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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
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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
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)
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
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
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
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
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
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
2. A historical /skills perspective Constituents and their interaction
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)
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
3. An information systems perspective Information and Decision Technologies Business Intelligence: Competitive Advantage Middleware Decision Modelling Data Mining, KDD Middleware Analytic Database Production Database
EIS, OLAP Production Database Analytic Database Analysis & Synthesis Application of Models Data Information Knowledge Information & Knowledge: The Value Chain
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
OLAP and MultidimensionalViewing: Main features • Multidimensionality = Data
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
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
Event tree • Historical data 1978 – 1996 • 1 year horizon divided in 4 quarters
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)
Value at Risk • Finance industry has introduced Value at Risk (VAR) also known as the β-var. -fractile return r(x,y)
Implemented Solution VaR HN 131638 EV 82565 VaR Results
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
Stochastic Programming Supply Chain Model 1 Production (PR) Customer Zones (CZ) Distribution Centres (DC) Packing (PC)
Stochastic Programming Supply Chain Model 2
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…
Stochastic Programming Model and data instances Scenarios: 100
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
Data Mart Analytical Database Decision Database Information Systems…Data marts Portfolio Models Information Analysis Models Transactional Database
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
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
Supply Chain Cost ($) C Efficient Frontier B B1 A Customer Service measured in maximal delivery time (days) B2 1 2 4 3
Financial Risks • Markowitz (Nobel Prize) • Mean variance (M-V Theory) • Diversification through ‘not strongly correlated assets’
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
Portfolio Holdings data Aknowledgment to Alpha Strategies
Absolute Volatilities & Correlations Aknowledgment to Alpha Strategies
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
Contributions from Groups of Holdings • We can generalise these expressions from individual holdings to groups of holdings as follows :- Aknowledgment to Alpha Strategies
Marginal Contributions to Risk Aknowledgment to Alpha Strategies