IBM Algorithmics Introduction to Portfolio Credit Risk EngineBusiness Management
DescriptionWhat we offer
You gain hands-on experience with the portfolio credit risk engine, the Algorithmics component that calculates portfolio credit risk and bottom-up measures of integrated market and credit risks.
ObjectivesWhat you learn
- Articulate the key data elements required to calculate portfolio credit risk and which Algorithmics' components can provide these inputs
- Define each of the various measures available in PCRE
- Discuss the principles behind the PCRE models
- Generate a typical/sample report based on statistical measures
- List the types of scenario analysis supported within PCRE
- Generate a typical/sample report for scenario analysis
- Launch PCRE Setup Manager and Results Viewer
- Initiate the PCRE controller and workers in a multiprocessor environment
TopicsThe best for you
DAY 1: PORTFOLIO CREDIT RISK ENGINE BASICS
The Portfolio Credit Risk Engine within Algo One
In this section we discuss the fundamental model upon which the engine is based and the location of the engine (PCRE) within the Mark-to-Future framework.
Inputs and Data
The inputs and data required to drive the portfolio credit risk model are varied. They are also dependent on the sophistication of the model to be adopted. Accordingly, we begin by examining the basic inputs, and address possible additional inputs and data second. Typical input categories include counterparty/obligor/name, exposure, credit quality, recovery rates, historical series and aggregation keys.
Hands-on Experience: Setup
This section familiarizes participants with the Setup Manager tool within PCRE. The objectives revolve around locating data and making associations within the data set.
Outputs and Measures
The contrast between the different classes of measures - absolute, additive, marginal, incremental, cumulative - and the details of the more complex calculations are the primary focus. Interpretation and application of the measures to business purposes is also discussed.
DAY 2: STRESS TESTING AND INTEGRATED RISK MEASUREMENT
Hands-on Experience: Results
This section familiarizes participants with the Results Viewer and Report Definitions Editor tools within PCRE. The objectives revolve around running the engine and effectively viewing results. A demonstration of the ARA reporting infrastructure will be provided upon prior request.
A look at the math behind - and hands-on usage of - an analytic approximation to PCR measures.
An interactive demonstration of the various methods of scenario analysis available within PCRE is followed by a short hands-on case study.
Integrated Market and Credit Risk
Exposure modelling is a key feature of portfolio credit risk measurement within Algo One. We explore the generation of exposures within MtF for use in PCRE calculations of integrated market and credit risks.
PrerequisitesWhat should you know
You should have:
- Knowledge of basic credit risk (e.g. definition of rating, PD and LGD) is essential.
- Some portfolio credit risk knowledge would be an asset.
- IBM Algorithmics Foundations of RiskWatch
- IBM Algorithmics Introduction to Scenario Engine
- IBM Algorithmics Exposure Modeling in RiskWatch
- IBM Algorithmics Exposure Modeling in Risk & Financial Engineering Workbench
AudienceWho should attend
The advanced course is aimed at quantitative analysts or capital managers with a credit risk focus; however, the significant hands-on emphasis may also make it of interest to non-quantitative business analysts.