The Effect of Volatility Spillover from Macroeconomic Factors on Stock Price Crash Risk Under Uncertainty Conditions Across Time Intervals and Structural Breaks Using BEKK-GARCH Models and the ICSS Algorithm
Financial markets, particularly stock markets, are consistently influenced by macroeconomic factors. The volatility of these factors can significantly impact investor behavior and stock price fluctuations. Among these, stock price crash risk, as one of the most critical concerns for investors and economic policymakers, is heavily affected by sudden and unexpected changes in macroeconomic indicators. The present study aims to apply the BEKK-GARCH model to examine the effect of volatility spillover from macroeconomic factors on stock price crash risk under uncertainty conditions across time intervals and structural breaks. In terms of purpose, this research is applied, and in terms of data collection, it is a descriptive, ex-post-facto study. Methodologically, it is analytical and quasi-experimental, and in execution, it is a time-series and cross-sectional study. The statistical population of this research includes companies listed on the Tehran Stock Exchange from 2011 to 2020. Since not all members of the population met the necessary criteria, a purposive sampling method was employed, and 119 companies were selected as the statistical sample. The collected data from the statistical samples, along with the proposed bivariate GARCH models, Granger causality test, Vector Autoregression (VAR) test, and the ICSS algorithm, were analyzed using EViews software (Version 11). The results of the data analysis indicated that macroeconomic variables—including inflation rate, exchange rate, gross domestic product (GDP), money supply, economic growth, interest rate, and liquidity volume—significantly influence stock price crash risk under uncertainty conditions across time intervals and structural breaks.