A least-squares Monte Carlo approach to the estimation of enterprise risk

Hongjun Ha () and Daniel Bauer ()
Additional contact information
Hongjun Ha: Saint Joseph’s University
Daniel Bauer: University of Wisconsin-Madison

Finance and Stochastics, 2022, vol. 26, issue 3, No 2, 417-459

Abstract: Abstract The estimation of enterprise risk for financial institutions entails a re-evaluation of the company’s economic balance sheet at a future time for a (large) number of stochastic scenarios. The current paper discusses tackling this nested valuation problem based on least-squares Monte Carlo techniques familiar from American option pricing. We formalise the algorithm in an operator setting and discuss the choice of the regressors (“basis functions”). In particular, we show that the left singular functions of the corresponding conditional expectation operator present robust basis functions. Our numerical examples demonstrate that the algorithm can produce accurate results at relatively low computational costs.

Keywords: Risk management; Least-squares Monte Carlo; Basis functions; 60J22; 91G60; 33C50 (search for similar items in EconPapers)
JEL-codes: C63 G22 G32 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00780-022-00478-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:finsto:v:26:y:2022:i:3:d:10.1007_s00780-022-00478-7

Ordering information: This journal article can be ordered from
http://www.springer. ... ance/journal/780/PS2

DOI: 10.1007/s00780-022-00478-7

Access Statistics for this article

Finance and Stochastics is currently edited by M. Schweizer

More articles in Finance and Stochastics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().