May 2021
Armen Mirzoyan, Associate Director, MA Predictive Analytics Unit
Gega Todua, Assistant Director, MA Predictive Analytics Unit
Forward-looking Risk MeasurementA Moody’s Analytics Presentation
Forward-Looking Risk 3
IFRS 9 Requirements
(5.5.17)
An entity shall measure ECL of a financial instrument in a way that reflects an unbiased and probability-weighted amount that is determined by evaluating a range of possible outcomes.
“ ”
(5.5.18)
When measuring ECL, an entity need not necessarily identify every possible scenario. However, it shall consider the risk of probability that a credit loss occurs by reflecting the possibility that a credit loss occurs and the possibility that no credit loss occurs, even if the possibility of a credit loss occurring is very low.
“ ”This may not need to be complex analysis. In some cases, relatively simple modelling may be sufficient, without the need for a large number of detailed simulations of scenarios.“ ”(B5.5.42)
…an entity need not necessarily identify every possible scenario.“ ” (5.5.18)
Regulatory Origins of Forward-Looking Risk
Forward-Looking Risk 4
Forward Looking & Probability-Weighted Outcomes
» Requires expected credit losses (ECL) to account for forward-looking information
» Requires probability-weighted outcomes when measuring expected credit losses – Estimates should reflect the possibility that a credit loss occurs and the possibility that no credit loss occurs
Macroeconomic modelling satisfies both requirements above
Key Take-Aways
Forward-Looking Risk 7
Moody’s Analytics Global Forecast CoverageApril 2021
Sources: Moody’s Analytics
Included in the Global Model (73 countries)Additional forecasts (IFRS9) (28 countries)
Forward-Looking Risk 8
Key Features
Collaborative Access and Integration Develop scenarios individually or collaboratively in a real-time, multi-user environment.
Integrate forecasts into your workflow seamlessly through our API and Excel Add-In.
Comprehensive CoverageCreate scenarios for 101 countries and 10 regional aggregates, out to 30-years.
Evaluate monthly updated forecasts for 10,000+ economic and financial time series.
Robust Editing & Visualization ToolsAdjust detailed variables to simulate shocks or more discrete factors.
Visualize your changes through interactive dashboards, charting and data tables.
Forward-Looking Risk 10
Linkages in the model allow for global shock propagation and contagion effects, and help ensure scenario consistency
» Trade flows (exports reflect partner imports)
» Financial markets (stock prices and bond yields)
» Prices (exchange rates, terms of trade and global commodity prices)
» Investment (foreign direct investment and capital flows)
Diagnostic processes ensure that our forecasts are stable from month to month and consistent withthe business cycle outlook of each nation.
Provides Globally Linked ForecastsGlobal Macroeconomic Model
Forward-Looking Risk 11
Each Country-model is a Mix of Theory and Data
Modelling Approach
Our ModelsIntersection of purely data- and
purely theory-based models
Theory» Quality of forecast and scenarios » Complex» Limited quantity of forecasts
Data» Quality and quantity of forecasts» Easy to produce» Not ideal for scenario analysis
Forward-Looking Risk 12
Detailed Quantitative & Qualitative TestingCountry-model Methodology Overview
Specification choice» Theoretical reasoning versus statistical propertiesIn-sample equation fit» R-squared, RMSE, information criteria» Fitted values and residualsForecasting performance» Back-testing: conditional and unconditional evaluation» Benchmarking during important past episodesSensitivity to shocks» Forecasts across scenarios» Response to individual shocks
13
Identify Equation for Development•Revised indicator•New indicator•Indicator with poor performance
•Model owners prioritize based on need, use, and performance
Equation Estimation•Specification•Variable selection•In-sample fit•Out-of-time fit•Theoretical consistency
Model Integration Testing•Test inclusion of proposed equation in model system
•Examine impact on other core indicators
•Examine shock properties
Equation Approval•Model owner examines equation development results and impact analysis
•Model validator examines test results
•Joint approval required to advance proposed equation
Equation Implementation•Production team implements equation specs
•Runs battery of stability tests. •Generates baseline forecast output
•Model owner confirms forecast output as intended
Documentation•Equation estimation codes, results, summary findings archived
•Production equations published to user interfaces
•Model system documentation refreshed annually
