Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/185607
Authors: 
Blasques, Francisco
Koopman, Siem Jan
Nientker, Marc
Year of Publication: 
2018
Series/Report no.: 
Tinbergen Institute Discussion Paper TI 2018-088/III
Abstract: 
Locally explosive behavior is observed in many economic and financial time series when bubbles are formed. We introduce a time-varying parameter model that is capable of describing this behavior in time series data. Our proposed model can be used to predict the emergence, existence and burst of bubbles. We adopt a flexible observation driven model specification that allows for different bubble shapes and behavior. We establish stationarity, ergodicity, and bounded moments of the data generated by our model. Furthermore, we obtain the consistency and asymptotic normality of the maximum likelihood estimator. Given the parameter estimates, our filter is capable of extracting the unobserved bubble process from observed data. We study finite-sample properties of our estimator through a Monte Carlo simulation study. Finally, we show that our model compares well with noncausal models in a financial application concerning the Bitcoin/US dollar exchange rate.
Subjects: 
bubbles
observation driven models
noncausal models
stationary
ergodic
consistency
asymptotic normality
exchange rates
JEL: 
C22
C58
G10
Document Type: 
Working Paper
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