Measuring and Managing the Impact of IFRS 9/CECL on Earnings Volatility and Capital

Transcription

1 Measuring and Managing the Impact of IFRS 9/CECL on Earnings Volatility and Capital Yashan Wang, Senior Director, Head of Valuation, Accounting, and ALM Research Jing Zhang, Global Head of Quantitative Research November, 2016

2 1 Introduction 2

3 Introducing IFRS 9/CECL Impairment Model» They are accounting standard setters response to the financial crisis to improve the accounting and reporting of financial assets and liabilities To solve the so-called too little, too late problem» A forward-looking expected loss model to replace Incurred Loss model: IFRS 9 takes a 3-stage approach in setting allowance CECL takes a life time approach» IFRS 9 will become effective in 2018 and GAAP CECL in

4 Impacts of IFRS 9/CECL Can Be Significant and Profound In addition to potential increase in allowance, IFRS9 may affect» Variation in provisions, driven by changes in PIT PDs, updates on recording dates, and Stage migration will increase earning volatility» Provision volatility increases, will increase earnings volatility» Available capital Loss allowance under CECL and FAS 5&114 can also differ materially due to differences between PIT and TTC PD as well as between the assumed loss emergence period and loan maturities These impacts on earnings and capital may be significant with consequences for loan product design, pricing, and credit portfolio management 4

5 Questions for Today» What will be the impacts of the new impairment model on The levels of allowance of credit portfolios? The volatilities of allowance and earnings? Capital?» Can we answer these questions empirically?» How can we measure and manage these volatilities ex ante? 5

6 Rest of the Presentation 1. Introduction 2. What Do Half a Million Loans Say about CECL s Impact on Loss Allowance? 3. Measuring and Managing the Impact of IFRS9/CECL on Earning Volatility and Capital 4. Appendix: Decision Capital Measure and Application to Portfolio Improvement 6

7 2 What Do Half a Million Loans Say About CECL s Impact on Loss Allowance? 7

8 Allowance Rate and Net Chargeoff (NCO) Rate, a Failed Bank 4% 3% Washington Mutual % 3 1% 2 1 0% Q1 2005Q1 2006Q1 2007Q1 2008Q1 Allowance Rate (Total Loan and Lease) Forward One-Year NCO Rate (Total Loan and Lease) Allowance to One-Year NCO Ratio (Right Axis) 8

9 The Too Little, Too Late Problem» It is a common belief that banks do not create sufficient loss reserve, early enough in time, under the existing incurred loss model, to absorb actual credit losses, in particular during economic down turns too little, too late» IFRS 9 and CECL are induced to address the too little, too late problem manifested during the recent financial crisis» Will the new accounting standards solve the problem?» How much is CECL s impact on loss allowance and earnings? 9

10 Allowance Rate and NCO Rate, Total Loan and Lease 4% Bank-1 3% 2% Bank-2 2% 1% 0% 0% 6% 4% Bank-3 4% Bank-4 2% 2% 0% 0% Allowance Rate (FR Y-9C) Forward One-Year NCO Rate (FR Y-9C) 10

11 Aggregated Allowance Rate and NCO rate of 15 CCAR/DFAST Banks 4% 3% 2% 1% 0% 2003Q3 2005Q3 2007Q3 2009Q3 2011Q3 2013Q3 2015Q3 Allowance Rate (Total Loan and Lease, FR Y-9C) Forward One-Year NCO Rate (Total Loan and Lease, FR Y-9C)» Allowance under the incurred loss model was too little, too late during the financial crisis 11

12 How Much Impact Does CECL Have?» There have been a number of reports, many based on surveys, to assess the impact of CECL» In 2011, OCC and Fed made predictions that industry allowances would go up by 30% to 50%» June 2015, Sageworks survey: 57% bankers expect 10-50% increase in allowance» September 2015, KBW predicted that small and mid-size banks would see a median increase in the ALLL of 3%» In 2015, MainStreet Technologies survey: 20% - 50% increase in allowance» August 2016 Risk article, Fitch warned US banks could see loss allowance surge by $50Bn $100Bn if implemented today. This will shave 25 to 50 basis points off tangible common equity ratios» Would CECL have solved the too little, too late problem? How much impact does CECL have? 12

13 Moody s Credit Research Database (CRD)» CRD is the world s largest historical time series of private firm loan data for C&I borrowers. There are 19 contributing banks in North America» CRD contains borrower financial statements, facility and loan information 670,293 borrowers, 1,398,955 facilities, 20,494,726 entries in total Quarterly snapshots of C&I portfolios» From 2000 Q2 to 2015 Q4» Facility information: origination date/amount, contractual maturity, unpaid balance» Borrower information: internal rating/pd, industry, geographical info, size, etc.» Forward looking, PIT PD term structures from Moody s Analytics RiskCalc» We leverage the CRD loan-level data to explore allowance under CECL and compare with the incurred loss model over the last 12 years 13

