Presentation Transcript

1. Developing Macro-stress tests

   Session 9 Mindaugas Leika
2. Macroprudential policy framework I. Macroprudential policy definition, targets, policy transmission channels and relationships with other policies (Monday) II. Institutional structure (Tuesday) III. Policy tools (Tuesday) IV. Risk identification and quantification: stress testing (This lecture)
3. AGENDA What is macro stress testing? Macro stress testing framework Macro ST process Use of stress tests Did STs fail?
4. Financial stability analysis Quantitative analysis Qualitative judgment Risks and vulnerabilities Stress-testing Shocks Sensitivity analysis, forecasts Transmission mechanism
5. How can we group risks: A) Credit risk B) Market risk C) Liquidity risk D) Operational risk (e.g. failure of SWIFT, software etc. BoE quantifies this) Usually arises gradually Arise instantly
6. What is the purpose of macro stress testing? Provide quantitative and forward looking assessment of the capital adequacy of the banking system*. Accountability to the public Decision-making support Measuring the impact of systemic risk Macro prudential policy tool: addresses banking system vulnerabilities (capital buffers, exposures to particular sector, absence of diversification, capital planning, investor confidence etc.) *Source: Bank of England (2013). A framework for stress testing the UK banking system.
7. Why macro stress testing: Two concepts of losses There are two concepts linked to risk mitigation techniques: I Expected losses(loan loss provisions, loan impairment charges); II Unexpected losses (economic capital). Expected losses are mean loss rate, i.e. amount that bank reasonably expects to lose. Expected losses are usually covered by loan loss provisions or loan impairment charges. It is called known part of losses. Unexpected losses represent volatility of losses, i.e. unknown part. Shareholders equity is used to absorb these losses. We have to presume, that banks not only need capital to absorb these losses, but also have to stay above minimum regulatory capital requirements through the full business cycle. The targeted level of unexpected losses depends on two factors: minimum capital requirements and targeted rating.
8. Example I Example: Bank has 100 bn in assets with 100% risk weight. Average interest rate is 8%. Shareholders equity is 8 bn (CAR 8%). Liabilities (deposits and bonds) is 92 bn. Average return on liabilities is 4%. At the end of the year bank expects to receive 8 bn in return from its assets and pay to debt holders 3,68 bn. Return to shareholders is 4,32 bn. However, if bank’s expected losses are 3% (PDs), return to shareholders is lower (return to debt holders is fixed!): 4,32-3=1,32 bn. What happens, if during an economic downturn, PDs increase up to 8% ? Bank’s losses are much higher than expected and equal to 8 bn. In this case, bank’s income is equal to its losses: 8bn-8bn=0. Bank’s payments to debt holders is fixed, hence bank needs to tap its capital base to pay interest rate: 8-3,68=4,32 bn capital left. That’s below minimum CAR of 8%. Bank needs to be closed or recapitalized.
9. Example II How much additional capital bank needs to hold? Bank provisions 3% for expected losses and needs additional reserves of 5 bn just to have zero profit. In this case return to its shareholders is 0. To come up with the worst case scenario, and calculate additional reserves, we need to perform a stress test and model expected and unexpected losses. Under Basel II IRB approach we have to model PDs, LGDs. EADs are given. Losses are expressed as: Expected losses=PDs x LGDs x EADs Under Basel II STD approach non IFRS and Basel I we model loan loss provisions (LLPs): ∆LLPs=∆NPLs x provisioning rate Unexpected losses can be measured as a number of standard deviations from expected losses (VaR concept).
