Modeling Ultimate Loss Given Default on Corporate Debt

Jacobs, Michael; Karagozoglu, Ahmet K.

doi:10.3905/jfi.2011.21.1.006

Cited by 16 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For a Bayesian stochastic model for PD in the Basel Asymptotic Single Risk Factor ("ASRF") class of models, underlying the Basel II Advanced IRB model for credit loss, seeJacobs & Kiefer (2010).2 For applications of the CEV model in finance seeChan et al (1992) andJacobs (2001), in the context of term structure and interest rate derivatives. 3 See Jacobs (2011, 2012) for a 2-factor structural credit model with stochastic LGD;Araten & Jacobs (2004) orJacobs & Karagozoglu (2011) for empirical models of LGD; and Frye & Jacobs (2012) for a structural credit risk model in ASRF class of models, featuring co-monotonic LGD and PD that provides a parsimonious function for downturn LGD.…”

mentioning

confidence: 99%

The Impact of Asset Price Bubbles on Credit Risk Measures

Jacobs

2015

JFRM

View full text Add to dashboard Cite

This study presents an analysis of the impact of asset price bubbles on standard credit risk measures, including Expected Loss ("EL") and Credit Value-at-Risk ("CVaR"). We present a styled model of asset price bubbles in continuous time, and perform a simulation experiment of a 2 dimensional Stochastic Differential Equation ("SDE") system for asset value determining Probability of Default ("PD") through a Constant Elasticity of Variance ("CEV") process, as well as a correlated a Loss-Given-Default ("LGD") through a mean reverting Cox-Ingersoll-Ross ("CIR") process having a longrun mean dependent upon the asset value. Comparing bubble to non-bubble economies, it is shown that asset price bubbles may cause an obligor's traditional credit risk measures, such as EL and CVaR to decline, due to a reduction in both the standard deviation and right skewness of the credit loss distribution. We propose a new risk measure in the credit risk literature to account for losses associated with a bubble bursting, the Expected Holding Period Credit Loss ("EHPCL"), a phenomenon that must be taken into consideration for the proper determination of economic capital for both credit risk management and measurement purposes.

show abstract

mentioning

confidence: 99%

The Impact of Asset Price Bubbles on Credit Risk Measures

Jacobs

2015

JFRM

View full text Add to dashboard Cite

show abstract

Supervisory Requirements and Expectations for Portfolio‐Level Counterparty Credit Risk Measurement and Management

Jacobs

2015

Quantitative Financial Risk Management

View full text Add to dashboard Cite

Purpose -This study aims to survey supervisory requirements and expectations for counterparty credit risk (CCR). Design/methodology/approach -In this paper, a survey of CCR including the following elements has been performed. First, various concepts in CCR measurement and management, including prevalent practices, definitions and conceptual issues have been introduced. Then, various supervisory requirements and expectations with respect to CCR have been summarized. This study has multiple areas of relevance and may be extended in various ways. Risk managers, traders and regulators may find this to be a valuable reference. Directions for future research could include empirical analysis, development of a theoretical framework and a comparative analysis of systems for analyzing and regulating CCR. Findings -Some of the thoughts regarding the concept of risk will be considered and surveyed, and then how these apply to CCR will be considered. A classical dichotomy exists in the literature, the earliest exposition upon which is credited to Knight (1921), who defines uncertainty is when it is not possible to measure a probability distribution or it is unknown. This is contrasted with the situation where either the probability distribution is known, or knowable through repeated experimentation. Arguably, in economic and finance (and more broadly in the social or natural as opposed to the physical or mathematical sciences), the former is a more realistic scenario that is being contending with (e.g. a fair vs loaded die, or die with unknown number of sides.) The authors are forced to rely upon empirical data to estimate loss distributions, but this is complicated because of changing economic conditions, which invalidate forecasts that our econometric models generate. Originality/value -This is one of few studies of the CCR regulations that is so far-reaching.

show abstract

References

2012

Financial Risk Management

View full text Add to dashboard Cite

Modeling Ultimate Loss Given Default on Corporate Debt

Cited by 16 publications

References 27 publications

The Impact of Asset Price Bubbles on Credit Risk Measures

The Impact of Asset Price Bubbles on Credit Risk Measures

Supervisory Requirements and Expectations for Portfolio‐Level Counterparty Credit Risk Measurement and Management

References

Contact Info

Product

Resources

About