MARKET RISK MANAGEMENT BLUEPRINT. Proposal for Managing Risk at a Bank

Transcription

1 1 MARKET RISK MANAGEMENT BLUEPRINT Proposal for Managing Risk at a Bank Dr Philip Symes

2 Agenda 2 I. INTRODUCTION II. COMPARISON OF VAR TECHNIQUES III. CURRENT APPROACH TO MANAGE MARKET RISK IV. METHODOLOGY BLUEPRINT V. SUMMARY

3 Introduction 3 Disparate risk initiatives are linked both on the it side and on the methodology side Conceptually the link is provided by the concept of economic capital VaR Probability UL Business Risk Debt Equity Firm value Current value

4 Introduction 4 The market risk project has to balance the goals of three distinct groups... Senior management Apples to apples comparison of risks across multiple businesses Provide basis for future economic capital allocation decisions Risk controlling Satisfy regulatory requirements and minimise regulatory capital requirements Clear, unambiguous measure of market risk as the basis for reporting and limiting risk taking Risk managers/traders Tools to understand the risk in traded position Need to perform ad hoc analyses Test novel risk metrics

5 Introduction 5 The proposed principal risk metric is a historical simulation based VAR... VAR meets the needs of senior management... Regulatory requirements Across business comparisons Basis for economic capital... And can be used by Risk Controlling/Management to Limit risk down to desk on ever individual trader levels Report risk tracking Historical simulation is currently the optimal VaR methodology choice It captures the effects of both non-normality and nonlinearity It is becoming de facto industry standard It appears to enjoy the tacit endorsement of regulators It is computationally tractable

6 Introduction 6... Clearly, though, traders and business unit risk managers need significant information in addition to VAR: Stress tests Scenario analysis Greeks and sensitivity measures Stress grids for options Hot spot and contributory VaR analysis Best hedge analysis (for linear products) Liquidity risk indicators P/L histories

7 Comparison of VAR Techniques 7 There are three generic approaches to measuring VAR Parametric VaR is the simplest approach and accurately measures VaR for instruments with linear, normal returns Monte Carlo Simulation allows the relaxation of the assumptions of linearity Historical Simulation allows the relaxation of the assumptions of both linearity and normality

8 Comparison of VAR Techniques 8 The three models have significantly different methods for arriving at a Value-at-Risk result... P A R A M E T R I C V A R M O N T E C A R L O S I M U L A T I O N H I S T O R I C A L S I M U L A T I O N 1 U s i n g h i s t o r i c a l r e t u r n d a t a, c a l c u l a t e p a r a m e t e r s o f v a r i a n c e / c o v a r i a n c e m a t r i x, C M 1 B a s e d o n r i s k f a c t o r d y n a m i c s, g e n e r a t e s c e n a r i o s f o r e a c h r i s k f a c t o r : 1 B a s e d o n h i s t o r i c a l p r i c e d a t a o v e r t h e l a s t t r a d i n g d a y s, c a l c u l a te t h e p e r c e n t a g e c h a n g e i n e a c h r i s k f a c t o r : 2 C a l c u l a t e v e c t o r o f p o r t f o l i o r i s k f a c t o r s e n s i t i v i t i e s : 3 P O S 1 x C a l c u l a t e V a R :. F r e q σ 1 1 d P d R F 1 P O S n x d P d R F n σ i j = = σ 1 n σ n 1 σ n n. P R F 1 = W P R F n C u r r e n t V a l u e 2 3 F r e q C a l c u l a t e t h e i m p l i e d c h a n g e i n p o r tf o l i o N P V f o r e a c h s c e n a r i o a n d s o r t r e s u l t s : S c e n a r i o 1 S c e n a r i o 2 S c e n a r i o 3. S c e n a r i o N D a y s P V 1 P V 2 P V 3 S o r t i n a s c e n d i n g o r d e r P V N N C u r r e n t V a l u e F r e q C a l c u l a t e V a R : ( i d e n t i f y 9 5 t h % i l e N P V ) D a y s P V P V 3 5 P V P V N 2 3 % R F i = ( R F i R F i ) / R F i % R F i = ( R F i R F i ) / R F % R F i - 1 = ( R F i R F i - 2 ) / R F i - 2 G e n e r a t e s c e n a r i o s f o r e a c h r i s k f a c t o r b y a p p l y i n g v e c t o r o f p e r c e n t a g e c h a n g e s to c u r r e n t m a r k e t v a l u e ( R F i o ) : R F i 1 = R F i 0 x ( 1 + % R F i - 1 ) R F i 2 = R F i 0 x ( 1 + % R F i - 2 ). R F i = R F i 0 x ( 1 + % R F i ). C a l c u l a t e V a R : ( s e e s t e p s 2 & 3 f o r M o n t e C a r l o s i m u l a ti o n ) D i s tr i b u t i o n o f P V V a R : = 9 5 t h % i l e V a R : = 9 5 t h % i l e 0 P V V a R : = 9 5 t h % i l e

