EMERGING ARCHITECTURE OF TOOLS AND COMPONENTS FOR QUANTITATIVE MODELING AND DECISION SUPPORT

1. EMERGING ARCHITECTURE OF TOOLS AND COMPONENTS FOR QUANTITATIVE MODELING AND DECISION SUPPORT Presented To: “Landelijk Netwerk Mathematische Besliskunde” (LNMB) and the “Nederlands Genootschap voor Besliskunde” (NGB) 16 January 2003 Gautam Mitra CARISMA Department of Mathematical Sciences, Brunel University and OptiRisK Systems

2. EMERGING ARCHITECTURE OF TOOLS AND COMPONENTS FOR QUANTITATIVE MODELING AND DECISION SUPPORT Supported by researchers and colleagues including: E F D Ellison, C A Lucas, N Jobst, P Valente, N S Koutsoukis, C Poojari, B Dominguez-Ballesteros, T Kyriakis, A Mirhassani, G Birbilis Acknowledgement UK Research Council: EPSRC and UK Govt Industrial sponsors include: Fidelity Investments, APT Inc., UBS Warburg, Unilever Research, EU sponsored: OSP CRAFT , SCHUMANN project (Daimler Chrysler, Ford Spain, Yamanouchi BV, Iberinco, LCP)

3. Outline 1. Introduction and Background 2. A historical /skills perspective 3. An information systems perspective 4. Mix and Match Models 5. Illustrative Applications 6. DSS and IS Connections 7. A Web perspective 8. Discussions

4. Knowledge Systems Solution Systems Computational Optimisation Modelling LP Modelling Linear Programming Integer Programming Constraint Satisfaction IP Models Interior Point Method Preprocessing Sparse Simplex Algebraic LP Language Branch & Bound Polyhedral CP 1. Introduction and Background MPG to CARISMA Stochastic Programming Risk Decisions Parallel platforms Information Systems

5. 1. Introduction and Background Convergent activities/developments • CARISMA The Centre for the Analysis of Risk and Optimisation Modelling Applications • SPInE: Stochastic Programing Integrated Environment • BOOK: Interaction of information systems and decision technologies.by Nikitas S Koutsoukis and Gautam Mitra, Kluwer. • OSP-CRAFT and WEBOPT: Optimisation Services provision over the net

6. 1. Introduction and Background Mission of CARISMA The mission of CARISMA is to be a centre of excellence recognised for its research and scholarship in the following: • the analysis of risk, • optimisation modelling, • the combined paradigm of risk and return quantification. Industry Focus Finance Industry - Bank, Insurance, Pension Funds Large Corporates - FTSE 100, Multinationals, EUROTOP Public Sector/Utilities, Environment, Food, Agriculture, Health

7. 1. Introduction and Background The Faculty Director: Professor Gautam Mitra Deputy Director: Professor Christos Ioannidis Research Lecturers: Paresh Date, Fabio Spagnolo, Chandra Poojari Newly approved open positions :Research professor in Risk Modelling and Research Lecturer in Financial Risk Faculty members: 7 professors and 5 lecturers Research Associates : 4 Ph.D. Students: 16

8. Outline 1. Introduction and Background 2. A historical /skills perspective 3. An information systems perspective 4. Mix and Match Models 5. Illustrative Applications 6. DSS and IS Connections 7. A Web perspective 8. Discussions

9. 2. A historical /skills perspective

10. 2. A historical /skills perspective Constituents and their interaction

11. 2. A historical /skills perspective • Skills Requirement • Algorithm design and tuning • Software engineering and testing • Information engineering • Domain expertise • Financial engineering • Logistics and supply chain • Transportation planning and scheduling • Project development (solutions/applications) • Proof of concept  quick win  deployment • (System integrators)

12. Outline 1. Introduction and Background 2. A historical /skills perspective 3. An information systems perspective 4. Mix and Match Models 5. Illustrative Applications 6. DSS and IS Connections 7. A Web perspective 8. Discussions

13. 3. An information systems perspective Information and Decision Technologies Business Intelligence: Competitive Advantage Middleware Decision Modelling Data Mining, KDD Middleware Analytic Database Production Database

14. EIS, OLAP Production Database Analytic Database Analysis & Synthesis Application of Models Data Information Knowledge Information & Knowledge: The Value Chain

15. Datapreparation Data analysis & data mining Datastorage Deployment KNOWLEDGEWORKERS Description Summarization Pattern recognition Exception detection Segmentation Classification Profiling Scoring Forecasting Simulation Optimization MODEL BUILDERS INFORMATION CONSUMERS Datamart Desktopsoftware Extract Cleanse Impute Transform Calculate Enrich Manage Load Browser Web Server Datawarehouse Browser Browser Datamart Paper Reports Datasources Data collection software External data ERP systems Other transaction systems Functional department systems Legacy databases

