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Simulated likelihood inference for stochastic volatility models using continuous particle filtering

Abstract:

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. First, we propose a new SV model, namely SV-GARCH, which bridges the gap between SV and GARCH models: it has the attractive feature of inheriting unconditional properties similar to the standard GARCH...

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Publisher copy:
10.1007/s10463-014-0456-y

Authors


Publisher:
Kluwer Academic Publishers
Volume:
66
Issue:
3
Pages:
527-552
Publication date:
2014
DOI:
EISSN:
1572-9052
ISSN:
0020-3157
URN:
uuid:af33f19d-89bf-44d3-ba01-2c2ede3578ab
Source identifiers:
476993
Local pid:
pubs:476993

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