EconPapers    
Economics at your fingertips  
 

Non-Gaussian Stochastic Volatility Model with Jumps via Gibbs Sampler

Arthur T. Rego and Thiago R. dos Santos

Papers from arXiv.org

Abstract: In this work, we propose a model for estimating volatility from financial time series, extending the non-Gaussian family of space-state models with exact marginal likelihood proposed by Gamerman, Santos and Franco (2013). On the literature there are models focused on estimating financial assets risk, however, most of them rely on MCMC methods based on Metropolis algorithms, since full conditional posterior distributions are not known. We present an alternative model capable of estimating the volatility, in an automatic way, since all full conditional posterior distributions are known, and it is possible to obtain an exact sample of parameters via Gibbs Sampler. The incorporation of jumps in returns allows the model to capture speculative movements of the data, so that their influence does not propagate to volatility. We evaluate the performance of the algorithm using synthetic and real data time series. Keywords: Financial time series, Stochastic volatility, Gibbs Sampler, Dynamic linear models.

New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
Date: 2018-08, Revised 2018-10
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link)
http://arxiv.org/pdf/1809.01501 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1809.01501

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().

 
Page updated 2018-10-18
Handle: RePEc:arx:papers:1809.01501