The impact of pre-selected variance inflation factor thresholds on the stability and predictive power of logistic regression models in credit scoring

The role of operational risk in the 2007/2008 financial crisis is explored. The factors that gave rise to the crisis are examined and it is found that although the event is largely regarded as a credit crisis, operational risk factors played a significant role in fuelling its duration and severity. It is concluded that, from an operational risk perspective, 2008 was the worst on record. Considering the extensive role of operational risk in global financial calamities, suggestions are made to improve the management of this risk type.

Background: With the increasing use of complex quantitative models in applications throughout the financial world, model risk has become a major concern. The credit crisis of 2008–2009 provoked added concern about the use of models in finance. Measuring and managing model risk has subsequently come under scrutiny from regulators, supervisors, banks and other financial institutions. Regulatory guidance indicates that meticulous monitoring of all phases of model development and implementation is required to mitigate this risk. Considerable resources must be mobilised for this purpose. The exercise must embrace model development, assembly, implementation, validation and effective governance.Setting: Model validation practices are generally patchy, disparate and sometimes contradictory, and although the Basel Accord and some regulatory authorities have attempted to establish guiding principles, no definite set of global standards exists.Aim: Assessing the available literature for the best validation practices.Methods: This comprehensive literature study provided a background to the complexities of effective model management and focussed on model validation as a component of model risk management.Results: We propose a coherent ‘best practice’ framework for model validation. Scorecard tools are also presented to evaluate if the proposed best practice model validation framework has been adequately assembled and implemented.Conclusion: The proposed best practice model validation framework is designed to assist firms in the construction of an effective, robust and fully compliant model validation programme and comprises three principal elements: model validation governance, policy and process.

Many banks use the loss distribution approach in their advanced measurement models to estimate regulatory or economic capital...., Risk Management, Original Research, Loss distribution approaches (LDAs), severity distribution, Scenario analysis

The Basel II accord (2006) includes guidelines to financial institutions for the estimation of regulatory capital (RC) for retail credit risk. Under the advanced Internal Ratings Based (IRB) approach, the formula suggested for calculating RC is based on the Asymptotic Risk Factor (ASRF) model, which assumes that a borrower will default if the value of its assets were to fall below the value of its debts. The primary inputs needed in this formula are estimates of probability of default (PD), loss given default (LGD) and exposure at default (EAD). Banks for whom usage of the advanced IRB approach have been approved usually obtain these estimates from complex models developed in-house. Basel II recognises that estimates of PDs, LGDs, and EADs are likely to involve unpredictable errors, and then states that, in order to avoid over-optimism, a bank must add to its estimates a margin of conservatism (MoC) that is related to the likely range of errors. Basel II also requires several other measures of conservatism that have to be incorporated. These conservatism requirements lead to confusion among banks and regulators as to what exactly is required as far as a margin of conservatism is concerned. In this paper, we discuss the ASRF model and its shortcomings, as well as Basel II conservatism requirements. We study the MoC concept and review possible approaches for its implementation. Our overall objective is to highlight certain issues regarding shortcomings inherent to a pervasively used model to bank practitioners and regulators and to potentially offer a less confusing interpretation of the MoC concept.