A Flexible State-Space Model with Application to Stochastic Volatility

Christian Gourieroux and Yang Lu

No 2016-39, Working Papers from Center for Research in Economics and Statistics

Abstract: We introduce a general state-space (or latent factor) model for time series and panel data. The state process has a polynomial expansion based dynamics that can approximate any Markov dynamics arbitrarily well, and has a latent, endogenous switching regime interpretation. The resulting state-space model is associated with simulation-free, recursive formulas for prediction and ltering, as well as the maximum composite likelihood estimation method, with an extremely low computational cost. When applied to the stochastic volatility (SV) of asset returns, the model can capture, in a uni ed framework, stylized facts such as heavy tailed return, volatility feedback, as well as time irreversibility. The methodology is illustrated using Apple stock return data, which con rms the improvement of our model with respect to a benchmark SV model.

Keywords: Endogenous regime switching; polynomial expansion; composite likelihood; time irreversibility; volatility feedback; copula. (search for similar items in EconPapers)
JEL-codes: C14 C32 (search for similar items in EconPapers)
Pages: 52
Date: 2016-11
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