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Abstracts

Common Mathematical Mistakes in Quantitative Modeling

Dr Paul Wilmott

Quantitative finance models are becoming increasingly sophisticated and quants are taking on responsibilities for larger and larger decisions. Is this a safe and stable scenario? The speaker will argue that some quants' lack of real-world experience and blind belief in mathematical modelling is a dangerous combination. He will outline some of the common mistakes that quants keep making and show how these can be rectified by using relatively simple mathematics.

  a. Jensen's inequality arbitrage
  b. Sensitivity to parameters
  c. Correlation
  d. Lack of diversification/size of trades
  e. Reliance on dynamic hedging (arguments), risk neutral versus real
  f. Feedback
  g. Too much complexity
  h. Calibration
  i. Supply and demand
  j. Valuation is not linear

 

Overview of @RISK 5.0 and DecisionTools Suite 5.0

Sam McLafferty

Palisade Corporation

Sam will give a guided tour of the stunning new DecisionTools Suite 5.0 for Microsoft Excel.  The DecisionTools Suite has been rewritten and expanded, adding new products to provide the most comprehensive set of quantitative tools available anywhere.  All component products offer streamlined interfaces, robust reporting, new features and tighter integration with Excel and each other.  The result is a powerful, cohesive package that is more than the sum of its parts.

The Suite incorporates flagship @RISK 5.0 for risk analysis using Monte Carlo simulation, and it also adds new versions of PrecisionTree and TopRank.  PrecisionTree provides decision analysis with decision trees, while TopRank performs fast, convenient "what-if" sensitivity analysis.  Furthermore, the data analysis software NeuralTools and StatTools have been added.  NeuralTools performs predictive analysis using neural networks, while StatTools provides time-series forecasting and a wide range of other statistical functions.  Rounding out the new Suite is Evolver 5.0, the genetic algorithm optimization tool.  Sam will touch on the most important new aspects of the DecisionTools Suite products, and answer any questions you may have.

 

From Geological Knowledge to Good Decisions
using @RISK models, A North Sea Case Study

Knut Hollund
StatoilHydro

Industry Focus: Oil and Gas
Product Focus: @RISK

Some of the steps in the decision process for an on-going off-shore field development are presented. The challenge was to turn relatively small geological structures consisting of both unproven prospects and proven oil and gas reserves into a field development with good economic performance.

An offshore field development typically involves a huge investment, and it is no surprise that oil companies take several months to develop geophysical interpretations, geological models, flow simulation models and economical models in order to support decisions properly. This workflow may lead to one good estimate, but may fail to describe the upside potential and risk involved. A simple @RISK model was built specifically to support decisions in the early phases of the field development.

The model includes a tool for displaying important upside potential seen in geological and geophysical evaluations, and a parametric model for the oil and gas production resulting in a “fast model” that could be applied as part of a @RISK simulation handling all essential elements in the cash flow for various field development concepts. One of the challenges was to make a model that realistically accounted for the information we knew would appear before production start. This was solved using optimization inside the Monte Carlo simulation.

The model made it possible to explore important upside potential seen in geophysical evaluations, and answer if and in what sequence further wells should be drilled. An improved understanding of the value for different drilling strategies was gained by studying distributions for in-place oil and gas volumes for various scenarios. The model would quickly give comparable economic figures for various development concepts. The upside potential and risk associated with the development could also be studied. Another important property of the model was the ability to incorporate the results of the appraisal wells and narrow the uncertainty range in the modeling as soon as the information became available.

 

Economic Capital Modelling for Operational Risk

Dr Raj Nataraja
Chase Cooper

The Basel II Accord requires larger financial institutions to use the advanced measurement approach to derive their regulatory capital for operational risk.  This capital is the reserve that the banks should hold in addition to those for market and credit risk. 

The operational risk capital should take account of internal loss data, external loss data, risk and control assessment data and scenarios.  Further, the frequency of loss events and the severity of losses have to be modelled probabilistically.  In addition, the capital should be computed across several business lines and loss event types that may include different levels of granularity and correlations.  Integration of all these elements together with some business specific qualitative adjustments that is logical, consistent, and believable is far from simple.

