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
1. Introduction and Background
MPG to CARISMA
Stochastic Programming
Risk Decisions
Information Systems
Parallel platforms
5
Convergent activities/developments
1. Introduction and Background

• 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
Mission of CARISMA
1. Introduction and Background

• 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
Constituents and their interaction
2. A historical /skills perspective
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
Information and Decision Technologies
3. An information systems perspective
Business Intelligence Competitive Advantage
Middleware
Decision Modelling
Data Mining, KDD
Middleware
Analytic Database
Production Database
14
Information Knowledge The Value Chain
15
Datasources
Data collection software
External data
ERP systems
Other transaction systems
Functional department systems
Legacy databases
16
OLAP and MultidimensionalViewing Main features

• Multidimensionality Data

17
(No Transcript)
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 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 ß-var.

?-fractile
?
return r(x,y)
27
VaR Computation
28
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
Supply Chain Model 1
Stochastic Programming
Production (PR)
Customer Zones (CZ)
Distribution Centres (DC)
Packing (PC)
32
Supply Chain Model 2
Stochastic Programming
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
Modelling approach
Uncertainty optimum decisions

• Construct decision models which capture return
  and risk (due to uncertainty)
• Combine models of optimum resource allocation and
  models of randomness

38
Information SystemsData marts
Information Analysis Models
Portfolio Models
Transactional Database
39
Information SystemsDatamarts
40
Information SystemsData 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
•  

45
(No Transcript)
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
51
Summary of Absolute Decomposition
Aknowledgment to Alpha Strategies
52
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
•  

53
Traditional Optimisation-based DSS

• Traditionally, optimisation applications comprise
  models, the optimiser, and a data mart, connected
  via a model management system.

Models
Typical Optimisation Decision Support System
Computation Algorithms Solutions
End-User Application
Modelling System
Data Mart
54
A typical Optimisation Solution
Training
Consultancy
R D
Models
Analyst Interaction
Computation Algorithmic Solutions
End-User Application
Modelling System
End-User Interaction with System
Data Mart
Specialist Interaction
55
Access to Tools
Internet, WAN, Other NET
Modelling Systems
Computation Tools
Models
End User
End User
Data Mart Technology
56
Access to Vertical Solutions/DSS
Models
Computation Algorithmic Solutions
Internet, WAN, Other NET
End-User Application
Modelling System
Data Mart
DSS 1
End User
Models
Computation Algorithmic Solutions
End User
End-User Application
Modelling System
Data Mart
DSS 2
57
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
•  

58
Discussion 1

• OR software tools and components are developed to
  
• Respond to business needs
• Incorporate current technology platforms
• Different skill sets are required to bring
  together technology solutions
• New developments in
• Risk modelling and risk management bring
  simulationa nd optimisation closer
• Role of Model explanation
• Web is the preferred / chosen delivery platform

59
Discussion 2
60
Discussion 2
61
Thank You for your attention any questions ? I
would appreciate your feedbackcomments
www.carisma.brunel.ac.uk www.optirisk-systems.com