The pattern of long-term volatility transferring to the industry sector in the Tehran Stock Exchange using the mixed data model (GARCH-MIDAS approach)

In financial markets, risk management is tied to the concept of fluctuations and it is impossible to manage it without knowing why fluctuations occur. Fundamental analysis of fluctuations requires the study of very large forces in the market, and this is very difficult due to the lack of sufficient data, uncertainty about exogenous variables and the time required to study them. In this study, an attempt is made to identify the pattern of uncertainty transfer of variables affecting the long-term turbulence of industrial sector returns on the Tehran Stock Exchange. In this regard, the mixed data model (GARCH-MIDAS) and data of various internal and external variables with daily, monthly and seasonal frequencies in the period 2009-2010 have been used. In the stage of selecting variables, different models, variables affecting the turbulence of the efficiency of the industrial sector in the long run were estimated and the variables that had a significant effect on the transmission of fluctuations to the industrial sector were selected. Among the various domestic and foreign variables, uncertainty due to inflation, exchange rate, gold price and oil price have transmitted turbulence to the industrial sector in the stock exchange and in the long run. In addition, the results of the final model show that inflation is the most effective source of instability in the industrial sector in the Tehran Stock Exchange, which indicates that the capital market is more sensitive to domestic variables. According to the final model estimates, inflation, exchange rate and gold price uncertainty in the short and long term have had a positive and significant effect on industry turbulence. It is estimated