Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/195431 
Year of Publication: 
2017
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 5 [Issue:] 4 [Publisher:] MDPI [Place:] Basel [Year:] 2017 [Pages:] 1-23
Publisher: 
MDPI, Basel
Abstract: 
We develop a new 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. 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 good power properties for detecting bubbles. Empirical analysis using price-dividend ratios of S&P500 highlights the advantages of our method.
Subjects: 
parameter learning
markov switching
MCMC
real time bubble detection
JEL: 
C11
C13
C32
G12
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

Files in This Item:
File
Size
394.49 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.