We employ the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator(MS-LPPLS-CI)approach to identify positive and negative bubbles in the short-,medium,and long-term for the Indian stock market,using wee...We employ the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator(MS-LPPLS-CI)approach to identify positive and negative bubbles in the short-,medium,and long-term for the Indian stock market,using weekly data from November 2003 to December 2020.We use a nonparametric causality-in-quantiles approach to analyze the predictive impact of monetary policy shocks on bubble indicators.We find,in general,strong evidence of predictability across the entire conditional distribution for the two monetary policy shock factors,with stronger impacts for negative bubbles.Our findings have critical implications for the Reserve Bank of India,academics,and investors.展开更多
文摘We employ the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator(MS-LPPLS-CI)approach to identify positive and negative bubbles in the short-,medium,and long-term for the Indian stock market,using weekly data from November 2003 to December 2020.We use a nonparametric causality-in-quantiles approach to analyze the predictive impact of monetary policy shocks on bubble indicators.We find,in general,strong evidence of predictability across the entire conditional distribution for the two monetary policy shock factors,with stronger impacts for negative bubbles.Our findings have critical implications for the Reserve Bank of India,academics,and investors.
