Adaptive Filtering for Stochastic Volatility by using Exact Sampling

ShinIchi Aihara, Arunabha Bagchi, Saikat Saha

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.

References

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Paper Citation


in Harvard Style

Aihara S., Bagchi A. and Saha S. (2013). Adaptive Filtering for Stochastic Volatility by using Exact Sampling . In Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8565-70-9, pages 326-335. DOI: 10.5220/0004454703260335


in Bibtex Style

@conference{icinco13,
author={ShinIchi Aihara and Arunabha Bagchi and Saikat Saha},
title={Adaptive Filtering for Stochastic Volatility by using Exact Sampling},
booktitle={Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2013},
pages={326-335},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004454703260335},
isbn={978-989-8565-70-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Adaptive Filtering for Stochastic Volatility by using Exact Sampling
SN - 978-989-8565-70-9
AU - Aihara S.
AU - Bagchi A.
AU - Saha S.
PY - 2013
SP - 326
EP - 335
DO - 10.5220/0004454703260335