9 Comparison of VAR Techniques 9... And correspondingly different advantages and disadvantages ADVANTAGES DISADVANTAGES Parametric Easy to incorporate many risk factors Data matrices widely available Assumes normality of distributions Fails to capture non-linearity of options Provides strong management intuition about sources of risk Monte Carlo Simulation Historical Simulation Provides accurate risk measures for options Simple, requires pricing model and historical data only Captures historical freak events Computationally intensive & requires additional approximations for multiple risk factors Provides little management intuition Requires large amounts of historical data Strongly dependent on the particular data sample Provides little management intuition

10 Comparison of VAR Techniques 10 Historical simulation is becoming the industry standard for bank-wide risk measurement 70 Participants in 1996: 5 investment banks, 4 international banks, 4 commercial banks Participants in 1999: 6 investment banks, 3 international banks and 2 commercial banks (so far) None Param etric Monte-Carlo Historical These numbers are based on an OWC-survey on risk management for traded products

11 Comparison of VAR Techniques 11 Historical simulation appears to enjoy the tacit support of the regulators Publications by Federal Reserve economists Darryll Hendricks James M. Mahoney Informal contacts between OWC and regulators Approval of other institutions historical simulation systems

12 Comparison of VAR Techniques 12 Summarising all these factors historical simulation out-performs the other techniques and is therefore our recommended methodology for the bank-wide value at risk calculations ATTRIBUTE PARAMETRIC TECHNIQUE MONTE-CARLO SIMULATION HISTORICAL SIMULATION Ease of computation Methodology: - Ability to capture non-linearity - Ability to model non-normality - Ability to capture all relevant risk factors Independence from historical data Stress testing and scenario analysis: - Ability to model significant disturbance - Ability to stress parameter assumptions - Ease of setting up scenario analysis Ability to calculate contributory VaR N/A Business perception - Perceived to be a good method by banking community - Perceived to be a good method within the bank Costs

13 Current Market Risk Management Approach 13 Historically it has emerged that the bank uses three risk tools ATTRIBUTES CASH DERIVATIVES HISTORICAL SIMULATION Coverage Cash instruments Non-cash instrument All VALUE AT RISK CALCULATION Methodology Parametric Parametric Historical Simulation Captures specific risk Yes No Yes Back-testing Dirty Dirty Yes Documented Yes ADDITIONAL FEATURES, USING THE SAME ENGINE Stress-Test Stress test and / or scenarios are run on all engines Calculate the Greeks Yes Yes No Vega term structure / profile Yes Yes No Limit tracking Yes Since the historical simulation runs on a faster engine all three risk tools take about the same amount of time to calculate VAR

14 Current Market Risk Management Approach 14 A wide range of limits is set - differentiated by product line: VAR limits usually soft (no intra-day checking capabilities) Scenario tests widely seen as the best risk management tool Nominal limits to assure diversification Greeks limits mainly set on desk / trader level Step-loss & P&L limits to manage maximal losses

15 Current Market Risk Management Approach 15 We have identified the following issues to be addressed in a proposed market risk measurement framework Not everyone in the business has a good understanding of VaR black box approach, that only gives VaR as a single figure should be avoided need to teach assumptions of VaR what does VaR mean?

16 Current Market Risk Management Approach 16 cont Supplementary information besides VaR is required to fully understand the risk taken and manage it appropriately Sometimes products & their hedges are not in the same system Existing VaR numbers are not effectively communicated data on different levels is not available to all business units Several business units would appreciate intra-day VaR numbers would also like to get other relevant risk numbers intra-day

17 Methodology Blueprint 17 The outline for the future of market risk measurement at the bank seems clear... Foundation phase (four months - six months) 1. Refine details of bank wide historical simulation approach, informed by Conversations with regulators Industry best practice IT and software constraints 2. Define additional risk measures and their use 3. Improve and extend the market data supplied by Asset Control 4. Define an appropriate VaR based limit structure 5. Disseminate VaR knowledge throughout appropriate business areas

18 Methodology Blueprint 18 The outline for the future of market risk measurement at the bank seems clear (cont.) Implementation phase (around six months) 1. Implement solution defined in foundation phase Post implementation phase (four months - six months) 1. Economic capital allocation for all risk sources 2. Introduce risk adjusted performance measures... but many details still need to be clarified

