Preparing for Defaults in China s Corporate Credit Market David Hamilton, PhD Managing Director, Singapore Glenn Levine Senior Economic Research Analyst, New York Irina Baron Quantitative Credit Risk, New York 1
Today s Presenters David Hamilton, PhD Managing Director Stress Testing and Credit Risk Analytics, Asia-Pacific Singapore Glenn Levine Corporate Stress Testing Model Lead, Capital Markets Research Group New York Irina Baron Quantitative Credit Risk Research Analyst, Capital Markets Research Group New York 2
About Moody s Analytics Credit Risk and Macroeconomic Models Software Data Macroeconomic and Credit Research Training and Certification Consulting and Advisory Services Credit Ratings Credit Research Global Presence, Local Expertise 3
Preparing for Defaults in China s Corporate Credit Market 1. China s macroeconomic and credit risk outlook What are key statistics telling us about future risk? 2. Measuring and managing the default risk of Chinese firms What strategies are effective for spotting the riskiest exposures in a credit portfolio? 3. Incorporating macroeconomic variables into default risk forecasts How can we condition PDs on macroeconomic variables? How can we utilize such conditioned PDs for stress testing and IFRS 9 impairment calculations? 4
Definition of Default Four types of events constitute a debt default under Moody s Investors Service s definition*:» A missed or delayed disbursement of a contractually-obligated interest or principal payment» A bankruptcy filing or legal receivership by the debt issuer or obligor» A distressed exchange whereby: A borrower offers creditors a new or restructured debt or a new package of securities that amount to a diminished value relative to the debt obligation s original promise and The exchange has the effect of allowing the issuer to avoid an eventual default» A change in the payment terms of a credit agreement or indenture imposed by the sovereign that results in a diminished financial obligation * Excerpted from Moody s Rating Symbols and Definitions (Moody s Investors Service, 2016) 5
Corporate Debt in China Has Grown Rapidly Since 2008 China s corporate debt as a percent of GDP has grown sharply since 2008 and has surpassed most other major economies globally. Data sources: Moody s Investors Service and Bank for International Settlements 6
Growth in Risky Corporate Debt Has Historically Led to Surges in Default Rates Data set includes US bond and loan issuers rated by Moody s Investors Service between 1992 and 2015 7
An Independent, Quantitative Credit Risk Model Can Be Useful Distribution of China Onshore Ratings by 10 Domestic Ratings Agencies Distribution of Ratings Implied by Moody s Analytics Probabilities of Default AA- 3.3% Non-Investment Grade 0.05% Data sources: Financial Times (Wind Information) and Moody s Analytics 8
The Number of Firms with EDFs Has Nearly Tripled Since 2002 as Listings Have Boomed Data source: Moody s Analytics 9
China s Macroeconomic and Credit Risk Outlook 10
Despite Challenges, China s Economy Has Been Proven to Be Resilient 11
Moody s Analytics Baseline Outlook is for Solid Economic Growth GDP Growth, % Change Year Ago Sources: National Bureau of Statistics, Moody s Analytics 12
Default Risk for Chinese Firms Turned 2015 But Remains in the Range of the Past 6 Years Aggregate One-Year Probabilities (EDFs) of Default for Chinese Firms Data source: Moody s Analytics 13
China s Economic Outlook + 4 Alternative Macroeconomic Scenarios GDP Growth, % Change Year Ago Sources: National Bureau of Statistics, Moody s Analytics 14
Default Risk Forecasts Under Alternative Macroeconomic Scenarios Median One-Year Default Probabilities (EDFs) for Selected Industry Sectors China All sectors Consumer Discretionary Energy Health Care Data source: Moody s Analytics 15
Measuring and Managing the Default Risk of Chinese Firms 16
Measuring PD: Moody s Analytics Expected Default Frequency Model Expected Default Frequencies (EDFs) are derived from a causal model driven by fundamental credit risk factors: when the market value of a firm s assets is insufficient to cover its liabilities, then the firm defaults. Assets = Assets Default Point e rt Φ(d 2 ) + Equity Φ(d 1 ) The market value of a firm s assets is not directly observable. The EDF model utilizes a key insight to estimate the market value of assets: a firm s equity is like a call option on its asset value, with a strike price equal to liabilities due. There are 3 main drivers of the EDF model: 1. The default point: derived from a firm s liability structure 2. Market value of assets: inferred from equity prices 3. Volatility of the market value of assets: inferred from equity volatility 17
Do Models Informed by Equity Market Prices Work for Chinese Companies?» Model accuracy depends on the availability and quality of input data. A major advantage of Moody s Analytics models is the decades of experience developing models for many different economies world-wide.» EDF measures have proven to be effective measures of default risk in markets with distinctive or unique institutional features (e.g. Japan).» EDF measures do not include the effect of external support. They are useful stand-alone measures of risk that can and should be compared with measures that do include external support (e.g. ratings).» Chinese share prices (as well as other asset prices) are reliable enough that the PBOC uses them in policy decision making.» Institutional or market features that attempt to mitigate default may affect the expected level of PD at a given point in time, but as we will show effective early warning can still be achieved by observing relative PDs. 18