With its extensive library of distribution functions, efficient Monte Carlo simulation engine, reporting facilities, GUI, and interaction with VBA, @RISK for Excel provides a model development and validation platform that is flexible for research studies and robust for full-fledged business applications.  The present research and consultancy application, originally developed in @RISK4.5 has been migrated to @RISK 5.0 to take advantage of several of the new functionalities including those specific to the financial industry, and those for visualising and validating models.

The Economic Capital Model for a generic case study will be demonstrated as part of the presentation, illustrating that @RISK 5.0 is a development tool of choice for probabilistic modelling of financial applications.

 

Brand Risk – Towards a Meeting of
Professional Minds

David Abrahams
Brand Mediation

» Download presentation

Brands are now widely acknowledged as vital contributors to value creation.  Yet if their form and function are to be adequately reflected in models of business risk, brands need to be understood as more than trademarks and ‘reputations’.  Decision analysts can usefully apply a more precise anatomy of the brand in their capacity as advisers and facilitators.  Meanwhile, there is scope for marketing professionals to develop their familiarity with decision trees and Monte Carlo simulation, both in brand planning and in their advocacy of particular policies. 

This presentation will consider the reasons for an increasing interest in structured assessments of brand risk.  It will offer evaluative frameworks designed to establish common ground between decision analysts and marketing decision-makers.  It will explore the role of intermediate methods in promoting the value of stochastic modelling to non-specialists who hold marketing responsibilities. 

 

Using @RISK for Traffic Forecast Analysis

Inęs Teles and Fátima Santos
TiS.pt

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The risk analysis of road infrastructure traffic demand studies is traditionally supported by estimated demand values within three scenarios - pessimistic, reference, and optimistic – allowing for a very limited financing risk deterministic analysis.

In this presentation the developed approach draws on @RISK software and a traffic assignment model (supported by PTV VISUM software) which makes setting up financing risk analysis in stochastic terms possible, and allows one to benefit from the results coming from @RISK software.  In this way, we improve the quality of decision-making by representing traffic demand results in a probabilities distribution histogram, because it allows us to determine the confidence interval for the mean value of traffic demand (or revenue), and identify the most influential input variables for traffic demand through tornado graphics interpretation.

 

Post Denmark Uses @RISK
to Reduce Insurance Premiums

Frank Lyhne Hansen
PricewaterhouseCoopers

In order to proactively manage the risk in its everyday business, Post Denmark held a risk mapping workshop following its ownership change in 2005.  The event, attended by the risk officers from each of the organisation's business units, identified risks facing Post Denmark, rated the risks in terms of likelihood of occurring and the severity of the consequences should they arise, as well as the degree of control available to prevent them taking place, and then outlined activities to reduce their impact, should they occur.

Post Denmark pinpointed risks for which there was a direct correlation to its insurance premium, such as anything connected with its property and automotive fleet, as well as industrial accidents, business liability, post office robberies, theft (both internal and external), and fraud.  The organisation also defined the risk of competition from private couriers, and risk from a sharp downturn in business to business mailas a result of the increased use of digital communication.

@RISK gives data value
PriceWaterhouseCoopers (PwC), in its capacity as risk analysis consultancy to Post Denmark, recommended @RISK software from Palisade and developed a risk analysis model that used past events to predict the likelihood of future ones.  Frank Lyhne Hansen, PwC consultant, explains:  “Post Denmark had been meticulous in collecting data relating to every eventuality against which it must insure itself.  @RISK extracts true value from this information because it quantifies the risk faced by the organisation in a measurable format.  The technical sophistication of @RISK enables models to be built that reflect complex scenarios and are straightforward for users to deploy and interpret.”

@RISK predicts chance of accidents
Data for all past occurrences - dating back to 1996, were input into an @RISK model.  The model calculated the likelihood of these events happening, as well as their severity.  Ulrik Mester, of Post Denmark comments:  “Because we record every accident that has happened, in the past we have been fairly confident that the insurance premiums we had been paying were right.  However, we wanted to move this up a level and be absolutely certain that we were investing our funds in the most cost effective way for the organisation.  @RISK has enabled us to do this.”

@RISK's accuracy reduces insurance premiums
@RISK gives Post Denmark a clear understanding of the chance of a vast range of accidents happening.  As a result it has been more willing to underwrite more of its own risk which means lower insurance premiums.