10. From shocks to their outcome: how transmission mechanism works Credit risk Non- financial corporate sector Shocks originating in Real sector Transmission channels: exposure Real estate sector Households Public sector Financial institutions: profit/loss, capital Banks Transmission channels: common ownership, exposure etc. Feedback effects Market, liquidity, counterparty, contagion risks Shocks originating in Financial sector Insurance companies Other institutions Payment systems
11. Macroeconomic forecasts CB’s macro model GDP, Housing prices, interest rates, FX rate, unemployment Short-term equations with AR(1) terms and/or ECM: NPLs depencence on selected macro variables calculated for 1 to 4 quarters Long-term equations: NPLs depencence on selected macro variables calculated up to 3 years Equations on a bank-by-bank basis Banking sector data NPLs, provisions, credit growth Loan migration matrix Projected additional provisions On a bank-by-bank basis Banking sector data Interest income, expenses, credit growth, doeposits, interest rate etc. FX risk, concentration risk, income/expense, duration gap models Projected net losses/profit CAR Unexpected losses Monte-carlo simulation Number of banks that do not meet minimum CAR Macro Credit risk Stress testing model Real estate prices: collateral value for LGD calculation
12. TOP-DOWN STRESS TESTING FRAMEWORK
13. Three types of models for macro stress testing I Portfolio models (Credit Risk plus; Risk Metrics; Credit Portfolio View etc.) II Balance sheet models (Cihak, Boss et all. and modifications). III Market data based models (CCA). First type of models dominate in private sector, second and third type dominate in regulatory institutions.
14. Macro ST process
15. Macro ST process
16. Understanding the incentives There are at least three stakeholders in the stress-testing process: financial institutions, regulators and the public/markets. Usually they have different incentives: regulators want more data, more time, more extreme scenarios; financial institutions want to provide less data, use in-house models, usually less extreme scenarios. Regulators want to find the weakest components of the banking system, whereas institutions want to show resilience. Public wants “blood”- know institutions that fail the test.
17. Macro Stress testing steps Determine the objective of the stress test Design scenario Perform stress test Calculate stress losses Report results Determine actions
18. Basel II/III
19. scenario design (1) Shock matrix
20. scenario design (2) Risk mapping: from systemic risks to exogenous shocks Shock calibration Scenario design Risk correlation Macroeconomic models Baseline scenario Scenario output: macro and financial variables Adverse scenario
21. Historical experience Credit risk depends on the state of economy (business cycle)
22. Defining thresholds Expected (mean losses) E[x]=μ(x) P(X) Pass/Fail criteria and minimum capital requirements Confidenceinterval is identical to default probability: A 0,07% AA 0,03% BBB 0,1% AAA 0,01% Loan loss provisions Economic capital Loss Expected shortfall
23. Applying shocks: Normal and shocked PDF P(X) X2 represents shocked PDs, and as it has higher variance, it bears more risk than X1 X1 X2 Loan loss provisions Economic capital Loss Expected shortfall Loan loss provisions Economic capital
24. Losses and business cycle X1=f(GDP↑, FX rate, Unemployment↓, Interest rates↓, Concentration↓ etc.) X2=f(GDP↓, FX rate, Unemployment↑, Interest rates↑, Concentration↑ etc.) P(X) X1 represents upward trend X2 represents downward trend X1 X2 Loan loss provisions Economic capital Loss Expected shortfall Economic capital Loan loss provisions
25. Calculating losses
26. Calculating CAR Loan loss provisions; Forecasted from satellite credit loss model Net income before loan loss provisions; Forecasted from satellite income model Current Tier I and II capital (regulatory capital) Current RWA for: credit, market and operational risks Satellite credit growth model Loan loss provisions; Forecasted from satellite credit loss model Migration matrices
27. Use of stress tests
28. Theoretical use of stress tests What answers stress tests should provide: How much capital a bank needs to support its risk taking activities? (Forward looking) Is the current level of capital adequate? (Present) Lehman Brothers, Bear Stearns, Dexia, JP Morgan…. Did they do it right? Capital that is available vs. Capital that is needed vs. Capital that regulators need.
29. Actual use of stress tests during the crisis Stress tests popped out as a tool to address loss in public confidence Confidence was boosted by disclosing individual banks’ results, scenarios and data about exposures SCAP (US) vs. EBA (EU).
30. STs before and after crisis
31. Can Stress tests detect systemic risks? In theory, macroprudential STs should unveil the sources of systemic risk (see IMF (2012) In practice, sources indeed were identified correctly (e.g housing market in the US, contagion from Greece in the EU etc). Magnitude of shocks and subsequently their impact was miscalculated
32. Did STs fail?
33. Why stress tests can fail? (1) We can find many “wrongs”: Wrong models: too complex Wrong (absence of) data: where risks were “parked”? Wrong scenarios: underestimation of tail risk events and contagion effects Wrong incentives: no need to rock the boat, public will not understand Wrong scale: “shadow institutions” escaped Wrong policy measures
34. We calculate economic capital using 2 or three standard deviations….. If we use a normal distribution, two standard deviations from the mean means we still have almost 5 percent of observations outside of our horizon (2,5 percent in each tail). This means, we overestimate earnings and underestimate losses.
