Multiple Behavioral Conditions of the Forward Exchange Rates and Stock Market Return in the South Asian Stock Markets During COVID-19: A Novel MT-QARDL Approach

This study examines the short- and long-term effects of multiple quantiles of forward exchange rate premiums (FERPs) and COVID-19 cases on the quantiles of stock market returns (SMRs). We extend the Quantile Autoregressive Distributive Lag (QARDL) model, and the Multiple Threshold Non-linear Autoregressive Distributive Lag (NARDL) model propose a new Multiple Threshold Quantile Autoregressive Distributive Lag (MT-QARDL) approach. Unlike MT-NARDL, QARDL, and NARDL, the MT-QARDL model, which integrates the MT-NARDL model and the quantile regression methodology, captures both short- and long-term locational and sign-based asymmetries. For instance, at lower quantiles for Indian and Sri Lankan SMRs, bearish FERP exerts a positive influence, while bullish FERP has a negative effect during COVID-19. Conversely, bullish FERP negatively affects lower quantiles of SMRs of Bangladesh, India, and Sri Lanka, whereas bearish FERP either yields an opposite effect or remain statistically insignificant during COVID-19. The findings underscore long-term sign-based asymmetries due to the differential bearish and bullish FERP impact during COVID-19. However, in the long term, location-based asymmetries also existed as bullish FERP negative influence the SMRs of India, Bangladesh and Sri Lanka at higher quantiles but SMRs at lower quantiles insignificantly respond to the bullish FERP fluctuations during COVID-19.