داریوش فرید

The present study aimed to investigate the effect of financial fragility on macroeconomic variables within a TVP-VAR model. For this purpose, first the financial fragility variable was calculated. Then, this study evaluated the effect of this variable on macroeconomic variables including economic growth rate, inflation rate, and exchange rate. In this study, the quarterly data for the period 2001-2020 were used. The results indicated that financial fragility had a negative effect on economic growth but a positive effect on exchange rate and inflation by creating uncertainty in the economy. In other words, financial fragility in Iran increased economic fluctuations by reducing economic growth and increasing inflation and exchange rates.

Volatility and risk measurement are essential parameters in risk management programs that can affect economic activities and public confidence in the stock market. Also, these two are the keys in the studies that connect the stock market, economic growth, and other financial factors. In recent years, due to the instability in the Tehran Stock Exchange, controlling the adverse effects caused by the volatility of stock prices, predicting and modeling price dynamics, and measuring risk have become necessary for the participants in this market. In the present research, the class of hidden Markovian index models of conditional variance Heteroskedasticity (HM-GARCH) is used to predict the volatility of stock prices and accounts of the Tehran Stock Exchange. For a comprehensive review, the models are selected to include the characteristics of volatility clustering, asymmetry in volatility (leverage effect), and heavy tail of stock returns (with t-student distribution). Based on RMSE and AME criteria, the HM-EGARCH-Normal Exponential GARCH model with normal distribution is more effective than other models in predicting stock market volatility. Therefore, leverage is necessary to analyze stock market risks using hidden Markov models, but heavy tail distribution is unnecessary. The results indicate that the HM-EGARCH-Normal model appropriately assesses volatility and improves market transparency and risk management forecasts. Also, the VaR and CVaR market risk assessment post-tests using Kupiec and DQ tests do not show evidence of overestimation or underestimation.