Modeling and Forecasting TEDPIX using Intraday Data in the Tehran Securities Exchange

Abstract

The aim of the research is conditional and unconditional models performance comparison of volatility forecasting on Tehran dividend and price index (TEDPIX), using the intraday data, based on root mean square error in Tehran Securities Exchange (TSE). In research, it is tried to analyze the total price index behavior by conditional (Arch, Garch, Egarch, Gloston Garch and Rankle) and unconditional (Moving Average) and mixed models to determine the best forecasting model for price and dividend index of  active companies in Tehran Securities Exchange. Indeed, the research results will be an analytical review on which kinds of variance dissimilarity models has the more accurate forecasting. The research population focuses on Iran capital market and includes dividend and price index (TEDPIX) data of Tehran Securities exchange. The sample contains 10624 observed days from 2009 until 2015 with 30 minutes sampling interval which have been analyzed.

The research results indicate that for the reason of smaller error on mean square error the mixed model, designed based on conditional model, is more accurate than other reviewed models. Also the return fluctuations are more influenced by closer data because in the mixed model the moving average, which uses data from the 60 and 120 past hours, has more accurate prediction on return fluctuation. Finally, Diebold and Mariano test statistics was used to determine predictive accuracy in both models that has the lowest root mean square error (RMSE),and as a result there was no significant difference between accuracy of these two models.