16. OLAP and MultidimensionalViewing: Main features • Multidimensionality = Data

18. Outline 1. Introduction and Background 2. A historical /skills perspective 3. An information systems perspective 4. Mix and Match Models 5. Illustrative Applications 6. DSS and IS Connections 7. A Web perspective 8. Discussions

19. Modelling distribution of random variables Optimum Allocation Modelling - Scenario Analysis - Expected Value - Two Stage RP - Multistage RP - Chance Constrained Problems - Others Modelling SP STOCHASTIC PROGRAMMING MODELLING

20. Event tree • Historical data 1978 – 1996 • 1 year horizon divided in 4 quarters

21. Scenario Generation

22. Extended Syntax for AMLs • Consider SP models as refinement of deterministic problems by introduction of uncertainty • SP models identify: • An underlying deterministic model (core) • Information related to the randomness of the model (stochastic framework)

23. SP Modelling Constructs

24. Scenario Generation and SP Modelling

25. ALM model in SPInE: solution

26. Value at Risk • Finance industry has introduced Value at Risk (VAR) also known as the β-var. -fractile  return r(x,y)

27. VaR Computation

28. Implemented Solution VaR HN 131638 EV 82565 VaR Results

29. Outline 1. Introduction and Background 2. A historical /skills perspective 3. An information systems perspective 4. Mix and Match Models 5. Illustrative Applications 6. DSS and IS Connections 7. A Web perspective 8. Discussions

30. Supply Chain Application

31. Stochastic Programming Supply Chain Model 1 Production (PR) Customer Zones (CZ) Distribution Centres (DC) Packing (PC)

32. Stochastic Programming Supply Chain Model 2

33. Stochastic Programming • Stochastic Programming with recourse models are ideally suited .. two perspectives • (near) optimum resource allocation • hedge against uncertain future outcomes • Decisions not optimum for any one outcome, good for many outcomes ! • Two stage models • First Stage: ‘ Here-and-Now’ asset allocation decisions … takes into consideration scenarios(outcomes) • Second Stage: Recourse decisions optimal corrective actions as future unfolds…

34. Stochastic Programming Model and data instances Scenarios: 100

35. Stochastic Programming

36. Portfolio Application

37. Uncertainty… optimum decisions Modelling approach • Construct decision models which capture return and risk (due to uncertainty) • Combine models of optimum resource allocation and models of randomness

38. Data Mart Analytical Database Decision Database Information Systems…Data marts Portfolio Models Information Analysis Models Transactional Database

39. Information Analysis Models Pre-analysis Model Data Parameters Solution Analysis Post-analysis Performance Indicators Style Analysis Financial Ratios CAPM APT Simulation Models Internal Company Models Historical data Weighted Moving Average Factor Models Time Series Models ARCH, GARCH,… Neural Networks Genetic Algorithms Kalman Filters Chaos Internal Company Models What if Analysis Scenario Analysis Simulation Backtesting Internal Company Models Performance Indicators Risk Statistics and Indices Financial Ratios CAPM APT Simulation Models Risk Metrics Internal Company Models Information Systems…Datamarts

40. Production Database Market Data: Historical Prices Optimisation Engine Pre-Analytical Database Internal Data:Portfolios, Cashflows... User Input: Risk Aversion, Target Portfolio Return .. Model Data Parameters: Average Return Var/Cov Matrix ... Pre Analytics: Styles, Risk Statistics, Financial Ratios ... Portfolio Optimisation Model Continuous or Discrete Modelling System Decision Database Optimisation Results: Portfolio Returns, Potfolio Risk, Optimum Asset Mix Solver Post-Analytical Database Results Analytics: What if, Different objectives... Post Analytics: Backtesting, Risk Analysis... Information Systems…Data marts Data Mart Analytical Models Analytical Models

41. Model/Results Explanation

42. Supply Chain Cost ($) C Efficient Frontier B B1 A Customer Service measured in maximal delivery time (days) B2 1 2 4 3

43. Financial Risks • Markowitz (Nobel Prize) • Mean variance (M-V Theory) • Diversification through ‘not strongly correlated assets’

44. Outline 1. Introduction and Background 2. A historical /skills perspective 3. An information systems perspective 4. Mix and Match Models 5. Illustrative Applications 6. DSS and IS Connections 7. A Web perspective 8. Discussions

46. Portfolio Holdings data Aknowledgment to Alpha Strategies

47. Absolute Volatilities & Correlations Aknowledgment to Alpha Strategies

48. The Algebra of Risk Decomposition • We begin by breaking down the total variance of a portfolio into contributions from individual holdings • We have • From which we derive individual contributions to variance as Aknowledgment to Alpha Strategies

49. Contributions from Groups of Holdings • We can generalise these expressions from individual holdings to groups of holdings as follows :- Aknowledgment to Alpha Strategies

50. Marginal Contributions to Risk Aknowledgment to Alpha Strategies