New direction for Market Risk: Issues and Challenges

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Check out Anshuman Prasad’s 'New Direction For Market Risk: Issues & Challenges' at 3rd Annual Risk Americas presentation

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New direction for Market Risk: Issues and Challenges

  1. 1. 3rd Annual RISK AMERICAS, May 12-13, Marriott Downtown, New York City Presenter: Anshuman Prasad, Director, Risk and Analytics NEW DIRECTION FOR MARKET RISK: ISSUES AND CHALLENGES May 12-13, 2014
  2. 2. Agenda  Market Risk Management: Outstanding Issues  Impact of Regulations – Measures of Market Risk – Regulatory Stress Tests  Modeling Challenges – Valuation of Illiquid Instruments – Illiquidity Discount – Computational Trade-offs  Data Management Issues  Trends in Market Risk Systems  Organizational Culture 2
  3. 3. Market Risk Management: Outstanding Issues 3  Reliability of various measures of risk  Validation and back-testing of models to reduce model risk  Trading vs. banking book boundaries  Regulatory stress tests Regulatory Issues  Silo approach to risk management  Limited role of risk managers in decision making  Narrow, compliance-focused approach Organizational Culture  Valuation of illiquid products  Correlation under stressed environment  Model calibration  Tradeoffs: speed vs. accuracy  Incomplete and/or inaccurate data  Lack of unified data processing model  Limited involvement of business  Real-time computation, lack of integrated systems System and Data Issues Modeling Challenges
  4. 4. Impact of Regulations 4 Newer regulations impacting market risk management frameworks  Fundamental Review of the Trading Book (BCBS 219) Regulation  Stress Testing (Dodd-Frank, CCAR)  Model Risk Management (OCC SR-11/7)  Difficult to move assets between the trading and banking books  Shift to Expected Shortfall from VaR  Varying liquidity horizons Impact  Handling granular market shocks  Requires calculation of various parameters under stress  Widened the definition a “model”  Enhanced model validation and model governance framework  Emphasis on rigorous testing, model documentation and quantifying model uncertainty  Data aggregation and Reporting (BCBS 239)  Early warning systems  Comprehensive and granular reporting  Overhaul of systems and processes
  5. 5. Measures of Market Risk 5 There is no single measure of risk that can provide a consistent view of risk across market participants and regulators  Simple, established  Single view of risk Pros  Conservative  Size and likelihood of losses  Reduces cyclicality  Easy to interpret  Simple modeling procedure  Lacks coherence  Extreme tails  Inconsistencies Cons  Difficult to back-test  Prone to optimization errors  Can be misleading  Cannot be aggregated  Regulatory reporting  Setting risk limits Key Use  Regulatory reporting  Economic risk capital  Key measure used for hedging risk  Setting risk limits Value at Risk (VaR) Measure Expected Shortfall (ES) Sensitivities
  6. 6. Regulatory Stress Tests (CCAR, EBA)  Regulators provide a varying degree of granularity in their market shock variables  Some of the key requirements in dealing with these stress tests from a market risk perspective include – Comprehensive understanding of risk drivers – Customized scenario expansion models to expand regulatory scenarios – Robust input-output templates to ensure quick turnarounds – Organizational focus and coordination across functions Real GDP Growth Unemployment Equity Market Index House Price Index Stressed:Macrovariables Econometric Modeling Credit Spreads IR Term Structures IV Term Structures/Skews Correlations Valuation Models Market Risk Modelling CCP Exposure Modelling 6
  7. 7.  By discounting cashflows at future dates Description  Closed-form pricing for low factor/low dimension models; path independent  Closed-form pricing for low factor/low dimension models; path dependent  IRS, Bonds, Floaters Type of Products  First generation structures  Caps/Floors, CBs, TS models  Simulation pricing for high factor/high dimension models; path dependent  Highly complex structures Modeling Challenges: Model Calibration 7 Discounting Technique PDE-based Lattice-based MC Simulation  FASB valuation categories are based on complexity – Level 1/2/3  Level 3 assets are priced using in-house valuation models  Models built can only be as good as the underlying data !
  8. 8. Modeling Challenges: Illiquidity Discount  Driven by stress testing requirements, initial steps are being taken towards better management of the market impact of illiquidity; measures include – Checking for variability in liquidation times for illiquid positions – Setting up processes for measuring impact of trade volumes on market prices – Monitoring widening bid-ask spreads, specially in times of adverse market conditions  CCPs started for most liquid OTC derivatives; liquidity adjustment challenges seen in more exotic products  Methods for incorporating the illiquidity discounts in OTC valuation models 8  Adjustment of bid-ask spread for specific market risk parameters  Determine lower and upper limit price by relying on reasonable limits to trade performance Bid-ask Spread Adjustment Bounding Approach
  9. 9. Computational Trade-offs 9  Choice between delta-based methods and the full revaluation method  Most banks still use sensitivity-based approaches due to computational challenges associated with the full-revaluation approach Newer technology (GPUs, multi-core processors) helping reduce computation times and trade-offs Sensitivity Based Approaches  Ease of implementation  Approximations (Taylor expansion/grids) Full Revaluation Approach  Robust  Computationally intense CONTROL AGILITY
  10. 10. Data Management Issues 10 Increased regulatory focus on providing granular and frequent analysis of real time data  Traditional Extract-Transform-Load (ETL) processes not amenable to real- time computation of risk. Alternatives may include: – ELT: Data transformation after the data load, includes data virtualization – Data Federation: Single virtual view without actually moving the data Typical Data Management Processes  Trade Data  Market Data Extract  Validate  Enrich Transform  Reports  Ad hoc Queries Load
  11. 11. Trends in Market Risk Systems 11 Increase ‘risk velocity’ and ‘risk management’ clock-speed  Automation of stress testing and reporting framework  GPU-based farms enabling move to full revaluation approach Measure  Cloud Computing, a natural solution for higher data capacity needed for granular regulatory reporting  Service-based architecture with detailed data captured at trade level  Flexible data capture and storage solutions Timeliness and Accuracy Data/Reporting Attribute Comprehensive ness Adaptability
  12. 12. Organizational Culture 12  Front office alignment – Financial incentives being aligned with risk-informed performance indicators – RWA consumption by trade becoming an important statistic  Model validation and model development functions being strengthened – Investments in internal/external resourcing  Robust middle office and IT processes becoming a differentiator – Cost of funding (CVA, FVA etc) getting incorporated – Granularity/Speed/Flexibility in risk calculations and reporting Organizational structure and incentives are getting better aligned to market risk management objectives Market risk management will need to be more tightly integrated with strategic objectives, diversity of business and level of complexity
  13. 13. www.crisil.com/gra

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