Applying multivariate DCC-FIAPARCH model in examination of dynamic conditional correlation between monetary and financial markets in Iran

In this study, we examined Dynamic Conditional Correlation (DCC) between important monetary and financial markets of Iran using multivariate DCC-FIAPARCH model and daily market returns during eleven years, (from 2007(1386) to 2018(1396)), and The existence of hidden characteristics in financial data, i.e. the ability to record long-term memory in data, power or conversion power (unconditional variance to conditional variance due to the addition of observation to the time series) and asymmetry of market reaction to good and bad news have been studied. The results show that OPEC oil market fluctuations haven't effect on the domestic markets of Iran, very high and importance dynamic conditional correlation between the coin market (gold) and exchange rate, the existence of leverage, power and long-term memory with strong ARCH/GARCH characteristics. We also found that market data have clustering characteristics and the assumption of t-student distribution is more appropriate than normal distribution for market return distributions.