Going downturn
There is much debate regarding the definition of 'downturn' loss given default (LGD). In this article, Michael Barco offers an analytic approach for calculating downturn LGD so that credit risk capital is not underestimated or overestimated
In a background note by the Basel Committee on Banking Supervision (2004) on loss given default (LGD), the Committee seeks input from the financial industry on defining and quantifying 'downturn' LGD. The main reason for this requirement is that the Vasicek model (Vasicek (2002)) used in the Basel Accord does not have systematic correlation between probability of default (PD) and LGD and, to compensate for this deficiency, downturn LGD estimates are required to be used as an input to the model. The idea here is that a credit risk model with systematic correlation between PD and LGD using long-run LGD inputs should give comparable capital to a credit risk model without correlated PD and LGD using downturn LGD inputs. One suggestion by the Basel Committee to help quantify downturn LGD is to establish a functional relationship between long-run and downturn LGD. Recently, Miu & Ozdemir (2006) used Monte Carlo to specifically tabulate such a relationship in terms of the 'LGD mark-up' required to achieve downturn from long-run LGD.
In this article, we extend the work by Miu & Ozdemir to develop an analytical relationship between long-run and downturn LGD so that credit risk is not overestimated or underestimated in the Vasicek model. We do this by introducing another fully granular credit risk model that contains systematic dependence between PD and LGD. This model is calibrated to historical default and recovery rate data using the Merton model for firm asset return, and recoveries are modelled with a three-parameter lognormal distribution for the value of the assets of the creditor, which may include secured or unsecured assets of any priority or seniority. The choice of a lognormal distribution is first introduced in Pykhtin (2003), and is a more natural choice for quantities that remain positive compared with the Gaussian distribution used by Frye (2000). To determine downturn LGD, we then solve for the downturn LGD input in the Vasicek model so that it gives identical credit risk capital to the model with systematically correlated PD and LGD. We also show that, to correctly compensate for the lack of systematic correlation between PD and LGD, the Vasicek model requires two.
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact info@risk.net or view our subscription options here: http://subscriptions.risk.net/subscribe
You are currently unable to print this content. Please contact info@risk.net to find out more.
You are currently unable to copy this content. Please contact info@risk.net to find out more.
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@risk.net
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@risk.net
More on Credit risk
Credit risk management solutions 2024: market update and vendor landscape
A Chartis report outlining the view of the market and vendor landscape for credit risk management solutions in the trading and banking books
Finding the investment management ‘one analytics view’
This paper outlines the benefits accruing to buy-side practitioners on the back of generating a single analytics view of their risk and performance metrics across funds, regions and asset classes
Revolutionising liquidity management: harnessing operational intelligence for real‑time insights and risk mitigation
Pierre Gaudin, head of business development at ActiveViam, explains the importance of fast, in-memory data analysis functions in allowing firms to consistently provide senior decision-makers with actionable insights
Sec-lending haircuts and indemnification pricing
A pricing method for borrowed securities that includes haircut and indemnification is introduced
XVAs and counterparty credit risk for energy markets: addressing the challenges and unravelling complexity
In this webinar, a panel of quantitative researchers and risk practitioners from banks, energy firms and a software vendor discuss practical challenges in the modelling and risk management of XVAs and CCR in the energy markets, and how to overcome them.
Credit risk & modelling – Special report 2021
This Risk special report provides an insight on the challenges facing banks in measuring and mitigating credit risk in the current environment, and the strategies they are deploying to adapt to a more stringent regulatory approach.
The wild world of credit models
The Covid-19 pandemic has induced a kind of schizophrenia in loan-loss models. When the pandemic hit, banks overprovisioned for credit losses on the assumption that the economy would head south. But when government stimulus packages put wads of cash in…
Driving greater value in credit risk and modelling
A forum of industry leaders discusses the challenges facing banks in measuring and mitigating credit risk in the current environment, and strategies to adapt to a more stringent regulatory framework in the future