The recent observed decline of business cycle variability suggests that broad macroeconomic risk may have fallen as well. This may in turn have some impact on equity risk premia. We investigate the latent structures in the volatilities of the business cycle and stock market valuations by estimating a Markov switching stochastic volatility model. We propose a sequential Monte Carlo technique for the Bayesian inference on both the unknown parameters and the latent variables of the hidden Markov model. Sequential importance sampling is used for filtering the latent variables and kernel estimator with a multiple-bandwidth is employed to reconstruct the parameter posterior distribution. We find that the switch to lower variability has occurred in both business cycle and stock market variables along similar patterns.
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Paper provided by University of Brescia, Department of Economics in its series Working Papers with number
ubs0603.
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
Michele Polo & Carlo Scarpa, 2003.
"Entry Without Competition,"
Working Papers
245, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
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