Myths & Pitfalls in PIT versus TTC Credit Risk Management The impact of subtleties

Similar documents
Assessing the modelling impacts of addressing Pillar 1 Ciclycality

PRO-CYCLICALITY IMPLICATIONS OF IFRS9 AND THE RWA FRAMEWORK

Challenges For Measuring Lifetime PDs On Retail Portfolios

Alexander Marianski August IFRS 9: Probably Weighted and Biased?

IFRS 9, Stress Testing, ICAAP: a comprehensive framework for PD calculation

Analysis and Application of Credit Default Models. Masterarbeit

Choosing modelling options and transfer criteria for IFRS 9: from theory to practice

Expected Loss Models: Methodological Approach to IFRS9 Impairment & Validation Framework

A forward-looking model. for time-varying capital requirements. and the New Basel Capital Accord. Chiara Pederzoli Costanza Torricelli

Consultation on Guidelines for the Estimation of PD

Basel 2: FSA view on long-run PDs, Variable scalars & Stress testing. Dickon Brough Risk Model Review Financial Services Authority.

A response to the Prudential Regulation Authority s Consultation Paper CP29/16. Residential mortgage risk weights. October 2016

BCBS Discussion Paper: Regulatory treatment of accounting provisions

Multi-Curve Pricing of Non-Standard Tenor Vanilla Options in QuantLib. Sebastian Schlenkrich QuantLib User Meeting, Düsseldorf, December 1, 2015

Using survival models for profit and loss estimation. Dr Tony Bellotti Lecturer in Statistics Department of Mathematics Imperial College London

Effective Computation & Allocation of Enterprise Credit Capital for Large Retail and SME portfolios

Stress testing. One of the offered services

Actuaries Bringing Value to Banks by Implementing IFRS 9. International Actuarial Association Banking Working Group Webinar, 19 September 2017

Modeling Credit Correlations Using Macroeconomic Variables. Nihil Patel, Director

Deutsche Bank. IFRS 9 Transition Report

IMPROVING the CAPITAL ADEQUACY

Non-Linear Cyclical Effects in Credit Rating Migrations: A Markov Switching Continuous Time Framework

Statistics in Retail Finance. Chapter 7: Profit estimation

Global Credit Data SUMMARY TABLE OF CONTENTS ABOUT GCD CONTACT GCD. 15 November 2017

Credit Risk Modelling: A Primer. By: A V Vedpuriswar

Multi-Curve Convexity

Study on the costs and benefits of the different policy options for mortgage credit. Annex D

CECL Modeling FAQs. CECL FAQs

CREDIT LOSS ESTIMATES USED IN IFRS 9 VARY WIDELY, SAYS BENCHMARKING STUDY CREDITRISK

PRE CONFERENCE WORKSHOP 3

Credit Portfolio Simulation with MATLAB

An overview on the proposed estimation methods. Bernhard Eder / Obergurgl. Department of Banking and Finance University of Innsbruck

IFRS 9. Challenges and solutions. May 2016

IFRS 9 Readiness for Credit Unions

Bank Economic Capital An Australian Perspective. Bob Allen APRA Bank of Japan - Economic Capital Management Workshop 11 th July, 2007

Leaseurope & Eurofinas response to the EBA consultation paper on PD estimation, LGD estimation and treatment of defaulted assets

Stress Testing at the Deutsche Bundesbank

IFRS 9 Implementation Guideline. Simplified with illustrative examples

The impact of loan loss provisioning on bank capital requirements,

IRC / stressed VaR : feedback from on-site examination

Sparse Structural Approach for Rating Transitions

Department of Statistics, University of Regensburg, Germany

Implementing the Expected Credit Loss model for receivables A case study for IFRS 9

ASTIN Helsinky, June Jean-Francois Decroocq / Frédéric Planchet

Designing and Implementing a Basel II Compliant PIT-TTC Ratings Framework

Implementing IFRS 9 Impairment Key Challenges and Observable Trends in Europe

Credit Transition Model (CTM) At-A-Glance

IFRS 9 loan impairment

Economi Capital. Tiziano Bellini. Università di Bologna. November 29, 2013

Wider Fields: IFRS 9 credit impairment modelling

24 June Dear Sir/Madam

Refining the PRA s Pillar 2 capital framework

Stress Testing zwischen Granularität und Geschwindigkeit

CURRENT EXPECTED CREDIT LOSSES (CECL) BENCHMARKING SURVEY

CEBS Consultative Panel London, 18 February 2010

THE ASSET CORRELATION ANALYSIS IN THE CONTEXT OF ECONOMIC CYCLE

Asymptotic Risk Factor Model with Volatility Factors

Preprint: Will be published in Perm Winter School Financial Econometrics and Empirical Market Microstructure, Springer

