INFORMATION-THEORETIC ANALYSIS OF STOCHASTIC VOLATILITY MODELS

Oliver Pfante and Nils Bertschinger ()
Additional contact information
Oliver Pfante: Frankfurt Institute for Advanced Studies, Systemic Risk Group, Frankfurt a. Main, Hesse 60438, Germany
Nils Bertschinger: Frankfurt Institute for Advanced Studies, Systemic Risk Group, Frankfurt a. Main, Hesse 60438, Germany

Advances in Complex Systems (ACS), 2019, vol. 22, issue 01, 1-21

Abstract: Stochastic volatility models describe asset prices St as driven by an unobserved process capturing the random dynamics of volatility σt. We quantify how much information about σt can be inferred from asset prices St in terms of Shannon’s mutual information in a twofold way: theoretically, by means of a thorough study of Heston’s model; from a machine learning perspective, by means of investigating a family of exponential Ornstein–Uhlenbeck (OU) processes fitted on S&P 500 data.

Keywords: Information theory; stochastic volatility; Bayesian analysis (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S021952591850025X
Access to full text is restricted to subscribers

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:wsi:acsxxx:v:22:y:2019:i:01:n:s021952591850025x

Ordering information: This journal article can be ordered from

DOI: 10.1142/S021952591850025X

Access Statistics for this article

Advances in Complex Systems (ACS) is currently edited by Frank Schweitzer

More articles in Advances in Complex Systems (ACS) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().