Moodys Analytics Risk Practitioner Conference 2014 Cyclical Loss - - PowerPoint PPT Presentation

Moody’s Analytics

Risk Practitioner Conference 2014

Cyclical Loss Volatility in Auto Lending

Sebastian Ricketts Corporate Economist and Vice President of Economic Analysis

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Disclaimer

The presentation is intended for informational purposes only. The views expressed in this presentation are strictly those of the

• author. They do not necessarily represent the position of General

Motors Financial Company, Inc. or its affiliates. General Motors Financial Company, Inc. does not endorse or approve, and assumes no responsibility for, the content, accuracy or completeness of the information presented.

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Background

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Company Overview

• GM Financial is General Motors’ global captive finance company

− Achieving growth in earning assets while increasing GM’s sales through expanded and competitive product offerings − Upon the completion of the acquisition of Ally’s international assets (China JV pending), GM Financial will have a global footprint that covers ≈ 80% of GM’s worldwide sales

• GM Financial has over 20 years of operating history in North America and

decades as GM’s captive in Europe and Latin America

− Demonstrated expertise in originating, servicing and accessing capital markets to fund auto finance products − Both the North American and International management teams have led their respective

• perations through several economic and competitive cycles
• GMF has earning assets of $37B, operations in 18 countries and offers auto

finance products to approximately 14,000 dealers worldwide

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Growth in GM Financial

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GM Financial Acquisition 2010 2014 U.S. Floorplan Launched Canada Floorplan Launched Canada Subprime Launched International Acquisitions U.S. Prime APR Launch China Acquisition 2011 2012 2013 U.S. Lease Launched Canada Lease - Acquisition of FinanciaLinx

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Agenda

• 1. Cyclical Volatility across Credit Spectrum
• 2. Model and Methodology
• 3. Attributing Volatility
• 4. Where are we in the Cycle?

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Cyclical Volatility

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Data Set

• Unique data that was derived from month end snapshots of the full Equifax U.S.

consumer credit database − Includes all consumers with >=1 active account − Monthly history from July 2005 to current − Quarterly vintages at zip code level − Product Type (Loan and Lease) − Originator type (Captive. Peer, Bank, Finance Company, etc…) − Term (24/ 36/ 48 month etc..) − Data metrics # and $ (Active trades, Delinquency, C/O, Bankruptcy Filing, Closed Positive)

• In this analysis we explore

− All vintages and performance periods − 50 state aggregation − Originator type (Large lenders (Captive and Top 20 lenders)) − Product: Loan − Date metric: Charge off Units

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Portfolio View - Aggregated on Calendar Date

Calendar Date

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Data Source: Equifax

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Vintage dimension

Provides attribution of portfolio losses

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Data Source: Equifax

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Vintage dimension

Provides insight into trajectory of losses

Will provide insight into future portfolio performance Vintages shifted to common starting point

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Data Source: Equifax

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Vintage Dimension

Loss rates across vintages and credit tier

Example: The 2007Q3 vintage of 449 and below Bureau score loans; After 24 months (~2009Q3) from origination the vintage experienced roughly 20% losses from the initial pool. 680-719

Data Source: Equifax

<449

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Vintage dimension

Provides basis for advanced analysis and modeling

• Most “vintage analysis” refers to creating plots to gain intuition or utilize some simple algorithms

to time out future losses − Vintage Plots by Time by Score Band by Term by LTV ……….. − Vintage Plots by Age by Score Band by Term by LTV ………… − Lifecycle Average and Time Average − But

• Prone to high error rates when a portfolio is not static
• Cannot capture economic cycle
• Can try to capture vintage quality through segmentation to some extent
• Low scalability and high management cost
• Impractical for stress testing
• There are modeling methods available allow for a deeper investigation of heteroskedasticity

arising from “triangular” data sets. Best practices strive to incorporate explicitly − Economic dimension − Credit quality − Dynamic variation of originations for “what if” scenarios

• These methods scale well and are low cost

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Model and Methodology

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Model of forces impacting losses

Vintage (v) LifeCycle (a) Economy (t)

• Product Specific
• Age of Loan (a)
• Defaults on a 60 month car

loan have a typical loss curve associated with product and segment

• Origination Date Specific
• Cohort analysis (v)
• Loans originated in the

same period are subject to the reigning originations posture

• Calendar Time (t)
• 2007 recession impacted all

vintages at different points in their lifecycle

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Product Lifecycle

• The risk of going ‘bad’ depends on the age of the loan.
• The lifecycle for a loan is the characteristic shape that describes

the timing of the events.

• Product attributes drive the lifecycle differences

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Findings Across the Credit Spectrum

Prime volatility is significantly higher than Sub-Prime

Non-Prime Higher Cycle Volatility Lower Cycle Volatility Prime

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Vintage Credit Quality

• Vintages have unique characteristics that affect default at origination
• Show up as level shifts
• Economic and Industry conditions at time of booking
• Loan characteristics (score,ltv, dti, etc)
• Changes in underwriting standards
• Vintage Seasonality

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Vintage Quality Index

• Non-prime retrenched significantly in

the crisis

• Best ever quality in and post the

crisis as competition for loans was low to non-existent

• Growth in subprime competition has

seen normalization in credit quality

Non-Prime Prime

• Prime did not retrench during the

crisis

• Vintage Quality was negative as

lenders flocked to prime in the crisis

• As lenders have begun moving

downstream easing competition and good borrowers take advantage of low rates and an improving economy credit quality looks good

Relative to Long Run Average (2007-2014)

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Originations across Credit Spectrum

Subprime has yet to reach 2005 origination levels

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Decomposition Economy

• The consumer environment impacts all loans, regardless of age or

vintage.

• The environment is composed of several factors.
• Seasonality
• Portfolio management
• Macroeconomic environment

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Economy Index

Non-Prime Prime

Relative to Long Run Average (2007-2014)

• Non-prime: Contribution to losses

from the economy peaked in 2009

• Less bad impact through 2011
• Tailwind from economy in 2012

and 2013

• Tailwind in 2014 appears to be

moving to neutral (consistent with LR average impact) in 2015

• Prime Contribution to losses from the

economy peaked in 2009

• Less bad impact through 2010
• Tailwind from economy in 2011

and 2013

• Tailwind in 2014 appears to be

moving to neutral (consistent with LR average impact) in 2015

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Attributing Volatility

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Impact to Portfolio

Deep subprime: 70-80% of volatility attributed to

• riginations posture

(economy) at time of booking Prime: Volatility in losses attributed roughly equally

• riginations posture at

time of booking and the economic cycle Near-prime: Volatility in losses attributed more to the economic cycle

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Implications and Trends

• Overall:

− Models or analysis need to take into account different dynamics across the spectrum − Awareness of how your portfolio is affected can help navigate through a cycle

• Economy: Today, across credit spectrum the economy continues to provide a tailwind to

credit performance

• Sub-Prime: high loss scenario would be more likely from a high growth scenario where
• riginations policy becomes lax
• Effect on from economy is modest relative to originations posture (economy at time of
• rigination)
• Today, subprime vintage quality is neutral to a headwind to credit performance
• Near-Prime: high loss scenario would be more likely from an economic downturn
• Effect from economy is strong
• Today, Near prime vintage quality has normalized to a neutral position
• Prime: high loss scenario would be more likely from a combination of high growth where
• riginations become more lax followed by an economic downturn
• Effect on economy and originations posture appears to be 50/50ish
• Today, Prime vintage quality remains a tailwind

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Contact Info

Sebastian Ricketts sebastian.ricketts@gmfinancial.com 817-302-7156

www.linkedin.com/in/sebastianpalaoricketts/