19 Methodology Blueprint 19 The key decisions surrounding the historical simulation approach are What time period should be used: 250 days used to be standard but the trend is to use longer data sets. What data should be used: Credit spreads are currently missing How should missing data be dealt with How should changes e.g. in indices be handled How should model derived data e.g. implied volatilities be handled How often should the data be refreshed Daily: More accurate but harder to implement Periodically: slightly less accurate but easier to implement Backtesting methodology

20 Methodology Blueprint 20 Risk information should be used to drive a set of management action and the risk management KEY RISK INFORMATION RISK COMPO- NENTS DECISION/ACTION DRIVEN BY INFORMATION CAPITAL ADE- QUACY RESOURCE ALLO- CATION RISK MANAGE- MENT Bank-wide VaR (1-day, close-out adjusted and 10-day) Stress test results Bank-wide Desk/Book level Quasi-real time grids (for options books) Liquidity measures Additional risk analysis Hot spots Best hedges Sensitivity Measures Limits reporting PERFOR- MANCE MEASURE- MENT Trading P&L Volatility Sharpe Ratio Return on Capital

21 Methodology Blueprint 21 Uniform risk information should be made available to all levels and tests need to be run down to desk level KEY RISK INFORMATION Bank-wide VaR (1-day, close-out adjusted and 10-day) RISK MANAGEMENT GROUP DISTRIBUTION OF RISK INFORMATION SENIOR MANAGEMENT Intra-day - Daily Daily Intra-day - Daily FRONT OFFICE Desk/Book level VaR Quasi-real time Bank-wide stress tests Daily Weekly - Monthly Daily Desk level stress tests Daily Daily (real-time for option grids) Sensitivity measures Intra-day - Daily Real-time Liquidity grade Weekly - Monthly Weekly - Monthly Additional risk information Hot spots Best hedges Limits reporting Daily Daily Intra-day - Daily Daily, as brief summary Violations only, when they occur Real-time - Intra-day Real-time Intra-day Real time - Intra-day Trading P&L Volatility Sharpe Ratio Return on Capital Daily Quarterly -Yearly Daily

22 Methodology Blueprint 22 Hot spot analysis uses break-downs of value at risk to identify major risk sources HOT SPOT REPORT ASSET CLASS US EUROPE ASIA LATIN AMERICA Middle market equity Large corporates equity Equity options Futures (3 month) TOTAL EXAMPLE Futures (1 year) TOTAL Useful dimensions are: risk factors, asset class, geography, trading desk and positions

23 Methodology Blueprint 23 Limit setting is the major risk management tool for trading operations Risk control Control risk taking decisions Ensure bank s entry into new markets in a orderly fashion Control risks to the bank s franchise and reputation arising from dealing activities Allocating risk bearing capacities Where is the best return on capital generated? Delegation of authority Assure that senior traders make major risk taking decisions Regulatory compliance Because it is required by the regulators Allocator Senior Management Risk Management LOB DISTRIBUTED AUTHORITY Limit Level Firmwide LOB Desk Trader EXAMPLE LOB = Line of Business

24 Methodology Blueprint 24 The ideal limit struture will use cascading VAR limits and additional limits tailor to specific business needs LIMIT STRUCTURE Market RISK LIMIT STRUCTURE Cascading, coherent VaR limits Desk level augmented by Sensitivity to key factors Stress test limits Nominal limits Liquidity Portfolio concentration limits Fraction of issue size/daily trading volume limits Aged position limits A set of special limits should be set to manage liquidity risk

25 Methodology Blueprint 25 Alternatives for scenario pricing in front office systems vs. separate pricing engine Criteria Pricing in FO Systems Separate Pricing Engine Position Data Mapping Not needed Mapping needed for each instrument and every source system Pricing Funcitons Best of breed Single proprietary pricing function for each instrument Performance Limitations inherent to the system Scalability enabling multi-process and multisite Market Data Feeds Connections needed to every FO system Only one connection needed Result Aggregation Traceabiltiy and Control Necessity fot integrate data from different systems End user must master all FO systems or a separate tool is necessary Flexible aggregation due to consolidated data Only one system involved Risk Methodologies Limitations inherent to the system Transition of methodologies possible Process Alignment Independent calculation in FO system Synchronization needed to align all necessary information Regulators To be validated Has been validated

26 Summary 26 Much of the market risk related activities can proceed in parallel to the credit risk work (cont.)... IMPLEMENTATION TIME LINE OVERVIEW IT AND METHODOLOGY Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Credit Foundation Credit Exposure I Credit Exposure II Credit Risk I Credit Risk II Market Risk I Market Risk II