Nonlinear Combination of Financial Forecast with Genetic Algorithm

Complexity in the financial markets requires intelligent forecasting models for return volatility. In this paper, historical simulation, GARCH, GARCH with skewed student-t distribution and asymmetric normal mixture GRJ-GARCH 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 back-testing mechanisms are performed for forecast comparison of the models. Empirical findings show that the fat-tails are more properly captured by the combination of GARCH with skewed student-t distribution and Extreme Value Theory Hill. Modeling return volatility in the emerging markets needs “intelligent” combinations of Value-at-Risk models to capture the extreme movements in the markets rather than individual model forecast.

[1]  R. Baillie,et al.  The Message in Daily Exchange Rates , 1989 .

[2]  T. Andersen.  THE ECONOMETRICS OF FINANCIAL MARKETS , 1998, Econometric Theory.

[3]  Salih N. Neftçi.  Value at Risk Calculations, Extreme Events, and Tail Estimation , 2000 .

[4]  M. Steel,et al.  On Bayesian Modelling of Fat Tails and Skewness , 1998 .

[5]  David S. Bates.  The Crash of ʼ87: Was It Expected? The Evidence from Options Markets , 1991 .

[6]  James L. McClelland,et al.  PDP models and general issues in cognitive science , 1986 .

[7]  A. McNeil,et al.  Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach , 2000 .

[8]  Yasuhiko Yokote.  Object-Oriented Programming Languages , 1990 .

[9]  A. McNeil.  Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory , 1997, ASTIN Bulletin.

[10]  T. Bollerslev,et al.  A CONDITIONALLY HETEROSKEDASTIC TIME SERIES MODEL FOR SPECULATIVE PRICES AND RATES OF RETURN , 1987 .

[11]  F. C. Palm,et al.  GARCH Models of Volatility , 1996 .

[12]  S. Munch.  THEORY AND EMPIRICAL EVIDENCE , 2004 .

[13]  Shwu-Jane Shieh,et al.  Long memory in stock index futures markets: A value-at-risk approach , 2006 .

[14]  B. M. Hill,et al.  A Simple General Approach to Inference About the Tail of a Distribution , 1975 .

[15]  W. Fuller,et al.  LIKELIHOOD RATIO STATISTICS FOR AUTOREGRESSIVE TIME SERIES WITH A UNIT ROOT , 1981 .

[16]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[17]  Franz C. Palm,et al.  Simple diagnostic procedures for modeling financial time series , 1997 .

[18]  F. Palm.  7 GARCH models of volatility , 1996 .

[19]  Guojun Wu,et al.  Asymmetric Volatility and Risk in Equity Markets , 1997 .

[20]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[21]  J. Wooldridge,et al.  Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances , 1992 .

[22]  Kris Jacobs,et al.  Which GARCH Model for Option Valuation? , 2004, Manag. Sci..

[23]  Guojun Wu,et al.  The Determinants of Asymmetric Volatility , 2001 .

[24]  Peter Christoffersen,et al.  Série Scientifique Scientific Series Option Valuation with Conditional Skewness Option Valuation with Conditional Skewness , 2022 .

[25]  Willem C. Boeschoten.  Theory and Empirical Evidence , 1992 .

[26]  R. Chou,et al.  ARCH modeling in finance: A review of the theory and empirical evidence , 1992 .

[27]  Paul H. Kupiec,et al.  Techniques for Verifying the Accuracy of Risk Measurement Models , 1995 .

[28]  L. Glosten,et al.  On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks , 1993 .

[29]  Dobrivoje Popovic,et al.  Nonlinear combination of forecasts using artificial neural network, fuzzy logic and neuro-fuzzy approaches , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[30]  Esfandiar Maasoumi,et al.  Artificial neural networks for some macroeconomic series: A first report , 1994 .

[31]  Carol Alexander,et al.  Normal Mixture Garch(1,1): Applications to Exchange Rate Modelling , 2004 .

[32]  J. Peters.  Estimating and forecasting volatility of stock indices using asymmetric GARCH models and ( Skewed ) Student-t densities , 2001 .

[33]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[34]  Emese Lazar,et al.  The Equity Index Skew, Market Crashes and Asymmetric Normal Mixture GARCH , 2004 .

[35]  Peter F. Christoffersen.  Evaluating Interval Forecasts , 1998 .