Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Stress Testing and Liquidity Risk Management Antonio García Pascual Federico Galizia Monetary and Capital Markets Department International Monetary Fund Quant Congress USA New York, July 8-9, 2008 The views expressed in this presentation are those of the authors and do not necessarily represent the views of the IMF or IMF policy 1 Motivation: market induced credit risk … SIVs & subprime Forex mortgages • • • SIVs may have managed duration risk ... but overlooked market-induced credit risk (i.e. higher defaults because of higher interest rates) • Popular in Italy pre1992 (ECU) and in CEEC currently (CHF) Devaluation makes debt service more expensive while also hurting the economy (and jobs) … can result in loss of market liquidity for instruments and funding liquidity for institutions 2 Outline • • • • Design of adequate stress tests and scenario analysis Modeling linkages: N-th to default CDS basket Linkages between credit, market and liquidity risks Strengthening liquidity management 3 Macro-scenario analysis can help risk management Definition of macro scenario Stressed PDs Macroeconometric model Measuring the impact: Measuring the impact: 2nd round effects, feedbacks Exposures Stressed & LGDs Correlations Algorithm / Monte Carlo Logit, dynamic panel ... Probability Portfolio Loss Distribution for “Bank X” under stressed scenario 99.9% VaR EL = PDxLGD Unexp. Loss Losses 4 Calibrating a severe macro scenario (“1 in 20 years”) Country X. Macro Scenario: "Sudden Stop" Real GDP growth Baseline Stress Scenario Average inflation Baseline Stress Scenario Interest rates (policy rate) Baseline Stress Scenario Credit growth Baseline Stress Scenario Real Estate Prices (growth) Baseline Stress Scenario 2008 2009 2010 4.5 4.0 4.0 -0.5 4.0 1.0 7.5 7.0 6.0 5.0 5.0 4.0 5.0 6.0 4.8 8.0 4.5 6.0 25.0 10.0 20.0 5.0 15.0 4.0 -10.0 --15.0 --5.0 Forecast horizon: typically 2-3 years, in line with: • The forecasting horizon of the macro model • Tracing the lagged impact on the portfolio • The medium-term strategic planning of the institution 5 Measuring the impact of a macro scenario From supervisory agencies: A few examples of models linking macro scenarios to bank-specific risk factors Bundesbank • Dynamic panel for loan loss provisions: LLP = f [credit growth, GDP growth, change in interest rates] Banco de España • • Corporates, dynamic panel for default rates: DR = f [GDP growth, credit growth, interest rates] Mortgages, dynamic panel for default rates: DR = f[unemployment , interest rates] LGD = f[housing prices and changes in interest rates] Banque de France & Comission Bancaire • • Logit model for default rates: PD = f [GDP, long and short interest rates] Regression model for Net interest income: NIC = f [interest rate, private demand, average PD] Banca d’Italia • • • Preliminary PCA analysis on macro variables to reduce dimensionality Dynamic panel for default rates: DR = f [output gap, inflation , interest rate) Regression for operating profits: OP = f [economic activity, equity return, change in interest rates] 6 Outline • • • • Design of adequate stress tests and scenario analysis Modeling linkages: N-th to default CDS basket Linkages between credit, market and liquidity risks Strengthening liquidity management 7 From macro to market to credit risk An alternative to direct econometric modeling of PDs is a MtM model à la Merton Macro Scenario (systemic risk) Sectors Asset values Idiosyncratic risks Portfolio structure: • Industry • Geography • Size (concentrations) Loss Distribution “Downturn LGD” Stressed EDFs (99% VaR) Monte Carlo PLD 99 percentile Expected Loss Unexp. Loss Losses 8 From macro to market to credit risk: NTD CDS basket (Avesani, Garcia Pascual, Li, 2006) • Portfolio of N different companies each associated with a given CDS spread and a recovery rate. • Correlation of defaults is driven by m common factors and creditworthiness for each company i (i=1,…,N) depends on its asset value xi : xi ai1M1 ai 2 M 2 ... aim M m Zi 1 ai21 ai22 ... aim2 The conditional default probability of company i : The risk-neutral PD that company i defaults before time t (forward default hazard rate λi) : Under a copula model, the xi are mapped to ti (time of default) : x (a M ... a M ) i1 1 im m prob( xi xi | ) H i i 2 2 1 a i1 ... a im t i ( u ) du 0 Qi ti t 1 e xi F 1 Qi t ; prob xi xi | Qi t | 9 CDS spreads: 15 Large complex financial institutions 80 "Higher risk" profile (LEH, MER, GS, MS, JPM) 70 60 50 Up until Jun 2007: LEH "Medium risk" profile (CS, DB, BoA, Citi) • “Higher” risk profile: (LEH, MER, GS, MS) • "Lower “Medium” risk profile : (JPM,SG, CS, DB,BAR, BoA, BNP Citi) ) risk" profile (UBS, HSBC, ABN, • “Lower” risk profile: (UBS HSBC, SG, ABN, BAR, BNP) MER GS MS JPM CS DB 40 BoA 30 Citi UBS 20 HSBC SG 10 ABN BAR Apr-07 