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...
| Hlavní autoři: | , , |
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| Médium: | Journal article |
| Vydáno: |
2014
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Simulated likelihood inference for stochastic volatility models using continuous particle filtering
Vydáno 2014
Conference item