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
Understanding the interplay between investor sentiment and cryptocurrency returns has become a critical area of research.Indeed,this study aims to uncover the role of Google investor sentiment on cryptocurrency return...Understanding the interplay between investor sentiment and cryptocurrency returns has become a critical area of research.Indeed,this study aims to uncover the role of Google investor sentiment on cryptocurrency returns(including Bitcoin,Litecoin,Ethereum,and Tether),especially during the 2017-18 bubble(January 01,2017,to December 31,2018)and the COVID-19 pandemic(January 01,2020,to March 15,2022).To achieve this,we use two techniques:quantile causality and wavelet coherence.First,the quantile causality test revealed that investors’optimistic sentiments have notably higher cryptocurrency returns,whereas pessimistic sentiments have significantly opposite effects.Moreover,the wavelet coherence analysis shows that co-movement between investor sentiment and Tether cannot be considered significant.This result supports the role of Tether as a stablecoin in portfolio diversification strategies.In fact,the findings will help investors improve the accuracy of cryptocurrency return forecasts in times of stressful events and pave the way for enhanced decision-making utility.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.
文摘Understanding the interplay between investor sentiment and cryptocurrency returns has become a critical area of research.Indeed,this study aims to uncover the role of Google investor sentiment on cryptocurrency returns(including Bitcoin,Litecoin,Ethereum,and Tether),especially during the 2017-18 bubble(January 01,2017,to December 31,2018)and the COVID-19 pandemic(January 01,2020,to March 15,2022).To achieve this,we use two techniques:quantile causality and wavelet coherence.First,the quantile causality test revealed that investors’optimistic sentiments have notably higher cryptocurrency returns,whereas pessimistic sentiments have significantly opposite effects.Moreover,the wavelet coherence analysis shows that co-movement between investor sentiment and Tether cannot be considered significant.This result supports the role of Tether as a stablecoin in portfolio diversification strategies.In fact,the findings will help investors improve the accuracy of cryptocurrency return forecasts in times of stressful events and pave the way for enhanced decision-making utility.