Mester explains: “Previously, we were not comfortable taking on more risk as an organisation because we could not be certain how likely it was that a potentially costly incident would occur.  We therefore always had to insure against the worst-case scenario.  That has now changed – the combination of our data and @RISK's technology has given us the confidence to predict exactly what risks we face, as a result of which we do not need to pay so much for our insurance.”

 

Successfully Valuing Storage: A Real Options Approach to the Valuation of Real Assets

Dr Jennifer I Considine
University of Dundee

» Download presentation

This presentation is concerned with the role of strategic planning in the optimisation of real assets in the energy industry.  Specifically, a real options approach using @RISK Monte Carlo simulation methods when used as part of Real Options Valuation strategies.  The approach is used to evaluate and value storage facilities in the natural gas industry.  Beginning with an overview of standard storage valuation techniques, the paper moves to a real options approach, and an indepth look at dynamic heading strategies, such as back to back call and put options.

 

What’s In a Decision?

Sven Roden
Decision Analysis Group, Unilever

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This is an interactive group decision-making game to show the fundamental principles of decision-making and decision analysis.  This exercise will illustrate how seemingly simple decisions can become complex.  A simple analysis of the problem will show how to make the best decisions in the face of uncertainty whilst gaining commitment to action.   This is a fun session, but involves a real investment decision.  You may be advised to bring some money!

 

Risk Aggregation: Calculating the Cash Flow
@ Risk from Sub-Segments to Segments Levels
at ArcelorMittal

Douglas Cardoso
ArcelorMittal

» Download presentation

The presentation is about how to use a bottom-up risk assessment approach to obtain the Top Risks of the Group.  Based on data collect from 25 sub-segments, by a Risk Reporting Bundle, the risks were aggregated into 6 segments using @RISK to calculate the total Cash Flow at Risk.  In a second step, these risks were aggregated to get the Top 10 Risks in the Group Level.  It used probabilistic distribution on the financial impact (I) and on the likelihood (P) of each risk.  The final Cash Flow at Risk was the sum of the different I x P.  For each type of Risk (Risk Domain) a Latin-hyper cube simulation was run, to obtain the range of possible cash flow at risk in MUSD.

 

So You Think You Have the Right Data?

Andrea Dickens
Decision Analysis Group, Unilever

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Have you often wondered how good your data is?  Collecting data about future uncertainties from experts has a number of hidden traps.  In this interactive session we will make you aware of some of the most common sources of bias, and suggest ways to overcome them.

 

Delivering Client Value through
Uncertainty Management

Tim Wells BSc. 
Maritime Business Group, Halcrow Group Ltd

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Adding client value is increasing more than simply ‘providing the deliverable’.  In the face of significant uncertainty clients needs to be informed on the robustness of future investment decisions.  The application of @RISK to develop project risk budgets at contract stage together with ongoing review and monitoring, allows project partners to be aware of their ongoing risk exposure and informs project management decisions. 

The following paper explores the application of @RISK in a project risk management context, and also introduces additional applications where probabilistic analysis has helped clients appreciate the role and importance of uncertainty in decision making. 

Using Risk Analysis, Aided by @RISK, On a Water Supply System to Evaluate an Energy Cost Saving Project, at Águas do Douro e Paiva SA

Jaime Gabriel Silva
Águas do Douro e Paiva S.A.

This presentation is a risk analysis case study of an energy cost reduction project especially useful for companies managing a water treatment and supply system.  It highlights the advantages and difficulties observed in the model development, as well as the variability in its parameters, having in mind the exchange of information and practices between different companies.  The risk analysis helped to sort out the inputs to improve cost estimates prior to the project’s kick off.

AdDP faces increasing energy costs and management of the impact on its activity.  The company started several actions to minimise costs, mainly oriented to pump efficiency improvement, and to pumping stations functioning hours.  While the first group of measures was developed, focused on the system’s management and energy efficiency, the main electricity consumptions were evaluated and a second costs saving group of actions was identified.  These actions involved tariff reductions on the largest pumping stations, and pump efficiency corrections on the smaller ones. 