Validation•Independent validation team
•Reviews key equations•Reviews overall model system performance
•Performs historical backtesting
•Identifies issues•Recommends
Production Release•Equation integrated into monthly forecasting process
•Forecasts reviewed and adjusted per forecast governance procedures
Performance Tracking•Monthly performance tracking report
•Compares forecasted versus actual performance
•Considers several model vintages
•Published to users
Performance Review•Analyst examines performance monthly
•Flags indicators for watchlist
•Escalates indicators with poor performance to model owners
Rigorous Equation Development Process
Forward-Looking Risk 14
Equations Designed to Balance Theory & EmpiricsVariable Specification suggested by economic theory draws on…Unemployment rate Okun's LawLabor Force Participation rate & demographicsPrivate consumption expenditure Keynesian consumption function / Euler equationPublic consumption expenditure Baumol's disease w/ endogenous responses to fiscal spaceFixed investment Accelerator model / Tobin's QInventory investment Adjustment process in deviations of final spending to firm outputExports Trading partner import demand and real effective exchange rateImports Imports reflect domestic demand + re-exporting demandLabor income (wages & salaries) Wage bargaining over revenue product of laborCentral bank target rate Policy assumption, based on an augmented Taylor Rule10yr Gov bond yield Fisher Rule w/ sovereign risk premium, global interest rate parityYield curve & market lending rates Term-structure of interest ratesExchange rate (floating) Interest rate parity (short-run) & purchasing power parity (long-run)Import price deflator Exchange rate pass-through of foreign prices, global commodity pricesConsumer price index Expectations augmented Phillip's curve based on firm price setting functionHouse prices, stock prices Asset pricing theoryGovernment total expenditure Sum of government consumption + debt service + net transfersGovernment total revenues Revenues equal the effective tax rate multiplied by incomeIndustrial production IP tracks the aggregate value added of goods-producing industriesDomestic credit (money supply) Liquidity demand depends on transactions value (GDP) and interest ratesCA balance (Identity) CA = net exports + net income + net transfers
Forward-Looking Risk 15
Kenya GDP Components – Model Drivers
Output Gap
Growth Expectations
Real Demand
GDP Identity
(Lending Rate -Monetary Policy rate)
Real Net Exports
Output Gap Real Potential GDP
Aggregate Demand
External Shock (e.g., U.S. GDP)
Forward-Looking Risk 16
Baseline Forecasting
Sources: ECB, Moody’s Analytics
-10.0
-5.0
0.0
5.0
10.0
15.0
2007Q1 2012Q1 2017Q1 2022Q1 2028Q2 2033Q2
US
-9.0
-4.0
1.0
6.0
11.0
16.0
21.0
2007Q1 2012Q1 2017Q1 2022Q1 2027Q1 2032Q1
China
-15.0
-10.0
-5.0
0.0
5.0
10.0
15.0
2007Q1 2012Q1 2017Q1 2022Q1 2027Q1 2032Q1
Eurozone
-6.0
-1.0
4.0
9.0
14.0
2007Q1 2012Q1 2017Q1 2022Q1 2027Q1 2032Q1
Kenya
Real GDP, % chg year ago Real GDP, % chg year ago
Real GDP, % chg year agoReal GDP, % chg year ago
Forward-Looking Risk 18
Phases of Scenario Workflow
Key Assumptionsx
Global Macroeconomic
Modely
Market and Credit Risk Models
z(x,y)
Combined Forecast
(x,y,z)
Credit risk (PD, LGD, EAD, ECL) and market risk instruments forecast
Macroeconomic series forecast
Forward-Looking Risk 19
Standard Scenarios
Moody’s Analytics Off-the-Shelf Scenarios
Expanded Regulatory Scenarios
Custom Scenarios
Partially or Fully Specified Economy Assumptions
Thematic Event-Driven Scenarios
Scenario Studio
Web-based Scenario Building Application
Real-time Rigorous Forecasting Process
Forecasts for 70+ countries used by 780 Clients worldwide
Scenario Generation Using Moody’s Analytics Global Model
S1 S2 S3 S4BL S5 S6 S8 FED PRA EBA CU
MOODYS’ ANALYTICS BASELINE + S1-S8 , S0 EXPANDED REGULATORY CUSTOM
S0 CO
CONSENSUS
Forward-Looking Risk 20
Pillars of Scenario Generation
ISeverity
Quantitative representation of “How favorable/adverse is given scenario”
Ensures that scenarios are representative and symmetric
around baselineGuides assignment of probability
weights
IINarrative
Determines overall nature of the scenario and guides the exact path
of forecastsHelps with understanding and
interpretation of scenariosEnsures that scenarios are globally
consistent
IIITransmission
Global linkages in models transmit shocks across countries
Ensures consistency of forecasts across countries
Delivers sizable initial shocks to models
Forward-Looking Risk 21
GDP Growth %, Annualized avg., 10,000 Simulations over a 5-yr PeriodScenario Calibration: Discrete Scenario Prob.