One-Year Accuracy Ratios for Chinese Companies Compare Favorably to Other Markets 1-Year Accuracy Ratio # Firms # Defaults China 62.64% 3,724 77 Japan 77.88% 4,558 93 Australia 70.06% 2,525 171 USA 78.14% 9,243 1,126 W. Europe 68.40% 7,459 509 Data set includes all firms in respective countries between 2007 and 2015. The Accuracy Ratio is a rank correlation statistic that tell us how well a forwardlooking risk scoring system identifies defaulters as well as non-defaulters. Data source: Moody s Analytics 19
Developing Strategies for Early Warning of Default Risk» One of the primary use cases for EDF measures is for early warning of potential credit events.» Monitoring and early warning are problems of classification: which firms in a portfolio should be considered relatively more risky, and therefore merit deeper investigation?» Moody s Analytics research has identified several useful strategies for developing early warning signals: 1. Absolute EDF level 2. Relative EDF level 3. EDF change 4. Relative EDF change 5. Slope of PD term structure 20
The EDF Measure for Hidili Surpassed the Early Warning Level in Mid-2011 and Stayed On Alert Strategy 1: EDF Level vs. Warning Level Data source: Moody s Analytics 21
Hidili s EDF Measure Began to Underperform Its Industry 63 Months Prior To Default Strategies 2 & 3: Relative Level & Relative Change Hidili s EDF measure broke above the median of its industry peer group The company s EDF measure crossed and remained above the 90 th percentile of its peer group Data source: Moody s Analytics 22
Ansteel Case Study Strategy 1: EDF Level vs. Group The EDF measure for Ansteel tracked the trigger level from 2011 until late 2014. The company's current EDF measure of 1.78% is below the China steel and metal products group's trigger of 5.76%. Strategies 2 & 3: Relative Level & Relative Change Ansteel s EDF measure has deteriorated to the point that it is worse than over 75% of firms in its industry sector. Data source: Moody s Analytics 23
Incorporating Macroeconomic Variables Into Default Risk Forecasts 24
EDF and Stressed EDF Measures EDF Unconditional PD (no assumption about the economy) The 1-year PD forecast as of today Stressed EDF Conditional PD (based on an economic forecast) Output is a 60 month time series forecast of the 1-year PD 1 to 10 year term structure; longer available 1-year horizon only Around 40,000 firms daily, globally 20,000 firms daily in North America, Western Europe, Japan, Australia/NZ, China/HK Data source: Moody s Analytics 25
Stressed EDF Models Are Historically Accurate In-Sample, Perfect Foresight Median Stressed EDF vs. Median Unconditional EDF China & Hong Kong Australia & New Zealand Japan Western Europe North America Data source: Moody s Analytics 26
CCAR Simulations Using Stressed EDFs Yielded EL Rates for Corporate Exposures Very Close to the Fed s Moody s Analytics Forecasted C&I Portfolio EL Rates vs. FRB Reported C&I EL Rates Data Sources: Moody s Analytics and The Federal Reserve Board 27
IFRS 9 Compliant PD Models Must Satisfy Several Requirements (IFRS 9 减值计提要求 ) 28
IFRS 9 Scenario-Conditioned, Probability- Weighted PDs 1 Economic Scenarios (GDP) 2 Stressed EDF Measures 4 Probability-Weighted EDF 3 Probability Density Function (GDP) Data Source: Moody s Analytics 29
Bank of China Stressed EDFs and IFRS 9 Probability-Weighted PDs % Data Source: Moody s Analytics 30
Summary and Conclusion 1. Corporate debt in China has risen sharply in recent years China s economy has been resilient, but faces several challenges ahead Corporate credit risk is rising, though still in line with the level prevailing over the past six years 2. EDF measures are ideal tools for developing early warning signals: Pointin-time, granular, long history Early warning can be achieved by looking at EDF level, relative EDF level, and EDF change Warning levels can be calibrated to actual data 3. EDF measures easily incorporate macroeconomic variables for scenario based applications, like IFRS 9 impairment and stress testing 31
Contact Us David Hamilton Managing Director Stress Testing and Credit Risk Analytics, Asia Pacific +65 6511 4650 tel +65 9236 1556 mobile david.hamilton@moodys.com Moody s Analytics 6 Shenton Way #14-08 OUE Downtown 2 Singapore 068809 Glenn Levine Corporate Stress Testing Model Lead, Capital Markets Research Group +1 212 553-9595 tel +1 646 28077-44 mobile glenn.levine@moodys.com Moody s Analytics 7 World Trade Center New York, NY 10007 USA Irina Baron Quantitative Credit Risk Research Analyst, Capital Markets Research Group +1 212 553-4307 tel irina.baron@moodys.com Moody s Analytics 7 World Trade Center New York, NY 10007 USA 32
Research Insights From Moody s Analytics China Outlook: The Cycle Turns Up, July 2016 Probability-Weighted Outcomes Under IFRS 9: A Macroeconomic Approach, in Moody s Analytics Risk Perspectives, June 2016 Estimating US Credit Risk Under the Fed's CCAR 2016 Severely Adverse Scenario, May 2016 Using EDF Measures to Identify At-Risk Names A Monitoring & Early Warning Toolkit, April 2016 From Moody s Investors Service Spillover from Potential Dislocation in Onshore Bond Market Would Be Limited, August 2016 Authorities Have Tools to Avert Financial Crisis, but Erosion of Credit Quality Likely, June 2016 33
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