AdDP’s Engineering Department decided to develop a project evaluation aided by decision analysis tools.  A model was built, taking into account all important input variables of the real problem using @RISK.  In fact, the significant number of variables with high uncertainty level and the author’s previous experience with @RISK led him to prefer an immediate approach based on a simulation model, with the main inputs described by continuous probability distributions, based on the data available for each variable.  Therefore, this @RISK model was used to evaluate the investment’s viability, within the enterprise’s concession life time.  The analysis presented here is supported by cost estimates, both for the construction and for the new infrastructures maintenance, as well as on estimates for the future evolution of electricity tariff and supplying volumes.  The decision analysis is based on a discounted cash-flow model that estimates costs and benefits through stochastic simulation and allows the analyst to estimate the project’s Net Present Value and to approach its probability distribution.

Águas do Douro e Paiva, SA (AdDP) is a water supply company, serving the Oporto region for 30 years, and is now beginning a wastewater system, on a similar basis. 

Presenting Large Scale Forecast
Results in an Intuitive and Informative
way: A Tobacco Industry Case Study

David Edison
Senior Consultant
Forecasting

Industry Focus: Manufacturing/Tobacco
Product Focus: @RISK for Excel, StatTools

» Download presentation

THE PROJECT: To produce a generic stochastic model making automated daily, monthly and annual sales forecasts for all tobacco products across a range of European regions, allowing users to apply subjectivity at any level to the forecasts being made.

THE SYSTEM: An Excel based model using both @RISK and StatTools, deemed to be the optimal solution following a pilot study. The core model involves a regression analysis of sales volumes against a range of historic variables, followed by stochastic sampling of future values of those variables, resultant sales and error terms, as well as incorporating subjective user assumptions. The model automatically sources its 17,000 separate sets of data (product / geography combinations) from ‘cubes’ and the data warehouse, and forecast statistics are returned to cubes for ease of use. Cubes containing forecast data at every percentile of confidence hold approximately 100 million data points but are extremely quick and simple to use and interrogate.

THE RESULTS AND BENEFIT: The client has a proven core generic forecasting model which can be used for ad hoc modeling and experimentation, but which also sits at the heart of a fully automated and integrated forecasting system. The whole system runs automatically on a monthly basis, and the user has the ability to make any subjective adjustments within the system in a simple way at any product level, at any geographical level, for any time period and at any confidence level. The end results of the automated forecasts are pre-defined summary exhibits, along with Business Intelligence ‘briefing books’ - a powerful way of allowing users to have access to all forecast statistics in pre-defined but flexible views, where the click of the mouse allows the same view to be seen for a different dataset, or broken down or analyzed in a different way, instantaneously. No more need for a hundred spreadsheets, and no more bulging ring binders!

Maximising Net Present Value of investment
in maintenance of assets

Ujjwal Bharadwaj
TWI Ltd
Cambridge/ Loughborough University

» Download presentation

The presentation describes and demonstrates a methodology to maximise the financial benefit of investment in asset maintenance. The risk based methodology is demonstrated using a spreadsheet model that uses @RISK for Monte Carlo Simulations (MCS).

Infrastructure managers are under increasing pressure to minimise life cycle costs whilst maintaining reliability or availability targets, and to operate within safety and environmental regulation. This paper presents a risk based decision-making methodology for undertaking run-repair-replace decisions with the ultimate aim of maximising the Net Present Value (NPV) of the investment on such maintenance. This methodology is based on established engineering, financial and statistical techniques that are in practice in power plant management. In this paper, the infrastructure system under consideration is assumed to consist of a number of structural components.

To demonstrate this approach, in the first instance, a qualitative risk analysis is conducted to highlight those components that are ‘high risk’. This enables operators to get an overview risk profile of their system and thereby focus resources on the more risky system components (structures). To analyze these risky structures, a quantitative risk analysis is performed on each of them.  For simplicity, corrosion has been considered as the main damage mechanism affecting the structures. A basic probabilistic model using the MCS technique is developed to obtain remaining life (RL) estimates of the structure under consideration. The RL estimates are then fed into another model – the Cost Risk Optimization (CRO) – model that weighs the risk of the structure being out of service (measured in monetary units as the product of the probability of being out of service and the cost of its consequences) with the cost of risk mitigation by undertaking some action – repair or replacement. Using this risk based approach, the CRO model gives the optimum time of action such that financial benefit is maximized. NPV is used to assess the value of an action so that time value of money is taken into account. In addition, the model can take into account tax credits accruing due to the depreciation of the structure (if applicable).

