Risk Factor Evolution for Counterparty Credit Risk under a Hidden Markov Model

Ioannis Anagnostou and Drona Kandhai
Additional contact information
Ioannis Anagnostou: Computational Science Lab, University of Amsterdam, Science Park 904, 1098XH Amsterdam, The Netherlands
Drona Kandhai: Computational Science Lab, University of Amsterdam, Science Park 904, 1098XH Amsterdam, The Netherlands

Risks, 2019, vol. 7, issue 2, 1-22

Abstract: One of the key components of counterparty credit risk (CCR) measurement is generating scenarios for the evolution of the underlying risk factors, such as interest and exchange rates, equity and commodity prices, and credit spreads. Geometric Brownian Motion (GBM) is a widely used method for modeling the evolution of exchange rates. An important limitation of GBM is that, due to the assumption of constant drift and volatility, stylized facts of financial time-series, such as volatility clustering and heavy-tailedness in the returns distribution, cannot be captured. We propose a model where volatility and drift are able to switch between regimes; more specifically, they are governed by an unobservable Markov chain. Hence, we model exchange rates with a hidden Markov model (HMM) and generate scenarios for counterparty exposure using this approach. A numerical study is carried out and backtesting results for a number of exchange rates are presented. The impact of using a regime-switching model on counterparty exposure is found to be profound for derivatives with non-linear payoffs.

Keywords: Counterparty Credit Risk; Hidden Markov Model; Risk Factor Evolution; Backtesting; FX rate; Geometric Brownian Motion (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-9091/7/2/66/pdf (application/pdf)
https://www.mdpi.com/2227-9091/7/2/66/ (text/html)

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:gam:jrisks:v:7:y:2019:i:2:p:66-:d:239237

Access Statistics for this article

Risks is currently edited by Mr. Claude Zhang

More articles in Risks from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().