Dynamic Algorithmology of Macroeconomic Data on the Stock Market with Emphasis on Economic Turbulences of the TARCH BEKK and VAR Model Performance

Economic algorithmology is used to understand data structure, relationships, and dynamics and the models used to analyze them. In macroeconomics, data includes complex information such as inflation rates, interest rates, GDP, and related financial variables that significantly impact financial markets. This requires specialized knowledge of economic, statistical, and information technology concepts. Therefore, given the importance of this issue, the present study aims to analyze dynamic algorithmic data on the stock market with an emphasis on the economic turbulences of the TARCH BEKK and VAR models in the Tehran Stock Exchange. For this purpose, using information related to macro variables and capital market indices over 10 years, from 2013 to 2022, the research hypotheses were examined and in this regard, the TARCH-BEKK, VAR, and Granger causality models were used to test the research hypotheses. The results show that the release of news resulting from changes in inflation rates, interest rates, exchange rates, and oil prices can affect stock returns.