14 Modeling C&I Allowance Rate under Incurred Loss» Historical allowance rate for C&I is publically available only in recent years (FR Y-9C, 10Q)» We estimate C&I Loss Emergence Period (LEP) for each individual bank so that the modeled allowance = [PD_over_LEP * LGD * EAD] matches FR Y-9C allowance in the overlapping period» PD and EAD from CRD, LGD from Moody s LossCalc» The estimated LEP ranges from 1.3 yrs to 2.6 yrs across banks, average LEP = 1.9 yrs» We then use the same LEP to calculate allowance rate in earlier years 4% Aggregated C&I Allowance Rate of 12 Banks in CRD 3% 2% 1% 2003Q3 2005Q3 2007Q3 2009Q3 2011Q3 2013Q3 2015Q3 Modeled C&I Allowance Rate, Incurred Loss FR Y-9C Historical Allowance Rate 14

15 Allowance on C&I Portfolios, A Sample Bank in CRD 4% 2% 0% Modeled C&I Allowance Rate, CECL» CRD loan-level data: balance, maturity» PD term structure from Moody s RiskCalc» LGD from Moody s LossCalc Modeled C&I Allowance Rate, Incurred Loss» CRD loan-level data: balance, internal rating/pd» LEP calibrated to FR Y-9C and 10Q data» LGD from Moody s LossCalc 15

16 Allowance on C&I Portfolios, More Sample Banks in CRD 6% Bank-A Bank-B 4% 4% 2% 2% 0% 0% 12% Bank-C 10% 6% Bank-D 5% 8% 7% 4% 2% 4% 2% 0% 0% CECL Incurred Loss 16

17 Aggregated Allowance Rate on C&I Portfolios, 12 Banks in CRD 5% 4% 3% 2% 1% 0% 2003Q3 2005Q3 2007Q3 2009Q3 2011Q3 2013Q3 2015Q3 Modeled C&I Allowance Rate, Incurred Loss Forward One-Year NCO Rate (FR Y-9C) Modeled C&I Allowance Rate, CECL StDev Incurred Loss CECL 2003Q3 2015Q4 0.55% 0.83% Crisis Period (2007Q1 2010Q4) 0.66% 1.04% 17

18 3 Measuring and Managing the Impacts 18

19 Earnings and Earnings Volatility of a Credit Portfolio» The contribution of a loan to a bank s earnings: interest income loss provision (i.e. change in loss allowance) net charge-off due to defaults and recovery» The earnings from a credit portfolio is the sum of instrument earnings. The earnings volatility of the portfolio depends on: the earnings volatility of individual instruments» Defaults and credit migration effects the correlation between individual instruments earnings» Origination and portfolio management decisions should account for both the expected level of earnings as well as earnings volatility. 19

20 Impact of IFRS 9/CECL Impairment on Earnings Volatility» Under IFRS 9 /CECL, earnings volatility of individual instruments will likely increase Periodical updates based on current and forward looking information make the loss allowance and thus earnings more volatile over time. Introduction of life-time loss allowance for Stage 2 assets makes instrument earnings more likely to have large swings» The correlation effects: Under IFRS 9/CECL, loss allowance is calculated as the expected loss of the instrument based on the forward-looking credit quality of the borrowers. The default correlation: during economic downturn, defaults tend to cluster Non-default state credit correlations: since the migration of instruments PIT PDs are correlated with each other, IFRS 9/CECL also increases the correlation of instruments earnings, which in tern increase the portfolio s earnings volatility. 20

21 Distribution of Earnings Under IFRS 9 and IAS 39 of a Sample Portfolio Probability of Negative Earnings For a portfolio consisting of exposures to 4734 public firms in Europe:» The earnings volatility under IFRS 9 is generally higher than that under IAS 39.» The distribution of the portfolio s earnings under IFRS 9 has a fatter tail than that under IAS 39.» The difference is entirely driven by the difference in impairment models. IFRS 9 earnings volatility: 0.95% IAS 39 earnings volatility: 0.75% 1.3% chance of negative earnings under IFRS % chance of negative earnings under IAS

22 The Impacts of Correlation and Concentration Are Significant Earnings Distribution Assuming 10% Pairwise Credit Migration Correlation Earnings Distribution Assuming 40% Pairwise Credit Migration Correlation Earnings volatility: 1.1% Probability of negative earnings: 1.2% Earnings volatility: 2.5% Probability of negative earnings:6.1% 22

