Langrock, R., Michelot, T., Sohn, A. et al. (1 more author) (2014) Semiparametric stochastic volatility modelling using penalized splines. Computational Statistics, 30 (2). pp. 517-537. ISSN 0943-4062
Abstract
Stochastic volatility (SV) models mimic many of the stylized facts attributed to time series of asset returns, while maintaining conceptual simplicity. The commonly made assumption of conditionally normally distributed or Student-t-distributed returns, given the volatility, has however been questioned. In this manuscript, we introduce a novel maximum penalized likelihood approach for estimating the conditional distribution in an SV model in a nonparametric way, thus avoiding any potentially critical assumptions on the shape. The considered framework exploits the strengths both of the powerful hidden Markov model machinery and of penalized B-splines, and constitutes a powerful and flexible alternative to recently developed Bayesian approaches to semiparametric SV modelling. We demonstrate the feasibility of the approach in a simulation study before outlining its potential in applications to three series of returns on stocks and one series of stock index returns.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014 Springer Verlag. This is an author produced version of a paper subsequently published in Computational Statistics. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | B-splines; Cross-validation; Forward algorithm; Hidden Markov model; Numerical integration; Penalized likelihood |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Sep 2016 14:12 |
Last Modified: | 11 Apr 2018 17:17 |
Published Version: | http://dx.doi.org/10.1007/s00180-014-0547-5 |
Status: | Published |
Publisher: | Springer Verlag |
Refereed: | Yes |
Identification Number: | 10.1007/s00180-014-0547-5 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:94382 |