Moody's Corporation · NYSE:MCO
Moody's — Technical Presentation (January 2019)
37 slides from Moody's's technical presentation, January 2019
Slide 1: Understanding IFRS 9 ECL Volatility with the PD Converter Volatility Attribution Tool
Understanding IFRS 9 ECL Volatility with the PD Converter Volatility Attribution Tool James Edwards January 2019
Slide 2: Scope of Today's Webinar
Scope of Today’s Webinar » The ImpairmentCalc software provides expected credit loss impairment calculations by taking user-defined asset classifications, credit risk measures, and IFRS 9 and CECL guidance to produce loss allowance. » There are several models within ImpairmentCalc that can cause ECL
Slide 3: Agenda
Agenda 1. Learning How to Interpret the Output of the Volatility Attribution Tool (~35 minutes) 2. Hands on Demo of Tool with Examples of Output (~15 minutes) 3. Q & A (~10 minutes) PIT PD Converter Volatility Attribution Tool 3
Slide 4: Learning How to Interpret the Output of the Volatility Attribution Tool
Learning How to 1 Interpret the Output of the Volatility Attribution Tool
Slide 5: A Introduction and Overview of Tool
A Introduction and Overview of Tool
Slide 6: ImpairmentCalc Rating to PIT PD Converter
ImpairmentCalc Rating to PIT PD Converter » The Rating to PIT PD Converter takes as input an agency rating or TTC PD and outputs a up-to-date and forward-looking PITPD for each instrument, based on: – Rating Grade – Country – Industry » For more details about the methodology of the Rating to PITPD C
Slide 7: Introducing the PIT PD Converter Volatility Attribution Tool
Introducing the PIT PD Converter Volatility Attribution Tool » The first major goal of the PIT PD Converter Volatility Attribution Tool is to report the changes in quarterly PITPD output across rating grades, countries, and industries » The second and perhaps even more important goal is to give an u
Slide 8: Breaking Down Quarterly PD Change into Factors
Breaking Down Quarterly PD Change into Factors » The Rating to PIT PD Converter models the relationship between rating and PIT PD: – Using country and industry specific data when available – Augmenting this data from surrounding regions or broader industry when there is a relative lack of data » Thi
Slide 9: B Description of PD Change Attribution Breakdowns
B Description of PD Change Attribution Breakdowns
Slide 10: Breaking Down Quarterly and Yearly PD Change into Factors
Breaking Down Quarterly and Yearly PD Change into Factors » PD Change Breakdowns in the Tool: 1. Model Component Attribution: Within the modelling specification, which estimated terms are driving the change in PD? 2. Geographical Attribution: From what regions are the public firm data driving the ch
Slide 11: Model Component Attribution
Model Component Attribution » PD output for a specific rating, country, and industry can be represented as: 𝑃𝐷𝑅𝑎𝑡𝑖𝑛𝑔,𝐶,𝐼 = 𝐵𝑎𝑠𝑒𝑅𝑎𝑡𝑖𝑛𝑔,𝑅𝑒𝑔𝑖𝑜𝑛 + 𝐼𝐶𝑇𝐼 + 𝐶𝐶𝑇𝐶 – Base: The base model includes an intercept term that allows the overall level of risk to vary based on the…
Slide 12: Model Component Attribution
Model Component Attribution » Due to this model specification, we can attribute the total change in PD for a country/industry/rating combination to the changes in the three factors. As an example: 𝑃𝐷𝑅𝑎𝑡𝑖𝑛𝑔,𝐶,𝐼 = 𝐵𝑎𝑠𝑒𝑅𝑎𝑡𝑖𝑛𝑔,𝑅𝑒𝑔𝑖𝑜𝑛 + 𝐼𝐶𝑇𝐼 + 𝐶𝐶𝑇𝐶 Total Attribution to…
Slide 13: Model Component Attribution- Examples
Model Component Attribution- Examples 2018Q2-2018Q3 Change for Baa3 Firms in Middle East Country Group (Aggregated Across Industry) Default Probability Model Component Attribution Country Industry Old PD New PD Change in PD Base Industry Credit Trend (ICT) Country Credit Trend (CCT) Bahrain 0.48% 0.
