the usage of credit register data for credit risk

[email protected] www.oenb.at [email protected] www.oenb.at

The Usage of Credit Register Data for Credit Risk Modelling – Applications and Experiences of OeNB Gerhard Winkler Copenhagen, October 24th, 2019 Supervisory Statistics, Models and Credit Quality Assessment Division www.oenb.at


• Statistics at OeNB

• A Short History of our Credit Register

• Usage of Credit Register Data for Credit Risk Modelling

• Case 1: A Model Framework for Benchmarking and Validation

• Case 2: A Structural Credit Risk Model for Supervisory and Financial Stability Analysis

• Case 3: The CoCAS Model Framework and its Utilization in our Inhouse Credit Assessment System (ICAS)

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Agenda

[email protected] www.oenb.at [email protected] www.oenb.at 3

Statistics at OeNB


SIDAT „One-Stop-Shop“

Financial Corporations

Reporting Agents

Non-financial Corporations

Public Authorities

Non-profit Organizations

Stat. Analysis (micro- /macro- prudential)

Monetary preparation and implementations

SAFIM

SAMBA

Dir.

Products

Statistics Department

Others/ Private

Households

Analysis

Enrichment Methodology

Processing Validation

Methodology

Organisation of Statistics Department at OeNB

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Domains of Statistics at OeNB

Supervisory statistics

Monetary and financial statistics (including MR statistics)

External and other related statistics

Financial accounts, government finance statistics

Companies register and companies balance sheet data

Credit register and AnaCredit

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• Credit assessment of Austrian non-financial corporates for monetary policy purposes

• Joint operation of CoCAS together with Bundesbank for other NCBs

• Chairmanship of EG ICAS (sub-group of RMC)

• Consolidated Banking Data (CBD): assets & liabilities, P&L, and regulatory equity of Austrian Banks

• BIS-Statistics: geographical and currency composition of Austrian banks' assets & liabilities on solo and consolidated level

• AnaCredit (and GKE): granular credit and credit risk data on instrument level for Austrian banks and financial institutions

Inhouse Credit Assessment

Statistical Analysis • Production and dissemination of (standard) reports and

statistical analyses for all relevant customers with a focus on supporting supervision

Methodology • International Working Groups for Supervisory Statistics, Central Balance Sheet Data Offices and AnaCredit (including chairmanship)

International Secondary Statistics

Model development

• Calibration and validation of statistical risk models: for assessment of coporates‘ creditworthiness within CoCAS/ICAS and for assessment of banks within supervision (ABBA)

Core Competences of Supervisory Statistics, Models and Credit Quality Assessment Division (SAMBA)

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A Short History of our Credit Register


Development of OeNB masterscale

Implementation of rating database for the purpose of CCR-reporting

1st major CCR-amendment “Basic” CCR-Data - new types of instruments - information on selected parameters of credit risk (e.g., rating)

1995 - 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Implementation of new OeNB counterparty reference database

2nd major CCR-amendment ( Basel II) - new on- and off-balance sheet positions (incl. derivatives) - collateral report (Basel II/Internal risk management) - exposure value (EV) - risk weighted assets (RWA) - expected loss (EL) - probability of default (PD)

Entry into force of the Memorandum of Understanding (MoU) on the exchange of information among national central credit registers (only some attributes, no information on risk)

3rd major CCR-amendment ( lessons learned from the financial crisis) - CLN, CDS - CCR securitization report

4th major CCR-amendment to align CCR with Capital Requirements Regulation (CRR)

“Fundamental” CCR-Data on Volumes - quarterly (before 1998) - monthly (since 1998)

AnaCredit Regulation adopted

- Decision to collect AnaCredit and CCR data in an integrated reporting framework

Go-live of AnaCredit and granular credit data collection („GKE“) in September

The Austrian CCR: Historical Overview


Loan by Loan

Natural persons

AC threshold: ≥ 25,000 and < 350,000 CCR threshold: ≥ 350.000

Loans and credit lines

CCR (Borrower by Borrower) Aggregated exposures

(by borrower)

Aggregated exposures (by borrower)

Legal entities

Loan by Loan ? AnaCredit (Loan by Loan)

Securities, off-balance items and derivatives

?

