A new Markov regime-switching count time series approach for forecasting initial public offering volumes and detecting issue cycles

This study proposes a novel Markov regime-switching negative binomial generalized autoregressive conditional heteroskedasticity model for analyzing count data time series. We develop a likelihood-based method for parameter estimation and give the one-step-ahead forecasting algorithms for the mean, variance, and quantiles. An empirical analysis of both the U.S. initial public offering (IPO) and Chinese A-share IPO markets indicates that our method is very efficient in forecasting monthly IPO volumes and detecting hot/cold issue markets. The first-day IPO return is positively correlated with the IPO volume in a hot issue market but negatively correlated with the IPO volume in a cold issue market, in both the U.S. and Chinese IPO markets. However, the average first-day return in the previous hot issue market has a significant positive impact on the current IPO volume for only the U.S. IPO market. Our approach helps to more accurately model and understand the behavior of hot/cold IPO issue markets.