Slide 14: Geographical Component Attribution
Geographical Component Attribution » PD output for a specific country draws as much as possible upon EDF data from that country. Because the data is finite, however, inference is drawn from broader country group, region, and world. » We can therefore think of the output of the PD Converter for a spe
Slide 15: Geographical Component Attribution
Geographical Component Attribution 𝑃𝐷𝑅𝑎𝑡𝑖𝑛𝑔,𝐶,𝐼 = 𝑓(𝐷𝑎𝑡𝑎𝐶𝑜𝑢𝑛𝑡𝑟𝑦 , 𝐷𝑎𝑡𝑎𝐶𝑡𝑟𝑦𝐺𝑟𝑜𝑢𝑝 , 𝐷𝑎𝑡𝑎𝑅𝑒𝑔𝑖𝑜𝑛 , 𝐷𝑎𝑡𝑎𝑊𝑜𝑟𝑙𝑑 ) » The Geographical Attribution attributes the change in PD to data across these four geographical areas: 1. Country: How would PD output…
Slide 16: Geographical Component Attribution Example
Geographical Component Attribution Example Date of Date of Data Date of Data for Date of Data Data for for Country Region for World Recalibrated Country Group PD Convert Change Output Attribution (excluding (excluding (excluding Country) Country Group) Region) 2018 Q1 Output 2018 Q1 2018 Q1 2018 Q1
Slide 17: Geographical Component Attribution Example
Geographical Component Attribution Example 2018Q2-2018Q3 Change for Baa3 Firms in Middle East Country Group (Aggregated Across Industry) Default Probability Geographical Attribution Country Industry Old PD New PD Change in PD Country Country Group Broad Region Global Bahrain 0.48% 0.58% 0.09% -0.02%
Slide 18: Risk Factor Attribution
Risk Factor Attribution » The PD Converter is calibrated on CreditEdge EDF data. CreditEdge EDF is a Merton-type structural model of default probability. EDF for a specific firm is a function of the “Distance to Default”, or how many standard deviations Asset Value must fall to reach the default poi
Slide 19: Risk Factor Attribution
Risk Factor Attribution » Distance-to-Default can be roughly split in two parts: 1. The Inverse of Leverage: How far in absolute terms asset value can fall before it reaches the default point (the default point being a function of debt). 2. Asset Volatility: A measure of the average size of asset va
Slide 20: Risk Factor Attribution
Risk Factor Attribution » Since Leverage in this instance is defined as Default Point divided by Equity Value: 𝐸𝑞𝑢𝑖𝑡𝑦 𝑉𝑎𝑙𝑢𝑒𝑖 1/𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖 𝐷𝑒𝑓𝑎𝑢𝑙𝑡 𝑃𝑜𝑖𝑛𝑡𝑖 𝐸𝐷𝐹𝑖 ≈ 𝑓 ≈ 𝑓( ) 𝐴𝑠𝑠𝑒𝑡 𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦𝑖 𝐴𝑠𝑠𝑒𝑡 𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦𝑖 » For Risk Factor…
Slide 21: Risk Factor Attribution
Risk Factor Attribution » Interpreting each Risk Factor: – Equity Value: Indicates broader credit conditions in a country or region by reflecting how investors are valuing the ownership of firms › Equity values are derived from firm’s daily stock prices – Default Point: Indicates broader credit cond
Slide 22: Risk Factor Attribution Example
Risk Factor Attribution Example » Similar to the Geographical Attribution, we recalculate PD Converter output through a mixture of last period risk factor values and new period risk factor values. Date of Data Date of Data Recalibrated Date of Data for Change for Equity for Default PD Convert Asset
Slide 23: Risk Factor Attribution Example
Risk Factor Attribution Example 2018Q2-2018Q3 Change for Baa3 Firms in Middle East Country Group (Aggregated Across Industry) Default Probability Risk Factor Attribution Country Industry Old PD New PD Change in PD Equity Value Default Point Asset Volatility Bahrain 0.48% 0.58% 0.09% 0.02% 0.00% 0.07
Slide 24: Full Example
