GARCH-Model Identification based on Performance of Information Criteria

The stock market prices are volatile due to influence by many factors such as global trends, local trends, and economic conditions. Identification of Generalized autoregressive conditional heteroscedasticity(GARCH) order for stock data is a challenging task due to more fluctuation in stock prices and high variance in data. GARCH is considered to model the conditional volatility of a stock time series. Stock markets data often exhibit volatility clustering. Though many models which belong to autoregressive conditional heteroscedasticity (ARCH) family has proposed, but all the previous studies gave their affirmative consent on the performance of GARCH (1,1), which is considered the standard model, maybe because of the belief held by many researchers that the first lag of conditional variance can capture all the volatility clustering. This can be highly misguiding, especially when the stock market data has high order variance. The focus of this work is to make use of existing, well-known Information Criteria (IC) to identify the stock indices data-generating-process whenever the GARCH effect is present. Akaike Informations Criteria (AIC), Bayesian Information Criteria(BIC), and Hannan-Quinn information(HQ) criteria have used for this experiment. We studied different models with different parameter values and observed the abilities of information criterion in choosing the correct model from a given pool of models. For higher-order GARCH models and high sample sizes, AIC was able to correctly predict the model with high probability, while BIC and HQ performed well for smaller order models.