Filtering and identification of stochastic volatility for parabolic type factor models

ShinIchi Aihara; Arunabha Bagchi

Filtering and identification of stochastic volatility for parabolic type factor models

ShinIchi Aihara
, Arunabha Bagchi

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

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Abstract

We consider the dynamics of forward rate process which is modeled by a parabolic type infinite-dimensional factor model with stochastic volatility. The parameters included in the stochastic volatility dynamics are estimated from the factor process as the observation data. Based on the maximum likelihood technique, we propose the off-line identification scheme and provide some numerical examples.

Original language	Undefined
Pages (from-to)	1-9
Number of pages	9
Journal	International journal of innovative computing, information and control
Volume	2
Issue number	2/5
Publication status	Published - Oct 2006

Keywords

METIS-238080
EWI-6112
IR-66192

Access to Document

http://www.ijicic.org/sss05-03.pdf

Cite this

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title = "Filtering and identification of stochastic volatility for parabolic type factor models",

abstract = "We consider the dynamics of forward rate process which is modeled by a parabolic type infinite-dimensional factor model with stochastic volatility. The parameters included in the stochastic volatility dynamics are estimated from the factor process as the observation data. Based on the maximum likelihood technique, we propose the off-line identification scheme and provide some numerical examples.",

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language = "Undefined",

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T1 - Filtering and identification of stochastic volatility for parabolic type factor models

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AU - Bagchi, Arunabha

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N2 - We consider the dynamics of forward rate process which is modeled by a parabolic type infinite-dimensional factor model with stochastic volatility. The parameters included in the stochastic volatility dynamics are estimated from the factor process as the observation data. Based on the maximum likelihood technique, we propose the off-line identification scheme and provide some numerical examples.

AB - We consider the dynamics of forward rate process which is modeled by a parabolic type infinite-dimensional factor model with stochastic volatility. The parameters included in the stochastic volatility dynamics are estimated from the factor process as the observation data. Based on the maximum likelihood technique, we propose the off-line identification scheme and provide some numerical examples.

KW - METIS-238080

KW - EWI-6112

KW - IR-66192

M3 - Article

SN - 1349-4198

VL - 2

SP - 1

EP - 9

JO - International journal of innovative computing, information and control

JF - International journal of innovative computing, information and control

IS - 2/5

ER -