Consistent concepts & uniform granularity for all instruments and types of borrowers!

Initi

al s

ituat

ion

Har

mon

ised

sol

utio

n

Integrated reporting (Loan by Loan)

Integrated reporting (Instrument by Instrument) Instrument by Instrument

Loan by Loan

Loan by Loan

ISIN by ISIN Securities, off-balance items and derivatives

Aggregated exposures (by borrower)

Aggregated exposures (by borrower) CCR

(Borrower by Borrower)

Loans and credit lines

AnaCredit and the Austrian CCR: Scope and Content


• Integrated reporting of AnaCredit and CCR is more granular than derived AnaCredit statistics − Filter relevant to types of instruments and debtors (natural persons) − Filter national attributes integrated in the granular credit data reporting − Compilation for “ECB view“

• AnaCredit transmission

− Transmission of AnaCredit by the deadlines set in Article 13 AC-R

− Managing different versions and previous AnaCredit transmissions

− Receiving international data

• Ensuring data quality − Historical CCR data available − AnaCredit data quality checks have to

be performed together with additional national checks − ECB’s data quality requests are drilled-down to original primary reports of banks

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AnaCredit

CCR

BSI

Credit Cube

Granular data Compilation

ISIN Cube Stage 1

Inte

rface

s op

erat

iona

l sys

tem

s

Selection, Aggregation

“BasicCube”

Integrated Reporting and Production of Derived Statistics


Usage of Credit Register Data for Credit Risk Modelling


• The Austrian CCR provides granular rating information in form of

• CRR-PDs from IRB and! partly from STA raters, and

• ordinal rating classes

in a multi-rater panel.

Using these information

• a quantitative mapping (via joint

obligors)

• a Best-PD-Estimate for each

rater-obligor relationship

can be derived.

Usage of Credit Register Data for Credit Risk Modelling (1/2)


• The rater-obligor-specific Best-PD-Estimates forms the

basis for analyzing

• rating heterogeneity

• across obligors,

• across raters,

• across time points,

• specific portfolios

• bank,

• sector,

• … .

Usage of Credit Register Data for Credit Risk Modelling (2/2)

Rat

ing

Sco

re

PD

Obligor


A Model Framework for PD Benchmarking and Validation


• Using ordinal rating information mapped on a common master scale

Measuring similarity/dissimilarity, i.e. proximity of ratings from

different sources.

• We propose 3 measures:

• for association τx

• for aggreement Cohen’s κ

• Rating (class) bias θ

Use-Case 1: A Model Framework for Benchmarking and Validation (1/3)

Bivariate comparison of bank 27 to all other banks:


• Using continuous rating information, i.e. transformed PDs

We propose a stochastic model for the

rating error and the underlying rating score distribution.

• Goal:

• estimation of the distribution of the

noise term (rating bias and deviation) and

• distribution of the latent true score/PD (consensus score/PD).



E.g. Consensus score of a company over time

E.g. Rating bias of different banks across different industries

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• The Austrian Banking Business Analysis (ABBA) framework is a quantitative model environment maintained to support the supervision of the high number of legally independent banks in Austria

• High risk banks are identified on a quarterly basis, which are then subject to a more detailed qualitative analysis by the supervisors

• An internal model containing the quantitative variables that have the highest explanatory power produces scores based on the latest available reporting data for the following different risk categories:

o Capital adequacy

o Business model risk and profitability

o Market risk

o Credit risk

o Operational risk

o Liquidity and funding risk

o Interest rate risk in the banking book

o Total score

• Model calibration is not based on defaults (alone), but on (broader) problem criteria

