Modeling the Dynamic Financial Condition Index (FCI) and Assessing Its Effectiveness in Predicting Iran’s Stock ‎Returns

This paper used a factor-augmented vector autoregressive model with time-varying coefficients to construct a financial conditions index. Time variation in the model’s parameters allowed the weights to be attached to each variable in the index to evolve and evaluate dynamics across time. The ability of the constructed index to predict various variables was also evaluated. The Financial Condition Index (FCI) was estimated by using the TVP-FAVAR method based on the quarterly data of the period of 1989-2019. The variables used included interest rate, exchange rate growth, inflation rate, consumption growth, banking facility growth, total stock market index growth, money supply growth, oil revenue growth, and gross domestic product growth rate. The findings indicated significant volatilities in the model’s parameters. The shock from improving the FCI led to a positive response to the stock market index. According to the findings, the constructed FCI had high predictability.