On filtering and estimation of a threshold stochastic volatility model

Robert J. Elliott; Chuin Ching Liew; Tak Kuen Siu

doi:10.1016/j.amc.2011.05.052

On filtering and estimation of a threshold stochastic volatility model

Robert J. Elliott, Chuin Ching Liew, Tak Kuen Siu^*

^*Corresponding author for this work

Department of Applied Finance

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

Abstract

We derive a nonlinear filter and the corresponding filter-based estimates for a threshold autoregressive stochastic volatility (TARSV) model. Using the technique of a reference probability measure, we derive a nonlinear filter for the hidden volatility and related quantities. The filter-based estimates for the unknown parameters are then obtained from the EM algorithm.

Original language	English
Pages (from-to)	61-75
Number of pages	15
Journal	Applied Mathematics and Computation
Volume	218
Issue number	1
DOIs	https://doi.org/10.1016/j.amc.2011.05.052
Publication status	Published - 1 Sept 2011

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10.1016/j.amc.2011.05.052

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title = "On filtering and estimation of a threshold stochastic volatility model",

abstract = "We derive a nonlinear filter and the corresponding filter-based estimates for a threshold autoregressive stochastic volatility (TARSV) model. Using the technique of a reference probability measure, we derive a nonlinear filter for the hidden volatility and related quantities. The filter-based estimates for the unknown parameters are then obtained from the EM algorithm.",

author = "Elliott, \{Robert J.\} and Liew, \{Chuin Ching\} and Siu, \{Tak Kuen\}",

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AU - Siu, Tak Kuen

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N2 - We derive a nonlinear filter and the corresponding filter-based estimates for a threshold autoregressive stochastic volatility (TARSV) model. Using the technique of a reference probability measure, we derive a nonlinear filter for the hidden volatility and related quantities. The filter-based estimates for the unknown parameters are then obtained from the EM algorithm.

AB - We derive a nonlinear filter and the corresponding filter-based estimates for a threshold autoregressive stochastic volatility (TARSV) model. Using the technique of a reference probability measure, we derive a nonlinear filter for the hidden volatility and related quantities. The filter-based estimates for the unknown parameters are then obtained from the EM algorithm.

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JO - Applied Mathematics and Computation

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On filtering and estimation of a threshold stochastic volatility model

Abstract

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