Economic Adjustment of Default Probabilities

Welcome to the participants of ICAI- Dubai Chapter on IFRS 9 Presentation

TSB Banking Group plc. Significant Subsidiary Disclosures. 31 December 2015

STRESS TEST MODELLING OF PD RISK PARAMETER UNDER ADVANCED IRB

What will Basel II mean for community banks? This

Stress Testing of Credit Risk Portfolios

Graduated from Glasgow University in 2009: BSc with Honours in Mathematics and Statistics.

In this respect, we consider RTS need to be provided at least on:

EBA/CP/2018/ May Consultation Paper

EBA Report on IRB modelling practices

EBA /RTS/2018/04 16 November Final Draft Regulatory Technical Standards

Direction. On a solo basis: Abbey National plc (the "principal firm(s)") Abbey National Treasury Services plc ("ANTS")

Are SME Loans Less Risky than Regulatory Capital Requirements Suggest?

Some Options for Evaluating Significant Deterioration Under IFRS9

Regulatory Capital Pillar 3 Disclosures

Advancing Credit Risk Management through Internal Rating Systems

Credit Cycles, Dual PIT/TTC Ratings, & Stress Testing

Deutscher Industrie- und Handelskammertag

Regulatory Capital Pillar 3 Disclosures

Prudent Valuation. Dirk Scevenels Head MRMB Trading Quantitative Analytics, ING. Amsterdam - 12 November 2014

Managing Capital Adequacy and Capital Utilization

Capital Buffer under Stress Scenarios in Multi-Period Setting

Stress testing of credit portfolios in light- and heavy-tailed models

Interaction between the prudential and accounting framework - Expected losses

Regulatory treatment of accounting provisions

arxiv: v2 [q-fin.rm] 11 Mar 2012

Credit Portfolio Risk

IFRS 9 Impairment Requirements

Modeling Credit Risk of Loan Portfolios in the Presence of Autocorrelation (Part 2)

Stress Testing Credit Risk Parameters

Supervisory Statement SS11/13 Internal Ratings Based (IRB) approaches. October 2017 (Updating June 2017)

ABI response to the EBA Consultation Paper on the. Draft Guidelines on the Incremental Default and Migration Risk Charge (IRC) (CP 49)

Fundamental Review Trading Books

Collateralized banking

Guidelines. on PD estimation, LGD estimation and the treatment of defaulted exposures EBA/GL/2017/16 20/11/2017

Moody s Analytics IFRS 9 Impairment: Current State of the Market. Burcu Guner EMEA Specialist Team - Director 9 th March 2016

IFRS 9 Implementation Workshop. A Practical approach. to impairment. March 2018 ICPAK

Supervisory Views on Bank Economic Capital Systems: What are Regulators Looking For?

Abstract. Key words: Maturity adjustment, Capital Requirement, Basel II, Probability of default, PD time structure.

Global Credit Data by banks for banks

Monitoring of Credit Risk through the Cycle: Risk Indicators

Transcription:

Myths & Pitfalls in PIT versus TTC Credit Risk Management The impact of subtleties RiskMinds 2015 Philipp Gerhold Amsterdam, 10 th December 2015 d-fine All rights reserved 0

Agenda» Part A: Basic concepts of PIT and TTC» Part B: Impact on credit risk modelling d-fine All rights reserved 1

A real-life example What is ***the*** PD?» Real-life example shows default rate and estimated Probability of Default (PD) for ships.» Can this be right?» Why does the regularly recalibrated rating system not adapt to reflect the increased default rates?» What is the correct PD in 2012? PD / Default rate Default rate PD 20% 10% Time 2007 2008 2009 2010 2011 2012 * d-fine All rights reserved 2

Proper Definition of PD depends on purpose» Without a precise question, there is no right answer What is the probability that the obligor defaults within the next 12 months given the current macroeconomic environment? What is the probability that the obligor defaults within a 12 month period given an average macroeconomic environment? Point-In-Time PD (PIT-PD) Through-The-Cycle PD (TTC-PD)» The right question to ask, depends on the intended purpose Accounting (IFRS 9): Evaluation of current fair value is intended. PIT-PD Risk Management: Focus on changing (worsening) economic environment. TTC-PD (+ Asset-Correlation R²) Credit institutes need to compute both, the PIT-PD and the TTC-PD. d-fine All rights reserved 3

