Business Cycle and Stock Market Volatility: A Particle Filter Approach
Casarin, Roberto; Trecroci, Carmine (2006), Business Cycle and Stock Market Volatility: A Particle Filter Approach. https://basepub.dauphine.fr/handle/123456789/6830
TypeDocument de travail / Working paper
Series titleCahiers du CEREMADE
MetadataShow full item record
Abstract (EN)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.
Subjects / KeywordsMarkov Switching; Stochastic Volatility; Business Cycle; Equity Market; Particle Filters; Sequential Monte Carlo
JELC11 - Bayesian Analysis: General
C15 - Statistical Simulation Methods: General
C22 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
C63 - Computational Techniques; Simulation Modeling
G10 - General
E32 - Business Fluctuations; Cycles
E44 - Financial Markets and the Macroeconomy
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