On parameter estimation of stochastic volatility models from stock data using particle filter - Application to AEX index -

Share/Save/Bookmark

Aihara, ShinIchi and Bagchi, Arunabha and Saha, Saikat (2009) On parameter estimation of stochastic volatility models from stock data using particle filter - Application to AEX index -. International journal of innovative computing, information and control, 5 (1). pp. 17-27. ISSN 1349-4198

open access
[img]
Preview
PDF
409kB
Abstract:We consider the problem of estimating stochastic volatility from stock data. The estimation of the volatility process of the Heston model is not in the usual framework of the filtering theory. Discretizing the continuous Heston model to the discrete-time one, we can derive the exact volatility filter and realize this filter with the aid of particle filter algorithm. In this paper, we derive the optimal importance function and construct the particle filter algorithm for the discrete-time Heston model. The parameters contained in system model are also estimated by constructing the augmented states for the system and parameters. The developed method is applied to the real data (AEX index).
Item Type:Article
Copyright:© 2009 ICIC International
Faculty:
Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:
Link to this item:http://purl.utwente.nl/publications/68223
Official URL:http://www.ijicic.org/sss07-02-1.pdf
Export this item as:BibTeX
EndNote
HTML Citation
Reference Manager

 

Repository Staff Only: item control page

Metis ID: 264076