Common Market Practice: Often no distinction between PIT and TTC» Mathematical Background: Merton model Obligor defaults, if asset value A falls below default threshold γ A = R X + 1 R 2 ε < γ TTC-PD is directly linked to default threshold γ PD TTC = Φ(γ) PIT-PD is the conditional default probability given the economic environment X PD PIT = Φ γ R X 1 R²» Common Market Practice Rating System» Credit institute typically operates rating systems that compute one PD-value PD RATING for each obligor.» This PD-value is typically neither purely TTC nor purely PIT but a hybrid, i.e. PD RATING = κ PD PIT + 1 κ PD TTC» The degree of pitness κ is typically unknown to the credit institute. PD RATING = κ PD PIT + 1 κ PD TTC Credit institutes typically compute hybrid PD without clear distinction between PIT and TTC perspective. d-fine All rights reserved 4

In an ideal world a single rating system provides PIT- and TTC-PD» Ideal world: Introduction of dual-channel rating system Dual-Channel Rating System» Ideally, a credit institute would operate a dual-channel rating system that computes for each obligor both, the PIT-PD and the TTC-PD.» Sometimes effective approaches are implemented in practice that intend to convert PIT-PDs into TTC-PDs, and vice versa, with effective factors.» However, a genuine dual-channel rating system is more than just a rescaling approach (see below). PD PIT PD TTC» Conceptual remarks on design of dual-cannel rating systems The difference between PIT and TTC rating systems is often ascribed to different temporal extensions of the calibration period. This is only partly true. The main difference is the dependence/independence on/of the macroeconomic environment. PIT-channel Built on risk drivers that adjust quickly on economic environment (earnings, behavioral data, ). TTC-channel Built on risk drivers that are rather independent of economic environment (sector, company size, ). d-fine All rights reserved 5

PIT and TTC rating systems behave quite oppositely» PIT rating system» TTC rating system» Rating class population depends on macroeconomic environment leading to strong systematic migrations.» Rating class population is independent of economic environment leading to only idiosyncratic migrations.» On the other hand, the observed default rates per rating class are (ideally) independent of economy.» On the other hand, the observed default rates per rating class fluctuate strongly depending on economy. Default rates per rating class PIT-RC1 PIT-RC2 PIT-RC3 Master-scale PD of RC1 Master-scale PD of RC2 Master-scale PD of RC3 Default rates per rating class TTC-RC1 TTC-RC2 TTC PD of RC1 TTC PD of RC2 TTC PD of RC3 t 0 t 1 t 2 t 3 Time t TTC-RC3 Re-Rating Re-Rating Re-Rating Re-Rating t 0 t 1 t 2 t 3 Time t Ideal PIT rating systems exhibit strong systematic migrations but rather constant default rates per rating class. TTC rating systems exhibit exactly the opposite behavior. d-fine All rights reserved 6

Agenda» Part A: Basic concepts of PIT and TTC» Part B: Impact on credit risk modelling d-fine All rights reserved 7

ICAAP: PIT-PDs lead to cyclically oscillating Capital Requirements» Determination of ICAAP Credit Risk Capital Requirements» Credit institute typically operates credit portfolio model to compute ICAAP credit risk capital requirements on basis of PD, asset correlation R², LGD, EAD, and many other parameters.» PDs used in credit portfolio model are typically taken directly from rating system, i.e. hybrid PDs with certain degree of pitness κ. PD R² LGD EAD Credit Portfolio Model» As a consequence the interpretation and properties of the resulting capital requirements depend on the pitness κ of the underlying rating system. Credit risk capital requirement» Rather PIT-PDs (small κ)» Rather TTC-PDs (large κ)» Property: Resulting capital requirement depends cyclically on macroeconomic environment, i.e. more increasing capital requirements in crisis.» Interpretation: Resulting capital requirements guarantee survival of upcoming 12-month period given the current macroeconomic environment with given certainty (e.g 99.95%).» Property: Resulting capital requirement independent of macroeconomic environment, i.e. constant capital requirements also in crisis.» Interpretation: Resulting capital requirements guarantee survival (statistically) of 1999 out of the 2000 upcoming years (for 99.95% certainty level) independent of current macroeconomic environment. PIT-type rating systems induce cyclically oscillating capital requirements. TTC-PDs should better be used. d-fine All rights reserved 8

