The methodology presented provides a quantitative way to characterize investor behavior and price dynamics within a particular asset class and time period. The methodology is applied to a data set consisting of over 250,000 data points of the S&P 100 stocks during 2004-2018. Using a two-way...

We propose a framework to solve non-linear DSGE models combining approximation and estimation techniques. Instead of relying on a fixed grid, we use Monte Carlo methods to draw samples from the state space, which are used to estimate an approximation for the value or policy functions of...

Understanding the relationship between macroeconomic variables and the stock market is important because macroeconomic variables have a systematic effect on stock market returns. This study uses monthly data from India for the period from April 1994 to July 2018 to examine the long-run...