Monetary Policy Shocks and Multi-scale Positive and Negative Bubbles in an Emerging Country: The Case of India

First, we employ the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to identify both positive and negative bubbles in the short-, medium, and long-term for the Indian stock market. We successfully detect major crashes and rallies during the weekly period from November 2003 to December 2020. Second, we utilize a nonparametric causality-in-quantiles approach to analyze the predictive impact of monetary policy shocks on the six bubble indicators. This econometric framework allows us to circumvent potential misspecification due to nonlinearity and instability, rendering the results of no causal influence derived from a linear framework invalid. The two factors of monetary policy shocks namely, the target and path associated with short- and long-term interest rates, reveal strong evidence of predictability for the six bubble indicators across their entire conditional distributions. We observe relatively stronger impacts for the negative bubble indicators due to the target factor rather than the path factor of monetary policy shocks. Our findings have significant implications for the Reserve Bank of India, as well as for academics and investors.