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...
المؤلفون الرئيسيون: | Pitt, M, Malik, S, Doucet, A |
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التنسيق: | Journal article |
منشور في: |
2014
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مواد مشابهة
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Simulated likelihood inference for stochastic volatility models using continuous particle filtering
حسب: Pitt, M, وآخرون
منشور في: (2014) -
Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models.
حسب: Kim, S, وآخرون
منشور في: (2005) -
Stochastic volatility: likelihood inference and comparison with ARCH models.
حسب: Kim, S, وآخرون
منشور في: (1994) -
Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models.
حسب: Kim, S, وآخرون
منشور في: (2003) -
Maximum Likelihood Inference for Asymmetric Stochastic Volatility Models
حسب: Omar Abbara, وآخرون
منشور في: (2022-12-01)