Source: Moody’s Analytics
0
100
200
300
400
500
600
700
800
-2.6 -1.8 -1.0 -0.2 0.6 1.4 2.2 3.0 3.8 4.6 5.4
S0: 96%severity
S0: 7% WeightS3: 10% downside severity
S3: 23% WeightBL
0.50 Severity40% Weight S1: 23% Weight
S1: 90% severity
S4: 4% downside severity
S4: 7% Weight
Forward-Looking Risk 22
Scenario Forecasting
15,000
17,000
19,000
21,000
23,000
18 19F 20 21F 22F 23F 24F 25F 26F 27F 28F
S1
S3
Baseline
78,00088,00098,000
108,000118,000128,000138,000148,000
18 19F 20 21F 22F 23F 24F 25F 26F 27F 28F
S1
S3
Baseline
9,0009,500
10,00010,50011,00011,50012,00012,500
18 19F 20 21F 22F 23F 24F 25F 26F 27F 28F
S1
S3
Baseline
4,500
5,000
5,500
6,000
6,500
7,000
7,500
8,000
18 19F 20 21F 22F 23F 24F 25F 26F 27F 28F
S1
S3
Baseline
U.S. GDP, 2012 bil. USD China GDP, 2015 Bil. CNY
Kenya GDP, 2009 Bil. KESEuro Zone Inflation, % change yr ago
Sources: Eurostat, ECB, Moody’s Analytics
Forward-Looking Risk 24
High-quality data
Sound model
Sound assumpt-
ionsLogistics
Comprising:» Process – The steps
taken in the production of a forecast
» Platform – the tools used to implement the forecasting process
Specifically:» Up-to-date» No errors» Long time series» Temporally consistent» Accurately calculated
Elements of Forecast Integrity
Forward-Looking Risk 25
Installed-software world» Sequential economic linkages» Simplified international interactions» Laborious cross-country comparison» Geographic aggregates post-
processed» Serial computing» File juggling» High analyst coordination costs
Cloud platform world» Simultaneous economic linkages» Sophisticated international interactions» Rapid cross-country comparisons» Geographic aggregates endogenous
» Parallel computing» Single database» Reduced analyst coordination costs
A cloud platform enables a distributed process with a globally linked model
Technology-enabled forecasting
Forward-Looking Risk 26
» A secure web application for scenario forecasting
» Facilitates collaborative forecasting
» Hosts several Moody’s Analytics models – Global, U.S., sub-national
» Supports rigorous forecast governance processes
» Enables model customization
Scenario Studio is …
Forward-Looking Risk 28
Linking Scenarios to Risk Measures
• Macroeconomic data• Baseline scenario
forecasts• Alternative scenarios
forecasts
Global Macroeconomic
Model
• Credit risk• Market risk• Interest rate risk• Liquidity risk
Key Risk Models• Expected Credit Loss• Capital Assessment• Change in Net Interest
Income• Change in Economic
Value of Equity
Key Risk Measures
IFRS9, ICAAP, IRRBB, Stress Testing, Business Planning
Forward-Looking Risk 29
Expected/Stressed Loss Calculation Framework
Macroeconomic Scenario ForecastMinimum 3 scenarios (baseline, upside &
downside)
Scenario Probability Weights
Macro-Conditioned, Point-in-Time, Forward-Looking Probability of
Default
Credit Stage1, 2 or 3 based on credit risk
Point-in-Time ConversionIf through-the-cycle
Default Risk MeasurePD model; external or internal rating
Loss Given Default
Exposure at Default Discount Factor Expected Credit
LossX X X =
Forward-Looking Risk 30
PD Modelling Techniques and ApproachesModel Types Vary by Need
Vintage-level
Panel-data modelling approach where the data is split by vintages
Historical data covers at least five years and there is a large number of
accounts in the portfolio
More suitable for big banks with long historical data and A-IRB models
Account-level
PD is forecasted using customer and loan characteristics, and
macroeconomic indicators
Historical data is short (e.g. 1-2 years) and/or the frequency of the data is
low.
