Valuation, Model and Data Risk Management

Transcription

1 Only the curious will learn Valuation, Model and Data Risk Management GARP Caribbean Chapter Meeting, March 28 th 2008 By Philippe Carrel, Reuters, Executive Vice President

2 "It was the failure to properly price these risky assets that set off the tidal wave of risk contamination". Alan Greenspan, speaking at Reuters on October 1 st

3 Growth, competition lead to accelerated innovation The quest for alpha leads to innovation Hedge Funds / Prop trading develop absolute return strategies Cross-asset strategies Combination of loans, securities, OTC instruments,hybrids Assumptions, Correlations, Proxies, Models Risk increasingly managed from front office Fast growth, competitive markets, accelerated innovation Securitisation boom (US$1.2 tn in 2006) Liability driven structuring Assumptions become usual practice Liquidity produces liquidity 3

4 Of the dangers of uncontrolled automation Uncontrolled automation lead to systematic risk Program trading Automated arbitrage and order generation Implied volatilities and correlations Automated feed of reference data Loss of confidence lead to systemic risk Sub-Prime Credit crunch Risk is increasingly managed from front office, model driven, assumption based Catalysts invalidate assumptions in the short run which leads to panic 4

5 Greed, Fear and Regulations Regulators obsessive focus on risk Basle II Mifid/NMS Triggered massive transfers of assets to special purpose vehicles Freed capital UCITS III Multiplication of trading venues Data repositories, analytics platforms Generalise use of derivatives and OTC instruments Valuation-based compliance Innovation Competition SFAS 157 Defines fair value as sell value Establish 3-level hierarchy based on valuation or on valuations actually. 5

6 A new risk class Market Risk Credit Risk Operational Risks Data & Valuation Procedures 6

7 Data + Models + Processes + Control = Valuation risk For..we need to..the following risks : Data Acquisition Orders Static Data OTC Data Data Processing Data Distribution Execution Position/PL Analytics Pricing Valuations Contribution Identify Assess Mitigate Monitor Report Data Risk Valuation (Process) Risk Model Risk Sensitivities (Mkt/Crdt) Collateral Liquidity Reporting Portfolio Risk Performance Compliance Regulatory Audit control Reporting 7

8 Valuation risk Data Prices Reference data Models and hierarchy Formats / Taxonomy Which data for which purpose? Workflow analysis Processes How does the firm use data, rely on processes and assumptions? Compatibility of models and data, consistency in use Data quality management (rating on availability, reliability, updates) Data hierarchy (dependencies on imported, existing, calculated data) Process hierarchy (critical path, dependence on assumptions) Workflow analysis (potential domino effect) Vulnerabilities (latency, breakdown) Connectivity (Synchronicity, external communications) Organisation Sponsorship, hierarchy, culture Governance Dashboard / warnings, alerts Alternative procedures Has the firm established valuation risk as part of its risk culture? 8

9 Golden copies and set-in-stone analytics OTC Markets Exchanges Loans OTC Derivatives Securities Futures & Opt OMS Providers Execution Venues Brokers FX FX OTC deriv Futures Fix Inc CDS Fix Inc Deriv, Equ, CFDs, Cmdts Orders Position Analytics and Valuations Front Office Back Office Admin & Accntg Asset Mgt Adhoc Reporting for Communities Real-time export Custody 9

10 ..quickly reveal their own limitations. Cash / Fx / Fwd / Opt / MM / STIR Deriv / Bills / Credit / Fix Inc/ MBS / CDOs Futures / Fut Opt / Equities / CFDs / OTC Deriv Metals / Energy / Softs OTC Markets Exchanges Electronic Execution Market Makers Distributors OMS Providers Brokers Others Trade Entry Order Processing Trade allocation Trade blotter Exception Mgt Input verification Position Management Order Generation Collateral Management Position Record Position Analysis Drill-Down Ticket/Pricing/Order Collateralizing deals Collateral reports Inventory Analysis Funding Scenario Builder Parameters Instrument search Book Structure Amendment forms Instrument Requests New bond forms CA Event forms OTC inst forms MARKET DATA Data Management Derivatives Pricing Curves / Surfaces Instrument analytics Hedge effectiveness Notifications End of Day/Month Maturity Network Communications Export to Admin / PB Exception Mgt Option expiry Terminations Cash events Reporting Trading Reports Operational Reports 3rd party reports End-of-day Reports Compliance Reports Output RM 10 Trading Operations Communications Processes

11 Open models, dynamic, adaptable workflow Data Acquisition Data Processing Data Distribution Input & Visualisation Calibration Pricing Pre-Trade Analytics Position Analytics Portfolio Reports P/L & Risk Scenario-based analytics Limits & Compliance Margin & Collateral Back-Office Records History & Audit Input Services Issuer Data T&Cs Corporate actions Market Data Valuations Projections Interactive Platform Risk aggregation Performance Decomposition VaR & Stress Test Scenarios Portfolio modelling X-Asset Aggregation Yield Curves Vol Surfaces Correlations Ratings Interactive Services Communication Services External connections Risk & Compliance Reports 11

12 ..dynamic adaptable processes, Workflow and hierarchy Contingencies in processes Data dependencies Defining Key Risk Factors (feed, curves, surfaces, matrices, diffusion model) for concentrations Scenario based risk analysis Sensitivity to chosen factors, observable input, economic factors Interdependence of risk factors (volatility, spreads, correlations) Impact on collateral, covenants (rule-based) Multiple model approach (highly recommended by most professional associations) Volume processing, scalability Connectivity, external contingencies Triggers and alerts Establish triggers (intra-day vol, price gaps, default event) Measure impact immediately Alternative procedures Alternative procedures Swappable, adjustable analytics Alternative data sets Alternative processes Position adjustments 12

13 ..interactive data models will make the firm more agile. Insights Huge volumes to be analysed and visualised almost instantly Multiple views, interactive queries, filtering Multiple data sources aggregated an reconciled automatically Multiple pricing models On-demand variables and risk factors aggregation Permanent compliance check against investment policies, risk limits Open Model Approach Multiple (alternative) models Scenario-based, cross-asset Combining the management of data, models and processes into a set of adaptable and interchangeable procedures for pricing, valuing and reporting, P/L, exposure and performance. How agile a firm can be in adapting to a new business environment. 13

14 Organisation and corporate culture Structure Risk culture acknowledging Valuation Risk as a standalone risk to be managed Risk committee Hierarchy, Empowerment Independence Policy/Strategy Governance and acountability (process owners) Taxonomy Hierarchy, Empowerment Audit and control Openess Disclosure of data, results, methodologies Democratised access to data (user interface) 14

15 As far as mathematics refer to reality they are not certain, as far as they are certain they do not refer to reality Albert Einstein Valuation, Model and Data Risk Management GARP Caribbean Chapter Meeting, March 28 th 2008 By Philippe Carrel, Reuters, Executive Vice President