Jan-07 Oct-06 Jul-06 Apr-06 Jan-06 Oct-05 Jul-05 Apr-05 Jan-05 Oct-04 Jul-04 Apr-04 Jan-04 Oct-03 Jul-03 Apr-03 Jan-03 0 BNP 10 Correlation of equity returns SG BNP DB ABN SG 1.00 0.87 0.70 0.80 BNP 1.00 0.69 0.73 DB 1.00 0.67 ABN 1.00 HSBC BARC UBS CS BoA CITI JPM LEH ML GS MS HSBC BARC UBS 0.65 0.62 0.55 0.68 1.00 0.66 0.67 0.61 0.68 0.78 1.00 0.73 0.67 0.65 0.75 0.68 0.73 1.00 CS BoA CITI JPM LEH ML GS MS 0.67 0.59 0.64 0.63 0.63 0.62 0.81 1.00 0.01 0.04 0.27 0.05 -0.06 0.04 -0.01 0.09 1.00 0.08 0.11 0.24 0.11 0.16 0.16 0.04 0.13 0.76 1.00 0.06 0.08 0.29 0.13 0.06 0.09 0.08 0.05 0.75 0.66 1.00 0.04 0.04 0.21 0.06 0.06 0.15 0.05 0.07 0.61 0.51 0.53 1.00 0.06 0.09 0.36 0.08 0.15 0.15 0.09 0.10 0.71 0.67 0.68 0.71 1.00 0.05 0.04 0.30 0.01 0.02 0.07 0.03 0.13 0.59 0.50 0.53 0.82 0.67 1.00 0.08 0.13 0.33 0.04 0.16 0.16 0.00 0.14 0.62 0.67 0.46 0.64 0.75 0.64 1.00 11 NTD probability computed under stress Baseline PDs a PD0 PD1 PD2 PDs when all factors in recession PD3 1.00 0.80 Left scale 0.60 0.40 Right scale 0.20 0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2 0 0.45 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 1.00 0.45 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 0.80 0.60 0.40 0.20 0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2 0 Avesani, Garcia Pascual and Li (2006) … can provide a measure of systemic counterparty risk 12 Outline • • • • Design of adequate stress tests and scenario analysis Modeling linkages: N-th to default CDS basket Linkages between credit, market and liquidity risks Strengthening liquidity management 13 Stress testing and counterparty risk: CDS pricing Value of Protection Leg (a function of PD*) S " risky" DV01 (a function of PnD*) PD PD* PnD • • C PnD* Upon an adverse scenario, counterparty risk “punches a hole” in the protection set … … implying that protection is now overpriced and value of CDS lower (Hull & White 2004) 14 Standard CDS valuation doesn’t work under stress Non-defaulted states (credit migration) Upon default Standard approach Use “standard” formulas with forward discount rates and default probabilities Assume value of the CDS to be equal to the LGD on the reference obligation Pitfalls It overlooks impact of counterpart risk on valuation (CDS value falls) It overlooks potential for double-default & no-recovery events 15 Is it market, credit or liquidity risk? I still trust you … in ten years! What happened to my 2-year benchmark? 16 Outline • • • • Design of adequate stress tests and scenario analysis Modeling linkages: N-th to default CDS basket Linkages between credit, market and liquidity risks Strengthening liquidity management 17 Policy recommendations on funding liquidity risk A private sector perspective on governance and organizational structure; analytical framework for measuring, monitoring, and controlling liquidity; and stress testing and contingency planning -- Stress testing practices for Risk Mgmt and capital planning -- Central bank operational frameworks should be flexible => frequency and maturity of operations, instruments, and the range of counterparties and collateral, to deal with extraordinary situations Principles for both financial institutions and the supervisory process 18 What can we do about market liquidity risk? Market participants should act promptly to ensure that the settlement, legal and operational infrastructure underlying OTC derivatives markets is sound. Cash settlement of obligations stemming from a credit event … Automate trade novations; trade data submissions and the timeliness of resolutions of trade matching errors for OTC derivatives … A longer-term plan for a reliable operational infrastructure supporting OTC derivatives Federal Reserve Bank of New York (June 9, 2008) Market participants and regulators agreed on … developing a central counterparty for credit default swaps that, with a robust risk management regime, can help reduce systemic risk Current gaps in price discovery mechanisms need to be addressed. The standardization of securitized instruments would help in this regard … A centralized over-the-counter (OTC) registry that would 19 collect and distribute transaction data ... Summary • Design of adequate stress tests and scenario analysis Macroeconomists, risk managers and quants team up • Modeling linkages: N-th to default CDS basket Macro to market to credit risk (systemic counterparty risk) • Linkages between credit, market and liquidity risks Counterparty risk appears in stress scenarios (and reality) • Strengthening liquidity management Funding liquidity versus market liquidity 20 Stress Testing and Liquidity Risk Management Antonio García Pascual [email protected] Federico Galizia [email protected] The views expressed in this presentation are those of the authors and do not necessarily represent the views of the IMF or IMF policy 21