Mean-variance asset-liability management with inside information

Xingchun Peng and Fenge Chen

Communications in Statistics - Theory and Methods, 2022, vol. 51, issue 7, 2281-2302

Abstract: This paper studies an asset-liability management (ALM) problem under mean-variance criterion with inside information. The asset-liability manager is allowed to invest in a financial market composed of a bond and a stock. The stock price process is governed by a diffusion process with random parameters. The uncontrolled liability process is described by a general diffusion process with hedgeable risks and unhedgeable risks. We model the inside information by a general random variable related to the future values of financial assets and liabilities. By using the Donsker Delta functional technique and the BSDE method, we derive the analytic expressions of efficient strategy and efficient frontier. To illustrate the general result, an example is provided in which the efficient strategy and efficient frontier are obtained in closed form. The comparison of efficient frontiers with and without inside information demonstrates that taking advantage of inside information can improve the efficient frontier.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2020.1772982 (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:lstaxx:v:51:y:2022:i:7:p:2281-2302

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2020.1772982

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().