Investigating the Relationship Between Liquidity and Asset Prices in Iran's Financial Market with Bayesian Averaging Modeling

The main purpose of this study is to do modeling and investigate the relationship between liquidity and its role in the development of the stock and the housing market. This is accomplished through comparing 10 of Bayesian Averaging methods and principal component analysis carrying out an extensive set of simulation studies based closely on real datasets that span a range of situations encountered in practical data analysis. The data used in this article includes the seasonal data during the period from (1385:1) to (1399:4. The evaluation results show 4 types of PC, so that the first PC shows more than 95% of the changes. In the first PC, the two variables of liquidity and exchange rate have the most weight, and respectively, the second, third and fourth PC are related to oil revenues, legal deposit ratio and coin price. The variables of liquidity, exchange rate and oil revenues have a positive relationship with the index of stock and housing prices, and the variables of the legal deposit ratio and the price of coins have a negative relationship with the index of stock and housing prices. Also, the estimation results of the 10 analyzed models of Bayesian averaging show that in the modeling of the stock price index as well as the modeling of the housing price index, the best models are obtained through applying PCA and the prior distribution of AIC. Therefore, the use of the principal component analysis and the AIC model as a basis for approximating the probabilities of the posterior model, under the condition that the probabilities of the prior model are similar to Zellner's g-prior, is approved in this research.