Latent State and Parameter Estimation of Stochastic Volatility/Jump Models via Particle Filtering

Particle filtering in stochastic volatility/jump models has gained significant attention in the last decade, with many distinguished researchers adding their contributions to this new field. Golightly (2009), Carvalho et al. (2010), Johannes et al. (2009) and Aihara et al. (2008) all attempt to extend the work of Pitt and Shephard (1999) and Liu and Chen (1998) to adapt particle filtering to latent state and parameter estimation in stochastic volatility/jump models. This dissertation will review their extensions and compare their accuracy at filtering the Bates stochastic volatility model. Additionally, this dissertation will provide an overview of particle filtering and the various contributions over the last three decades. Finally, recommendations will be made as to how to improve the results of this paper and explore further research opportunities.

Grantor dc:publisher.institution

African Institute of Financial Markets and Risk Management

Year dc:date.issued

2018

Author dc:creator

• Soane, Andrew

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/29223

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/29223