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 languageUndefined
Pages (from-to)1-9
Number of pages9
JournalInternational journal of innovative computing, information and control
Volume2
Issue number2/5
Publication statusPublished - Oct 2006

Keywords

  • METIS-238080
  • EWI-6112
  • IR-66192

Cite this

Aihara, ShinIchi ; Bagchi, Arunabha. / Filtering and identification of stochastic volatility for parabolic type factor models. In: International journal of innovative computing, information and control. 2006 ; Vol. 2, No. 2/5. pp. 1-9.
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Aihara, S & Bagchi, A 2006, 'Filtering and identification of stochastic volatility for parabolic type factor models' International journal of innovative computing, information and control, vol. 2, no. 2/5, pp. 1-9.

Filtering and identification of stochastic volatility for parabolic type factor models. / Aihara, ShinIchi; Bagchi, Arunabha.

In: International journal of innovative computing, information and control, Vol. 2, No. 2/5, 10.2006, p. 1-9.

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

TY - JOUR

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

AU - Aihara, ShinIchi

AU - Bagchi, Arunabha

PY - 2006/10

Y1 - 2006/10

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

VL - 2

SP - 1

EP - 9

JO - International journal of innovative computing, information and control

JF - International journal of innovative computing, information and control

SN - 1349-4198

IS - 2/5

ER -

Aihara S, Bagchi A. Filtering and identification of stochastic volatility for parabolic type factor models. International journal of innovative computing, information and control. 2006 Oct;2(2/5):1-9.

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