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Estimating a semiparametric asymmetric stochastic volatility model with a Dirichlet process mixture. (English) Zbl 1293.91141
Summary: We extend the asymmetric, stochastic, volatility model by modeling the return-volatility distribution nonparametrically. The novelty is modeling this distribution with an infinite mixture of normals, where the mixture unknowns have a Dirichlet process prior. Cumulative Bayes factors show our semiparametric model accurately forecasting market returns. During tranquil markets, expected volatility rises (declines, then rises as the shock increases) when the market shock is negative (positive). This asymmetry is muted when the market is volatile. In other words, when times are good, no news is good news, but during bad times, neither good nor bad news matters with regards to volatility.

MSC:
91B70 Stochastic models in economics
62G05 Nonparametric estimation
62F15 Bayesian inference
62H30 Classification and discrimination; cluster analysis (statistical aspects)
Software:
sapa
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