Grid-Enabling the ESG Akash Chopra

1. B+H Economic Scenario Generator Grid-Enabling the ESGAkash Chopra

2. Introduction to Barrie + Hibbert • Founded in 1995 • Industry leader in modelling financial market risk • Economic Scenario Generator • Annuity and DB Pension Asset-Liability models • 60 staff based in Edinburgh and London • Actuaries, economists, physicists, software developers • Grown from UK Life provider… (2004) • 70+% of UK Life Groups • …to European Life Group provider… (2005) • Majority of European CRO forum • …to the leading global provider (2006+) • North America, Asia, Australia, South Africa, global consulting firms

3. What is the ESG? • Multi currency stochastic asset model • In English please… • Monte-Carlo simulator • Modelling of economic variables and asset prices/returns • Calculates the distribution of variables into the future • Interest Rates • Inflation • Exchange Rates • GDP • Equities • Bonds

4. Why do our clients want the ESG? • Require sophisticated modelling of risk – need accurate pricing for assets/liabilities • Regulation - FSA • Awareness • Reporting • Quarterly reports to regulators, market • Auditors • Justification of modelling assumptions • Academically rigorous models

5. One cog in a complex machine Portfolio Data Market Data Calibration ESG Accounts Liability Model Reports

6. Typical ESG Simulation • No such thing! • Simple model • Single economy • A few assets (equities, bonds) • 30 year horizon in annual steps • 1000 trials • 1-2 hour run time • Complex model • 10 economies • Hundreds of assets • 60 year horizon in monthly steps • 10000 trials • Potentially a very long run time! In practice, 24-48 hours.

7. Typical ESG Usage • Life clients • Need to produce quarterly figures to satisfy regulators and market • Figures need to be produced in short timescales (days) • Stale data is a problem • Need to use latest market data • Timely reports indicative of good management • Preparation is key • Building up results incrementally as data becomes available

8. Why HTC? • Reducing sampling error • Increase number of trials - sampling error reduces slowly • Many different “what if?” scenarios are run • Interest rate risk • Credit risk • …up to 75 different scenarios in some cases • Human error! • Garbage in, garbage out • Re-running scenarios • Makes more sophisticated analysis possible

9. Outer Scenarios Inner Scenarios Nested simulation • “Nested simulation” or “stochastic on stochastic” involves generating a set of inner scenarios within a set of outer scenarios e.g.: • Why? • Estimate liability values at times other than t = 0 • Challenges: • In principle requires huge number of scenarios ~ 10002 = 1,000,000 • Least Squares Monte Carlo

10. Which HTC Framework? • No desire to write our own • Mature products already exist • Let’s not re-invent the wheel • Ease of integration • High level of documentation and support • Must fit into our application, not the other way round • ESG is a .NET application, so being able to avoid COM would be a bonus • Digipede fits the bill • Excellent introductory material (examples, tutorials) and email/phone support • Very little work required to get a basic implementation up and running • Several “grid design patterns” provided as standard • Flexible enough for us to tweak these to suit our purposes • .NET application – straightforward API usage

11. Problem Decomposition • Monte-Carlo is ideally suited to HTC • Digipede’s terminology • A job consists of many tasks • Each task is independent • Barrie + Hibbert terminology • A simulation consists of many trials • Each trial is independent • Does 1 trial = 1 task? • Depends on computational effort involved in 1 trial… • …which depends on the model complexity • User can choose “unit of decomposition”

12. Data Management • Setup • Model needs to be sent to each agent on the grid once per job • Modified existing Digipede pattern (Executive Worker) to accomplish this • Tasks • Require very little input data • Potentially generate large amounts of output data • Bypassed Digipede return mechanism for performance reasons

13. The User Doesn’t Want To Know

14. Client Requirements for HTC • Now • Order of magnitude improvement in performance • Quicker production of quarterly figures • Future • Reduced sampling error • 100 times the number of trials? • Complex liability pricing at t ≠ 0 • Nested simulation • Anywhere from 2 – 1000 times the number of trials • More frequent scenario generation • Weekly runs • Daily runs?

15. Our Requirements • Make life easy for our clients • “I need the results ASAP” • Dynamically adapt task size to match simulation/machine characteristics • “I need the results in X hours” • Use minimum amount of resource necessary to accomplish job within deadline • “Do not load any machine too heavily” • Desktop scavenging