Identifying Bull and Bear Markets in Stock Returns
Posted: 3 Jun 2002
This article uses a Markov-switching model that incorporates duration dependence to capture nonlinear structure in both the conditional mean and the conditional variance of stock returns. The model sorts returns into a high-return stable state and a low-return volatile state. We label these as bull and bear markets, respectively. The filter identifies all major stock-market downturns in over 160 years of monthly data. Bull markets have a declining hazard functions although the best market gains come at the start of a bull market. Volatility increases with duration in bear markets. Allowing volatility to vary with duration captures volatility clustering.
Keywords: high-frequency data, realized volatility, semi-Markov
JEL Classification: C22, C50, G14
Suggested Citation: Suggested Citation