More suitable for medium/small size banks
Portfolio-level
PD directly linked to macroeconomic drivers using time series techniques
Portfolio has restricted aggregate information available such as number of accounts in DPD buckets, outstanding
balance in DPD buckets, etc
More suitable for banks with lack of customer level data
Forward-Looking Risk 31
PD Vintage-level Approach
Variable capturing the heterogeneity across cohorts: vintage dummies,
portfolio characteristics (LTV, asset class/collateral type, geography, etc.)
and/or economic conditions at origination
Lifecycle Quality of Vintage Forward-looking Indicator
Dynamic evolution of vintages as they mature
Sensitivity of performance to the evolution of macroeconomic and credit
series
PD= f( , , )
00.010.020.030.040.050.060.070.080.090.1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
PD
Age
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
PD
Age
00.010.020.030.040.050.060.070.080.090.1
t0 t+4 t+8 t+12 t+16 T
PD
Time
Forward-Looking Risk 32
PD Vintage-level ApproachMortgages Example
Vintage dummies
Economic conditions at origination
Portfolio data at origination
1. Lifecycle 2. Quality of Vintage 3. Forward-looking Indicator
Large number of accounts leads to implementation problems.
Solution:Build curves based on the different combinations of score bins, segments and vintages.
Final PD Model
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
1 11 21 31 41 51 61 71 81
PD
Age
Predicted Lifecycle
Lifecycle
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
1 11 21 31
PD
Age
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
T-k T-30 T-15 T T+15 T+30 T+45 T+60 T+75 T+m
PD
Time
HPI YoY GrowthHistory Baseline Upside Downside
0
0.005
0.01
0.015
0.02
0.025
T-k T-4 T T+4 T+m
PD
Time
Default Rate Fitted Values BaselineFitted Values Upside Fitted Values Downside
Forward-Looking Risk 33
PD Vintage-level ApproachCredit Cards Example
Vintage dummies
Economic conditions at origination
Portfolio data at origination
1. Lifecycle 2. Quality of Vintage 3. Forward-looking Indicator
0
0.002
0.004
0.006
0.008
0.01
0.012
1 5 9 13 17 21 25 29
PD
Age
Predicted Lifecycle
Lifecycle
Large number of accounts leads to implementation problems.
Solution:Build curves based on the different combinations of score bins, segments and vintages.
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
PD
Age
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08
T - k T T+15 T+30
Time
Private Consumption (YoY Growth)History Baseline Upside Downside
Final PD Model
0
0.0005
0.001
0.0015
0.002
0.0025
0.003
T-k T-12 T-9 T-6 T-3 T T+3 T+6 T+9 T+k
PD
Time
Default Rate Fitted Values Baseline
Fitted Values Upside Fitted Values Downside
Forward-Looking Risk 34
Account-level PiT PDMapping IFRS9 PDs to IRB PD
The vintage PD model is used to map the shape of the PD curves to an account-specific risk metric in order to obtain consistent PD levels based on the latest available credit risk information.
0%
1%
2%
3%
0 5 10 15 20 25 30 35 40 45
Vint
age-
leve
l12m
PD
Months-on-book
Baseline UpsideDownside Prob. - WeightedActuals
0
0.01
0.02
0.03
0 5 10 15 20 25 30 35 40 45
Prob
.-wei
gthe
d12
m P
D
Months-on-book
Vintage Account
K
K Scaling factor Scorecard PDPD PD
Forward-Looking Risk 35
Portfolio-level Modelling
» If a portfolio has restricted aggregate information available such as number of accounts in DPD buckets, outstanding balance in DPD buckets, etc.:
– Model default rate calculated as number of defaulted accounts at time t on total number of accounts at time t. Link to macro drivers.
– Alternatively, use another portfolio-related metric as the dependent variable: portfolio delinquency, total balance, portfolio age, etc.
0 – 29 DPD 30-59 DPD 60-89 DPD 90-119 DPD
0-29 DPD 95.37% 2.13% 0.69% 1.81%
30-59 DPD 77.57% 1.82% 0.64% 19.97%
60-89 DPD 43.57% 1.05% 0.38% 55.00%
90-119 DPD 0.00% 0.00% 0.00% 100.00%
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
T-k T-26 T T+26 T+52 T+78 T+m
PD
Time
PD Baseline PD Upside PD Downside
Forward-Looking Risk 36
Optimal Variable Searching Tool
User friendlyPerforms the Variable Selection Algorithm for as much as 25 potential drivers in one step.
Flexible and customizableAllows the user to specify the model as desired.
FunctionalExports pre-selected models and estimation statistics to an Excel file.Makes historical two-way graphs with drivers.Computes cross-validation.