Full Example 2018Q2-2018Q3 Change for Baa3 Firms (Aggregated Across Industry) Default Probability Model Component Attribution Geographical Attribution Risk Factor Attribution Country Industry Old PD New PD Change in PD Base ICT CCT Country Country Group Broad Region Global Equity Value Default Point
Slide 25: C Additional Content in Tool
C Additional Content in Tool
Slide 26: Number of Firms in Data by Country and Industry
Number of Firms in Data by Country and Industry » The tool displays the number of firms in each Country, Industry, Country Group, and Region » This data gives users a sense of the depth of data in a particular region. » Note that countries with no observations in the CreditEdge universe are shown in
Slide 27: Geographical Breakdown of CCT and ICT
Geographical Breakdown of CCT and ICT » The CCT term is calculated as a weighted average of data from an individual country and its country group. The more data in the country, the higher weight on that country. The tool provides the percentage weights on country and country group for CCT. » The ICT
Slide 28: Historical Trend of PD Output
Historical Trend of PD Output » Finally, the tool provides graphical output of the average PD output for each country over the last 12 quarters. » Users can change the rating to be graphed, and compare across countries. They can also pick out the specific industries to be graphed. PIT PD Converter V
Slide 29: D Appendices
| ) Appendices
Slide 30: Understanding the Model Component Attribution
Understanding the Model Component Attribution 𝑃𝐷𝑅𝑎𝑡𝑖𝑛𝑔,𝐶,𝐼 = 𝐵𝑎𝑠𝑒𝑅𝑎𝑡𝑖𝑛𝑔,𝑅𝑒𝑔𝑖𝑜𝑛 + 𝐼𝐶𝑇𝐼 + 𝐶𝐶𝑇𝐶 1. Base Model: Calibrates the relationship between Example Base Values by Rating rating and point-in-time PD on a broad geographical and industry basis 100.00% - Overall…
Slide 31: Understanding the Model Component Attribution
Understanding the Model Component Attribution 𝑃𝐷𝑅𝑎𝑡𝑖𝑛𝑔,𝐶,𝐼 = 𝐵𝑎𝑠𝑒𝑅𝑎𝑡𝑖𝑛𝑔,𝑅𝑒𝑔𝑖𝑜𝑛 + 𝐼𝐶𝑇𝐼 + 𝐶𝐶𝑇𝐶 2. Industry Credit Trend: Measures the state of each of the 61 industries in the business cycle, by comparing average EDFs of firms in that industry against their 3 year…
Slide 32: Understanding the Model Component Attribution
Understanding the Model Component Attribution 𝑃𝐷𝑅𝑎𝑡𝑖𝑛𝑔,𝐶,𝐼 = 𝐵𝑎𝑠𝑒𝑅𝑎𝑡𝑖𝑛𝑔,𝑅𝑒𝑔𝑖𝑜𝑛 + 𝐼𝐶𝑇𝐼 + 𝐶𝐶𝑇𝐶 3. Country Credit Trend: Measures the state of each of country relative to the business cycle, by comparing average EDFs of firms in that industry against their three-year…
Slide 33: Understanding Risk Factor Attribution
Understanding Risk Factor Attribution » We split the change in EDF for each firm into the change in these 3 risk components: 1. Equity Value Change: Reflects the change in Equity Value for the firm, which increase Asset Value and, as an effect, reduces leverage. - This indicates broader credit condi
Slide 34: Understanding Risk Factor Attribution
Understanding Risk Factor Attribution » We split the change in EDF for each firm into the change in these 3 risk components: 2. Default Point Change: Reflects the change in Default Point for the firm, which increase the leverage of the firm: - This indicates broader credit conditions in a country or
Slide 35: Understanding Risk Factor Attribution
Understanding Risk Factor Attribution » We split the change in EDF for each firm into the change in these 3 risk components: 3. Asset Volatility Change: Reflects the change in the expectation of asset volatility for an individual firm in the next year. - This indicates broader credit conditions in a
Slide 36: Contact Us
CONTACT US Moody’s Analytics Support [email protected] moodysanalytics.com
Slide 37: PIT PD Converter Volatility Attribution Tool
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