23 Measuring Earnings Volatility Ex Ante An Overview Measuring earnings volatility consists of two main tasks:» We need to obtain the distribution of instrument earnings up to a future date. This is similar to obtaining the value distribution in a CPM tool like RiskFrontier: We first project instrument earnings for each credit state at the future date. We then use simulation to obtain the distribution of instrument earnings.» We need to account the correlation between instrument earnings. The correlation of earnings is determined by the correlation of credit migration, which is captured by the GCorr model in RiskFrontier. The general methodology we use to measure portfolio s earnings volatility is the essentially the same as the methodology used to compute portfolio s value distribution in RiskFrontier. 23

24 Impacts of IFRS 9/CECL and CPM Decisions» Stakeholders pay close attention to earnings as it has large impacts on stock prices. Prefer high earnings and low earnings volatility» IFRS 9/CECL will potentially increase the volatilities of provision, earnings, and capital, which in turn may affect: Loan origination---terms and pricing Customer relationship CPM decisions such as portfolio construction and hedging» Credit portfolio managers objectives: Minimize the portfolio s earnings volatility given a certain level of expected earnings equivalent to maximizing expected earnings to earnings volatility ratio. Minimize the loss in portfolio earnings under extreme conditions given a certain level of expected earnings under normal conditions equivalent to maximizing expected earnings to earnings tail risk. 24

25 4 Appendix: Decision Capital Measure and Application to Portfolio Improvement 25

26 4.1 Methods of Allocating Capital: Theoretically Sound Unified Approaches 26

27 Economic (EC) and Regulatory Capital (RegC) EC is a measure of economic risk associated with a portfolio.» Accounts for diversification and concentration risks» Provides insights allowing optimized risk-return profiles, facilitate strategic planning and limit setting, as well as define risk appetite» Provides a foundation for return-to-risk measures such as RORAC and EVA decision variables RegC when binding, results in tangible costs.» Additional capital is needed for new investments.» Changes in portfolio composition can require drastic action. Dividend restrictions, potential fines, reputation impairment Access capital markets at unfavorable terms Forced fire-sale 27

28 IFRS 9/CECL Likely Increases the Uncertainty of Future Capital Buffer» Banks have to meet minimum capital requirements at the reporting date, typically with some surplus, to ensure that minimum capital requirements are still met at a future time horizon in the face of uncertainty in capital demand and supply Uncertainty of Capital Demand» The credit quality of the underlying instrument, such as TTC PD may change at horizon, potentially causing RWA to change.» The instrument EAD may change at horizon as a result of specific loss provisions, which affects RWA calculations. Uncertainty of Capital Supply» The earnings associated with the portfolio contains uncertainty. CECL and IFRS 9 s loss recognition rules require institutions update loss allowance to be based on PIT PDs (and stage dependent under IFRS 9), affecting the earnings and thus the amount of capital available.» Charge-off due to realized default loss.» Adjustment of capital supply caused by changes in the gap between Basel oneyear EL and IFRS 9 loss allowance due to different TTC and PIT PD dynamics. 28

29 Capital Breach Probabilities Under Different Accounting Rules CECL Versus FAS 5&114 IFRS 9 Versus IAS % chance of capital breach under CECL. 3.9% initial capital surplus needed to limit the chance of capital beach under FAS5&114 at 0.1%. 0.20% chance of capital breach under IFRS % initial capital surplus needed to limit the chance of capital beach under IAS 39 at 0.1%. 0.10% chance of capital breach under FAS 5& % chance of capital breach under IAS 39. *Capital surplus is defined as the difference between capital supply and RegC demand (i.e., required RegC). It is represented as a percentage of notional. 29

30 Model Framework We start with a CAPM model that underpins classical RORAC investment decision rules while incorporating the key factors that impact investment and capital allocation rules» Add the regulatory capital requirement constraint into the optimization problem Institutions need to ensure equity satisfies RegC.» Account for the fact that institutions need to worry about uncertainty in capital surplus Institutions need to hold more than minimum RegC. Determine the portfolio-level additional capital buffer according to the distribution of portfolio capital surplus and the institution s risk appetite.» Recognize that individual instruments have different contribution to capital surplus uncertainty Allocate the portfolio-level additional capital buffer to each instrument according to capital surplus risk contribution or tail risk contribution. Derive the final capital allocation measure the Composite Capital Measure (CCM) Parameterize the final measures to reflect the degree to which RegC is constraining. 30