Forward-Looking Risk 37
OVS Customizable Features
» Target variable
» Scenarios
» Potential drivers and expected signs
» Maximum number of drivers in the final model
» Maximum number of lags for drivers
» Estimator and estimation options: any built-in estimator in the software
» Correlation coefficient threshold. Default value is 0.75.
» Maximum p-value on estimated coefficients. Default is 0.05
» Additional variables that enter in the model by default
Forward-Looking Risk 38
Dynamic Credit Risk Model-BuildingVariable selection for scenario-conditional forecasting
All combinations of size k from vector m
𝑪𝑪𝒎𝒎𝒌𝒌
Exclude models with collinear drivers
Pairwise Correlations(optional)
Ranking Criteria• Adjusted R2/RMSE• Likelihood-based criteria• Stationarity and cointegration• Validation
Optimal Model
k: right-hand-side variablesm: potential economic & internal drivers
• Expected estimated signs on drivers • Statistically significant drivers
Selection Criteria(optional)
Best Subset Variable Selection Algorithm
Forward-Looking Risk 39
Optimal Variable Searching ToolWeb Application
» Allows to run OVS on your own data
» No R installation needed
» Runs in browser
» Easy-to-use, no code involved
» 3 menu items on the sidebar with the last one showing OVS results
1. File upload
– upload a file with data in CSV format– upload the appropriate permutation
file – supplied by Moody’s
Forward-Looking Risk 40
OVS Tool Web ApplicationVariables Selection
2. Variable selection– Choose one target variable– Select fixed explanatory variables (optional)– Select potential drivers from the remaining
variables– Specify the explanatory variables for which
positive coefficient is required (optional)– Specify the explanatory variables for which
negative coefficient is required – Select variables for which p-value should not be
tracked (optional)
Forward-Looking Risk 41
OVS Tool Web ApplicationParameter Set-up & Results
3. Parameter set-up & OVS results– Choose the maximum number of drivers that can
be included in the model– Specify a p-value threshold for testing
significance of explanatory variables– Input path to a file where you want to export the
OVS results and a file name– Specify the maximum correlation coefficient
between each pair of variables– Choose GLM type that will be used for estimation– Press the run OVS button to obtain results– Results appear in the table below and they are
exported to the file you specified
Forward-Looking Risk 42
Significant Increase in Credit Risk
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
0 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96 102 108 114 120 126 132 138Age (Months-on-book)
Account-level Reporting Lifetime PD Account-level Origination Lifetime PD
d
d Distance to assess for threshold» To measure the change in risk since initial recognition, we examine the proportional difference between
– the lifetime PD at the reporting date Lifetime PD(T) , and
– the lifetime PD at the same age as the reporting date forecasted at origination Lifetime PD0(T)
» Distance b is utilized as the metric and is the percentage increase to the lifetime PD curve between origination and reporting date. Increases are examined to determine how to identify which are deemed significant.
Forward-Looking Risk 43
LGD Design ApproachesBalance and RecoveriesFor a facility i, time t and workout period w:
𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖 = 1 −𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑖𝑖,𝑡𝑡 − 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑖𝑖,𝑡𝑡+𝑤𝑤
𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑖𝑖,𝑡𝑡
By AssumptionLGD of 50-60% for PF, 30-40% for RE and 65-75% for CC; fully insured products usually get LGD of 5-10%.Estimates of recovery costs range from 1-2%.
Default Vintages & Macro Drivers Roll Rate Modelling
𝑅𝑅𝑅𝑅𝑖𝑖𝑡𝑡 = 1 − 𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖𝑡𝑡
Forward-Looking Risk 44
Prepayment Model with Macro Overlay
Customer, Loan Characteristics and Macroeconomic Factors
Customer Characteristics: First-time Buyer, Employment Status, Primary Income Verification, Borrower Income, Employment Status
Loan Characteristics: Loan Age Percentage (Lifecycle), Updated LTV with HPI, Equity Release, Debt Consolidation, Loan Restructure, Balance-to-income, Origination Channel, Time to Next Revision Date, Purpose
Macroeconomic Factors: Unemployment Rate
Prepayment Rate Scenario Forecast
2018-Jan 2022-Jan 2026-Jan 2030-Jan
25%
20%
15%
10%
5%
–Baseline – Moderate Recession – Protracted Slump – Strong Growth
UK Mortgage, Loan-level Model
» Modelling prepayment factor interpreted as the probability for a facility of not being (fully) prepaid by the end of year.
» Logistic/fractional logit depending on the granularity of the model (customer, vintage or portfolio level)
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