We develop a new asset price model where the dynamic structure of the asset price, after the fundamental value is removed, is subject to two different regimes. One regime reflects the normal period where the asset price divided by the dividend is assumed to follow a mean-reverting process around a stochastic long run mean. This latter is allowed to account for possible smooth structural change. The second regime reflects the bubble period with explosive behavior. Stochastic switches between two regimes and non-constant probabilities of exit from the bubble regime are both allowed. A Bayesian learning approach is employed to jointly estimate the latent states and the model parameters in real time. An important feature of our Bayesian method is that we are able to deal with parameter uncertainty, and at the same time, to learn about the states and the parameters sequentially, allowing for real time model analysis. This feature is particularly useful for market surveillance. Analysis using simulated data reveals that our method has better power for detecting bubbles compared to existing alternative procedures. Empirical analysis using price/dividend ratios of S&P500 highlights the advantages of our method.
Parameter Learning, Markov Switching, MCMC
Fulop, Andras and YU, Jun.
Bayesian Analysis of Bubbles in Asset Prices. (2014). Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1589
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