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Use-Case 2: A Structural Credit Risk Model for Supervisory and Financial Stability Analysis (1/3)


• We employ a structural credit risk model for the category credit risk

• Method: • Credit risk + • The algorithm is extended in order

to be able to consider correlations as they appear in the Hidden Gamma and in the Compound Gamma model • Data:

• CCR • Exposure (at default) and Best-PD-Estimates per rater-obligor relationship

• Unconsolidated Credit Risk Report • Aggregated Exposure (at default) and vol. weighted PD per size bucket (below the CRR

thresholds)


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ABBA component Ratios Source

Structural credit risk model Credit VaR Central Credit Register, Credit Risk Report

Structural credit risk model Expected Shortfall Central Credit Register, Credit Risk Report

Structural credit risk model Expected Loss Central Credit Register, Credit Risk Report

ABBA Score – Credit risk module 95% Credit VaR to loss bearing capacity Structural Credit Risk Model, COREP, Income Statement

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The CoCAS Model Framework and its Utilization in our Inhouse Credit Assessment System (ICAS)


• In-house Credit Assessment Systems (ICAS) are employed by NCBs to assess whether credit claims fulfill the eligibility standards for credit quality and may hence be mobilized as collateral for Eurosystem Monetary Policy Operations

• OeNB‘s ICAS utilizes CoCAS to assess the creditworthiness of non-financial corporates

• CoCAS is the Common Credit Assessment System jointly developed and operated by Deutsche Bundesbank and Oesterreichische Nationalbank and used also by other NCBs.

• The CoCAS credit assessment process consists of a two-stage procedure:

• Stage 1: A statistical model delivers a “base-rating” representing a “rating proposal”.

• Stage 2: An analyst makes the final decision on the creditworthiness of an entity and thus produces the final rating.

Use-Case 3: The CoCAS-Model within ICAS (1/4)



CoCAS-Three-Step-Model Architecture: • Step I: Consensus Model

• Dataset comprises a number of ratings (IRB, ECAI) for each financial statement.

• For each financial statement, aggregate the different ratings into one single consensus rating

• consensus rating is not simply a third-party rating or a weighted average thereof, but an unbiased and efficient estimate for the true latent rating of the firm.

• Consensus ratings are estimated based on a mixed effects model that takes into account rater-specific errors

• Step II: Regression Model • Regression analysis is used to explain the consensus rating by means of a set of financial ratios

• Allows prediction of consensus ratings based on financial statements data

• Step III: PD-level adjustment (optional) using Default Data • Validate the results of step II on the basis of realized default data

• Compare average predicted consensus ratings with observed default rates. If the two differ significantly from each other apply a shift to the predicted consenus ratings



CoCAS Consensus Model

• Addresses the drawbacks of existing approaches that are based exclusively on default information

• Uses both default information and information on non-defaulters (rating data)

• Rests on scientifically founded methods which have been proprietary developed at the OeNB jointly with WU Wien

• Employs a mixed effects model to estimate the rating errors of individual raters

• Aggregates rating information from IRBs and ECAIs on non-defaulters (true latent rating)

• The rating output of CoCAS is a one-year, point-in-time, issuer-specific probability of default, calibrated employing the Basel-II-IRB (CRR)-default definition



Calibration Quality and Discriminatory Power of current CoCAS model

• Min-p adjusted p-values: 1,0000/1,0000/1,0000/0,9282 (CQS1&2, CQS3, CQS4, CQS5)

• Hosmer Lemeshow Test: p-value: 0.3341

• Sterne Test: p-value: 0.3114

• Goodness of Fit of Regression Model: Adj. R²: 0.7110

• ROC: 0.9165

Danke für Ihre Aufmerksamkeit Thank you for your attention www.oenb.at [email protected] @oenb @nationalbank_oesterreich OeNB

the usage of credit register data for credit risk

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