Simulated likelihood inference for stochastic volatility models using continuous particle filtering
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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| Format: | Conference item |
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Kluwer Academic Publishers
2014
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