A further extension of duration-dependent models

Abstract

The duration dependence of stock market cycles has been investigated using the Markov switching model where the market conditions are unobservable. In conventional modeling, restrictions are imposed such that the transition probability is a monotonic function of duration, which is truncated at a certain value. This paper proposes a model that is free from these arbitrary restrictions and nests the conventional models. In the model, the parameters that characterize the transition probability are formulated in the state space. Empirical results from several stock markets show that the duration structures greatly differ depending on countries. These structures are not necessarily monotonic functions of duration and, therefore, cannot be described by the conventional models.duration, world stock markets, Markov switching model, non-parametric model, Gibbs sampling, marginal likelihood,

Are Chinese Stock Market Cycles Duration Independent?

“…6 6 A shortcoming of the parametric test is that the power of the test will be low if the structure of duration dependence is nonmonotonic under the alternative hypothesis. As pointed out by Isogai, Kanoh, and Tokunaga (2008), who estimate the transition probability function for Markov switching models using Gibbs sampling and Kalman filtering algorithm without any parametric assumption, the structure of duration dependence is not necessarily monotonic and, therefore, cannot be described by conventional duration‐dependent Markov switching models with a monotonic logistic function of duration. In addition, the small sample size also is a reason for not using parametric tests.…”

Identifying Bull and Bear Markets in Japan

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