PD Term Structure modelling: Standard approach invalid for PIT-PD» Determination of PD-Term-Structure» In particular, IFRS-9 Lifetime Expected Loss requires PIT-PD-Term Structure due to LEL = Maturity t EAD t PD PIT t LGD(t)» Term Structure PD(t) caused by migration processes.» Typically insufficient data render direct determination infeasible.» Indirect approach for computing PD(t) based on exponentiation of migration matrix M typically pursued. PD t n = m 1,1 m 1,2 m 1,3 m 2,1 m 2,2 m 2,3 m 3,1 m 3,2 m 3,3 n PD(t 0 )» Prerequisite for validity is Markov property of migrations.» Idiosyncratic (TTC-) migrations are a Markov process.» Systematic (PIT-) migrations are not a Markov process.» Standard exponentiation approach conceptually invalid for desired PIT-PD-Term-Structure.» Dramatic overestimation of convergence velocity of PIT-PD. d-fine All rights reserved 9

PD Term Structure modelling: Consistent approach possible» Constructing a conceptually consistent PIT-PD Term Structure» First Step: Construct TTC-PD-Term-Structure by standard migration matrix exponentiation approach.» Rationale: TTC migration is a Markov process. PD TTC t n = M TTC n PD TTC (t 0 )» Second Step: Construct PD PIT (t) on basis of TTC curve:» For current date t 0 determine PD PIT (t 0 ) and PD TTC (t 0 ) (e.g. by means of dual-channel rating system).» With given asset-correlation R² compute current X(t 0 ).» For future time t: Mitigate economic downturn by computing future expectation of factor X(t) given current factor X(t 0 ) assuming auto-correlation α according to E X t X t 0 ~ න X t e X t 2 +2αX t X t 0 dxt» Auto-Correlation α = cor X t, X t+1 given by historic observations.» Use X(t) to translate PD TTC (t) into PD PIT (t). d-fine All rights reserved 10

EBA stress testing: Simple rescaling of PDs insufficient» Conceptual considerations» For stress testing stressed macroeconomic risk drivers X(t) typically given (e.g. GDP growth -2% for first year, -1.5% for second ).» GDP decline needs to be translated into stressed PDs.» This is typically done by regression analysis yielding factor f stress PD stress = f stress PD» Factor f stress must match pitness degree κ of rating PD.» Problems with this approach:» Method cannot provide stressed migration matrices.» Method cannot provide genuine stressed PD term structure. Conceptually consistent standard approach to stress testing not yet established. d-fine All rights reserved 11

EBA stress testing: Genuine stressed PD term structure can be constructed» Constructing genuine stressed migration matrices and PD term structure» For stress testing stressed macroeconomic risk drivers X(t) typically given (e.g. GDP growth -2% for first year, -1.5% for second ).» Given the asset correlation R 2 the macroeconomic X(t) can be used to translated PIT- and TTC-PDs.» TTC term structure obtained from PD TTC t n = M TTC n PD TTC (t 0 )» Stressed PIT-PD term structure given by PD PIT (t) resulting from given (stressed) X(t).» Corresponding stressed migration matrices obtained by clustering the resulting stressed PD PIT (t) into rating classes.» Result: The stressed PIT-PD term structure is strongly affected by assumed stress at short time scales but converges to the same equilibrium limit as in the unstressed scenario. d-fine All rights reserved 12

Conclusion A consistent credit risk management requires a clean distinction between Point-In-Time (PIT) and Through-The-Cycle (TTC) PD. In practice, these subtleties often need to be respected more carefully. Unawareness in this subject affects various fields in risk management and may lead to - pro-cyclical oscillations of ICAAP Capital Requirements. - unrealistic PD Term Structure models. - conceptually inconsistent stress testing methodologies. d-fine All rights reserved 13

Dr. Philipp Gerhold Senior Consultant Mobile: +49 (0)162 263 1361 E-Mail: philipp.gerhold@d-fine.de d-fine GmbH Frankfurt München London Wien Zürich Dr. Bernd Appasamy Partner Mobile: +49 (0) 151 148 193 06 e-mail: bernd.appasamy@d-fine.de Zentrale d-fine GmbH Opernplatz 2 D-60313 Frankfurt/Main T. +49 69-90737-0 F: +49 69-90737-200 www.d-fine.com d-fine All rights reserved 14