Simulated likelihood inference for stochastic volatility models using continuous particle filtering

Simulated likelihood inference for stochastic volatility models using continuous particle filtering

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Discrete-time stochastic volatility (SV) models have generated a considerable literature in financial econometrics. However, carrying out inference for these models is a difficult task and often relies on carefully customized Markov chain Monte Carlo techniques. Our contribution here is twofold. Fir...

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Bibliographic Details
Main Authors:	Pitt, M, Malik, S, Doucet, A
Format:	Journal article
Published:	2014

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