| 1. |
- Aihara, ShinIchi, et al.
(författare)
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Filtering for Stochastic Volatility by Using Exact Sampling and Application to Term Structure Modeling
- 2015
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Ingår i: INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS. - Cham : Springer Science Business Media. - 9783319108919 - 9783319108902 ; , s. 329-348
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Konferensbidrag (refereegranskat)abstract
- The Bates stochastic volatility model is widely used in the finance problem and the sequential parameter estimation problem becomes important. By using the exact simulation technique, a particle filter for estimating stochastic volatility is constructed. The system parameters are sequentially estimated with the aid of parallel filtering algorithm with the new resampling procedure. The proposed filtering procedure is also applied to the modeling of the term structure dynamics. Simulation studies for checking the feasibility of the developed scheme are demonstrated.
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| 2. |
- Aihara, Shin Ichi, et al.
(författare)
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Adaptive Filtering for Stochastic Volatility by Using Exact Sampling
- 2013
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Ingår i: 10th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2013). - : SciTePress - Science and and Technology Publications. - 9789898565709 ; , s. 326-335
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Konferensbidrag (refereegranskat)abstract
- We study the sequential identification problem for Bates stochastic volatility model, which is widely used as the model of a stock in finance. By using the exact simulation method, a particle filter for estimating stochastic volatility is constructed. The systems parameters are sequentially estimated with the aid of parallel filtering algorithm. To improve the estimation performance for unknown parameters, the new resampling procedure is proposed. Simulation studies for checking the feasibility of the developed scheme are demonstrated.
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| 3. |
- Saha, Saikat, et al.
(författare)
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Particle Based Smoothed Marginal MAP Estimation For General State Space Models
- 2012
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Ingår i: IEEE Transactions on Signal Processing. - : IEEE Signal Processing Society. - 1053-587X .- 1941-0476. ; 61:2, s. 264-273
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Tidskriftsartikel (refereegranskat)abstract
- We consider the smoothing problem for a general state space system using sequential Monte Carlo(SMC) methods. The marginal smoother is assumed to be available in the form of weighted randomparticles from the SMC output. New algorithms are developed to extract the smoothed marginal maximuma posteriori (MAP) estimate of the state from the existing marginal particle smoother. Our method doesnot need any kernel fitting to obtain the posterior density from the particle smoother. The proposedestimator is then successfully applied to find the unknown initial state of a dynamical system and toaddress the issue of parameter estimation problem in state space models
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