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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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Publication status:
Published

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

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
Journal:
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS More from this journal
Volume:
66
Issue:
3
Pages:
527-552
Publication date:
2014-06-01
DOI:
EISSN:
1572-9052
ISSN:
0020-3157
Keywords:
Pubs id:
pubs:462746
UUID:
uuid:fdb5c2fe-d5b8-428f-9e6c-05593fa543cb
Local pid:
pubs:462746
Source identifiers:
462746
Deposit date:
2014-10-15

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