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Preparing for Modeling Requirements in Basel II: Model Development
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The Basel II Capital Accord, ready for implementation around 2007,
sets out detailed analytic requirements for risk assessment that will be
based on data collected by banks throughout the life cycle of the loan.
The purpose of Basel II is to introduce a better risk-sensitive capital
framework with incentives for good risk management practices. This article,
the first in a four part series, will give a managerial overview of
statistical modeling - an area of analytics that is paramount to the
Basel framework.
Portuguese Version (PART 1) - Credit Technologies
This series was also picked up by an international
journal in Brazil called CREDIT TECHNOLOGIES - a publication
translating the article in Portuguese side by side with the English
version.
An Introduction to Survival Analysis in Business
As the field of credit scoring is focused on predicting 'if' an account will become delinquent over a certain span of time, Survival Analysis can tell us 'when.' Survival Analysis is called different things in different industries: event history analysis, reliability analysis, time to failure, and even duration analysis. The purpose of this article is to give an introduction to the subject with an emphasis on how it can be used in banking and finance.
Preparing for Modeling Requirements in Basel II: Stress Testing
The idea of stress testing should be of interest to banks not only because of Basel, but because developing such approaches can provide the institution with the tools necessary to better manage their capital. This article will introduce a modeling approach called 'Pooled Cross Section Time Series Analysis' that uses aggregated data to predict the impact of economic and portfolio changes on bank default losses.
Forecasting New Product Acceptance
This paper summarizes a step by step procedure for forecasting new product sales from start to finish. Topics include preliminary data analysis, data preparation, basic statistics, hypothesis testing, correlations, regression, dummy variables, transformations, interaction terms, model validation, and forecast simulation.
Modeling Complex Data with Neural Networks
This paper discusses Neural Networks as a prediction tool for probability of default (PD) and
recovery (LGD) models. Points of interests include terminology, strengths, weaknesses, and what to look for in a good NN package.
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