35. However during this Global Financial Crisis volatility was much higher….. In August 2007, the Chief Financial Officer of Goldman Sachs, David Viniar, commented to the Financial Times: “We are seeing things that were 25-standard deviation moves, several days in a row”. As Andrew Haldane, executive director at the bank of England noticed: “Assuming a normal distribution, a 7.26-sigma daily loss would be expected to occur once every 13.7 billion or so years. That is roughly the estimated age of the universe. A 25-sigma event would be expected to occur once every 6 x 10124 lives of the universe.”
36. Volatility depends on time period (sample from Haldane (2009))
37. A Hypothetical example of a factor whose relationship to default is not clear until a crisis pushes it to new levels Source: S&P (2010)
38. Bimodal nature of rating transitions Source: Moody’s (2013) Stress Testing of Credit Migration. A Macroeconomic approach.
39. Why stress tests can fail? (2) Underestimated probability of adverse outcomes (disaster myopia) Reluctance to include severe scenarios Willingness to hold capital under less extreme scenario only Postponement of crisis Reverse engineering: scenarios are such, that bank never violates minimum CAR Short time series in emerging market countries Data quality issues We usually never look beyond economic capital! Historical scenarios are based on historical data. We can not test anything new using data from the past only
40. Reduced form vs. full-scale stress tests Most of the stress tests banks do are reduced form stress tests Reduced form – Monte Carlo simulation Full scale – links with macro variables. Correlations Why reduced form? If probabilities are unknown, we face uncertainty. In this case randomization is an answer.
41. Why reduced form is not suitable for macro STs Reduced form does depend on assumptions about distribution. Beta distribution has fatter tails than the normal one Reduced form ST has very little connection with macro variables Is opaque Is good, once we deal with random, uncorrelated price movements or volatilities. Is not suitable, once we deal with systemic events or highly correlated movements
42. The way ahead How to incorporate balance sheet adjustments into core models? How to avoid modeling partial equilibrium situations only, i.e. include feedback effects and adjustments in broader sectors of economy? How to model nonlinearities? System’s stability is most vulnerable then nobody anticipates shocks, i.e. risks are underpriced, real estate prices are at their peaks, GDP and credit grows fast. How to model aggressive risk taking and rapid build-up of imbalances? How to model financial innovations and market liberalization (historical data are not available at all or structural breaks emerge)? How to extend stress tests to other (non-bank) financial institutions?
43. Importance for macroprudential policy Based on Borio, Drehmann and Tstatsaronis (2012) objective of the stress tests is to support crisis management and resolution. Drilling down we can formulate this objective more precisely: a) calculation of how much capital should be injected into the system to prevent credit crunch; b) Identification of weakest financial institutions; c) signaling to the market about losses and restoring confidence in the banking system; d) Improve risk management practices, models and data collection; e) Story telling: use stress tests to describe shocks, transmission channels and possible impact on financial system and broader economy.
44. Seven best practice principles proposed by the IMF Define appropriately the institutional perimeter for the tests. Identify all relevant channels of risk propagation. Include all material risks and buffers. Make use of the investors’ viewpoint in the design of stress tests. Focus on tail risks. When communicating stress test results, speak smarter, not just louder. Beware of the “black swan.” Source: Macrofinancial Stress Testing—Principles and Practices (2012)
45. What to do? Do not constrain yourselves with historical experience and scenarios (it is not contrary to the “this time is different” syndrome, i.e. one should think that worst crisis might repeat again or my country is not necessarily too much different from the ones that experienced crisis earlier) Use judgmental adjustments in scenarios Use reverse stress testing more often to find break-even points (especially important in liquidity stress-testing) Make it simple. Last CCAR (2013) emphasized simplicity. Simplicity means no complicated “black box”: executives should be able to understand and supervisors to verify In the end, follow the advise by J.M. Keynes: “It is better to be roughly right than precisely wrong”