Backtesting counterparty risk: how good is your model?

Ruíz, Ignacio Montero

doi:10.21314/jcr.2014.173

Cited by 10 publications

(7 citation statements)

References 1 publication

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“…The research presented in this paper can be extended in a number of ways, such as considering the evolution of risk factors other than exchange rates. Another topic worthy of investigation is the enhancement of the backtesting framework presented by Ruiz (2014), by considering statistical tests similar to the ones presented by Berkowitz (2001) and Amisano and Giacomini (2007). Finally, an interesting research direction is the development of an agent-based simulation model with heterogeneous modeling approaches, with regards to the RFE models.…”

Section: Discussionmentioning

confidence: 99%

“…In this sub-section, we give a brief overview of a framework for the backtesting of RFE models. For a more detailed description, the reader is referred to Ruiz (2014). Backtesting is the process of comparing the distributions given by the RFE models with the realized history of the corresponding risk factors.…”

Section: Backtestingmentioning

confidence: 99%

“…In our numerical experiments, we use GBM and HMM to generate scenarios for the Euro against two major and two emerging currencies. We perform a thorough backtesting exercise, based on the framework proposed by Ruiz (2014), and find similar performances for GBM and a two-state HMM. Finally, we use the generated scenarios to calculate credit exposure for foreign exhange (FX) options, and find significant differences between the two models, which are even more pronounced for deep out-of-the-money instruments.…”

Section: Introductionmentioning

confidence: 96%

“…In an attempt to reduce this ambiguity, Anfuso et al (2014) introduced a complete of 22 framework for counterparty credit risk (CCR) model backtesting which is compliant with Basel III and the new Capital Requirements Directives (CRD IV). A detailed backtesting framework for CCR models was also introduced by Ruiz (2014), who expanded the corresponding framework for Value-at-Risk (VaR) models by the Basel Committee (Basel Committee on Banking Supervision 1996). The most ubiquitous model for the evolution of exchange rates is Geometric Brownian Motion (GBM).…”

Section: Introductionmentioning

confidence: 99%

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

Anagnostou

Kandhai

2019

Risks

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Backtestingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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

Anagnostou

Kandhai

2019

Risks

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“…From backtesting, one may find a so-called PFE-limit implied volatility of a model, by which, combined with a given budget, a bank's risk preference can be computed. Ruiz (2012) called the model that describes the evolution of the underlying market factors the risk factor evolution (RFE) model, on which the backtesting is done periodically. The related probability measure is called the RFE measure.…”

Section: Calibration and Backtestingmentioning

confidence: 99%

Efficient computation of exposure profiles on real-world and risk-neutral scenarios for Bermudan swaptions

Karlsson¹,

Feng

Jain

et al. 2016

JCF

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This paper presents a computationally efficient technique for the computation of exposure distributions at any future time under the risk-neutral and some observed real-world probability measures; these are needed for the computation of credit valuation adjustment (CVA) and potential future exposure (PFE). In particular, we present a valuation framework for Bermudan swaptions. The essential idea is to approximate the required value function via a set of risk-neutral scenarios and use this approximated value function on the set of observed real-world scenarios. This technique significantly improves the computational efficiency by avoiding nested Monte Carlo simulation and using only basic methods such as regression. We demonstrate the benefits

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