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Bayesian modeling of financial returns: a relationship between volatility and trading volume. (English) Zbl 1224.91179
This paper investigates the joint distribution of daily returns and trading volume based on the modified mixture model with Markov switching volatility specification. The authors extend this specification to heavier-tailed distributions for the returns using an algorithm based on Markov Chain Monte Carlo simulation methods is constructed to estimate all parameters and latent quantities in the model using the Bayesian approach. The authors are able to produce an estimate of the latent information process, which can be used in financial modeling. The obtained estimation result shows that the estimate of the persistence parameters drops and the estimate of the variance error rises in the volatility specification. Markov switching models are supported by the British Petroleum data.

MSC:
91G60 Numerical methods (including Monte Carlo methods)
65C05 Monte Carlo methods
65C40 Numerical analysis or methods applied to Markov chains
91G70 Statistical methods; risk measures
Software:
Scythe
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