”, in Handbook of Statistics, ed. By G.Maddala, and C.Rao, pp.209240, Elsevier Science, Amsterdam. Palm, F. and Vlaar, P. JG., (1997) “Simple Diagnostics Procedures for Modeling Financial Time Series,” Allgemeines Statistisches Archiv, 81, pp.85101 Peters, JP., (2001) Estimating and Forecasting Volatility of Stock Indices Using Asymmetric Garch Models and (Skewed) Studentt Densities, Mimeo, Ecole d’Admin. des Affaires, Unv.of Li`ege. Rumelhart, D.E. & McClelland, J.L. (1986) “PDP Models and General Issues in Cognitive Science”. In D.E. Rumelhart & J.L. McClelland (Eds.), Parallel Distributed Processing. Vol. 1. Cambridge, MA: MIT Press/Bradford Books. Saltoğlu, B. (2003) A High Frequency Analysis of Financial Risk and Crisis: An Empirical Study on Turkish Financial Market, Yaylım Publishing, Istanbul Sarma, M., Thomas, S. and Shah, A. (2001) Selection of ValueatRisk Models, Mimeo Shanming Shi, Li D Xu, Bao Liu. (1996) “Application of artificical neural networks to the nonlinear combinmation of forecasts,” Expert Systems,1996,13(3):195201 Tang, T.L. and Shieh, S. J., (2006) “LongMemory in Stock Index Futures Markets: A ValueatRisk Approach,” Phsica A, Vol.366, pp.437448 White, H. (1994) “Neural Networks,” Econometric Reviews, Vol.13, No.1. Wu, G., (2001) “The Determinants of Asymmetric Volatility,” The Review of Financial Studies. 14(3), pp.837859 
URI:  http://mpra.ub.unimuenchen.de/id/eprint/2488 