Deep impulse control: application to interest rate intervention

Bowen Jia and Hoi Ying Wong

Quantitative Finance, 2024, vol. 24, issue 2, 221-232

Abstract: We propose a deep learning framework for impulse control problems involving multivariate stochastic processes, which can be controllable or uncontrollable. We use this framework to estimate central bank interventions on the (controllable) interest rate to stabilize the (uncontrollable) inflation rate, where the two rates are correlated and cointegrated. This method is useful for small banks or insurance companies with high exposure to Treasury securities to predict and stress-test their potential losses from central bank interventions. We also study the mathematical properties of the proposed framework.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2024.2305152 (text/html)
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:taf:quantf:v:24:y:2024:i:2:p:221-232

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

DOI: 10.1080/14697688.2024.2305152

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().