31 Composite Capital Measure (CCM) Combining Economic and Regulatory Perspectives while Accounting for Loss Recognition» CCM accounts for the whole spectrum of credit risk (PD, LGD, maturity, and concentration risk) the regulatory capital requirement associated with the instrument The impact of loss recognition rules» CCM has the following decompositions CCM = Regulatory Capital + Regulatory Surplus Chrage + Concentration and Diversification Charge CCM = Economic Capital + Regulatory Capital Charge + Regulatory Surplus Chrage CCM = weight RC Regulatory Capital + weight EC Economic Capital + weight RS Regulatory Surplus» By design, CCM has the intuitive property that the sum of CCM at the deal level equals top of the house Capital. 31

32 Different Interpretations of CCM CCM = RegC + Concentration Risk/Diversification Benefit Adjustment 18.0% 16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% CCM = EC + RegC Cost Adjustment 18.0% 16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% Diversification Benefit Adjustment Concentration Risk Adjustment RegC EC RegC Cost Adjustment 32

33 CCM Versus RegC» CCM is imperfectly correlated with RegC, as it incorporates concentration adjustment that differs across instruments. Compare RegC and CCM» High credit quality assets recognize an EC diversification benefit which mutes the associated high RegC charge (relative to their economic risks). 33

34 CCM Versus EC» CCM is generally higher than EC as a result of the binding top of the house RegC+buffer.» CCM is significantly higher than EC for low-risk instruments given the relatively high RegC charge associated with these instruments. Compare EC and CCM Low-risk instruments» CCM converges to EC for very high-risk instruments. It happens as RegC does not reflect the dominating concentration and diversification risks of the assets. 34

35 4.2 Portfolio Improvement 35

36 Improve Portfolio Return on Actual Capital, EC, and Capital Uncertainty Using CCM RORAC» Rank these industry segments according to CCM RORAC and shift $100K from the bottom ranked industry with the top ranked industry.» The return on top-of-the-house capital (defined as RegC + additional RegC buffer), EC, and capital uncertainty (defined as the standard deviation of change in capital surplus to horizon) are calculated after each rebalance. Industry Holding ($K) Maturity PD Agriculture % Automotive % Banks and S&Ls % Chemicals % Construction % Food & Beverage % Medical Services % Mining % Tobacco % Transportation % All loans have 2% annual coupon, 50% LGD, and 20% RSQ. The risk-free rate is 1% with a flat term structure. The risk-premium parameter λ is 0.6. When applicable, minimum RegC is calculated based on Basel III advanced IRB approach; Additional RegC buffer is calculated as the minimum additional buffer needed to limit the chance of capital breach in one year under 10 bps. 36

37 Portfolio Rebalance (The First Two Iterations) Initial Holding - $100K Mining; + $100K Automotive - $100K Mining; + $100K Agriculture Industry Holding Rank Industry Holding Rank Industry Holding Rank Agriculture Agriculture Agriculture Automotive Automotive Automotive Banks and Banks and Banks and S&Ls S&Ls S&Ls Chemicals Chemicals Chemicals Construction Construction Construction Food & Food & Food & Beverage Beverage Beverage Medical Medical Medical Services Services Services Mining Mining Mining Tobacco Tobacco Tobacco Transportation Transportation Transportation

38 Portfolio Risk Return Tradeoff After Each Rebalance» After each rebalance of $100K (about 2% of the entire portfolio) according to the ranking of industry CCM RORAC, the portfolio return on capital, the EC RORAC, and the ratio between return and the standard deviation of capital surplus at horizon increase.» This example illustrates that investment decisions based on CCM RORAC can help institutions to improve return for a given level of capital available, a given level of exposure to economic risk, and a given level of RegC uncertainty at horizon. Step Rebalance EC RORAC ROE ES P σ ΔCS P 0 Initial Portfolio 11.5% 8.5% 63.7% K Automotive -100K Mining K Agriculture -100K Mining K Tabaco -100K Transportation K Medical Serv s -100K Transportation K Chemicals -100K Food & Bev K Construction -100K Transportation 11.8% 8.6% 64.5% 11.9% 8.6% 65.2% 12.2% 8.7% 65.5% 12.2% 8.8% 66.0% 12.3% 8.9% 66.6% 12.6% 9.0% 66.9% 38

39 Consolidated Capital Management Ideally capital management accounts for» The full spectrum of economic risk, including diversification and concentration risks;» The regulatory requirements and the how constraining these requirements are for the institution;» The likelihood of having insufficient capital surplus at a future date due to the uncertainty in capital supply and demand. Risk managers can address these issues through integrated measures such as CCM for a more consolidated approach of capital management, with applications toward» Risk-based pricing and incentive compensation» Capital allocation and charges» Limit setting» 39

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