Optimising Procurement of a
Geothermal Power Plant using PrecisionTree

Viktor Thorisson
Enex

» Download presentation

This paper deals with determining the optimal decision path regarding procurement of a geothermal power plant. The aim is to determine which of the following has the highest expected profitability; ordering components on the crucial path (the genset) before first drilling, after first successful drilling or after second successful drilling.

Basic geological estimations of probability of success (temperature and production
index) are obtained from geology consultancy.

Those estimates are fitted to distributions, simulation performed to estimate probability of failure (defined as minimrequired return on equity), as well as to determine expected values if success. Those results are used as input values to the decision tree with expected trade offs such as extra investment cost and expected lost production due to less efficiency when components are pre-ordered. The result from this analyze, i.e. that the components should be ordered before first drilling, is quite profound. All input variables need to change dramatically to affect theoptimal decision path. If production can start sooner adding income early in time to thecash flow it dramatically over influence all risk and expected cost due to pre-ordering of turbine analyzed herein.

The Newsboy method is used to determine optimal plant size if pre-ordered, the
programs @risk and precision-tree are used for simulations, decision tree and
sensitivity analysis.

Artificial Neural Networks in Pharmacy and Medicine

Dr Loai Saadah
Tawam Hospital

Purpose: PalivizumabTM is the first humanized monoclonal antibody used to prevent an infectious disease, namely Respiratory Syncytial Virus (RSV). It is used in premature infants, born at less than 35 weeks gestation, to reduce hospital admission in addition to alleviating severe symptoms requiring intensive medical therapy. However, there is little evidence of the overall benefit in the neonatal intensive setting during a RSV outbreak. In this study, we used artificial neural networks to build models that can identify babies who might benefit from PalivizumabTM in this setting.

Methods: We retrieved and documented demographics as well as other relevant clinical information for patients from four different RSV seasons in our neonatal intensive care unit. We included a total of fourteen input nodes; seven categorical (gender, Palivizumab Group, whether Palivizumab was given at any time during the outbreak, season, congenital heart disease (CHD), chronic lung disease (CLD), and RSV) in addition to seven numerical (gestational age, birth weight, age at the start of the season, apgar scores at 1, 5, and 10 minutes, and length of stay before the index case was identified). We used Neural Tools v1.0.1 (Palisade, UK) to train three probabilistic nets (three outcome variables; days of supplemental oxygen, length of stay after index case was identified, and survival) in batch with a preset error limit of 0.01% and self-generated learning rate. For the survival outcome, we used the prevalence of mortality in our patients, together with the model sensitivity and specificity, to calculate positive and negative predictive values. We also conducted a treatment-group, reassignment analysis to study the effect of use of Palivizumab on the three outcomes investigated.

Results: Information from a total of 177 (39 Palivizumab, and 138 control) patients was used in the model. Twenty one different cases, seven for each outcome variable, were used for prediction. Of the remaining cases in each model, eighty percent were always used for training, and twenty percent were used for validation. All nets converged in less than 4 seconds and 400 hundred training cycles. Palivizumab did not improve survival in this model. When all the prediction cases are counter assigned to the alternative group,  patients who were assigned to Palivizumab stayed either more or less days on supplemental oxygen (- 0.5 to 8.0 days). Moreover, hospital stay was either shortened or prolonged with Palivizumab (- 22.0 to 1.0 days).

Conclusion: Although Palivizumab did not improve survival in our model, it does seem to offer significant advantage, i.e. reduction in length of hospital stay, in selected premature infants during the RSV nosocomial outbreak. Identification of these patients will most likely need a combination of complex artificial intelligence modeling techniques together with competent clinical judgment. We suggest that utilization of trained artificial neural networks can significantly improve the cost-effective use of Palivizumab in this setting.

A Generalised Model for Valuing Early Stage Technology

Michael Brand
Captum Capital

» Download presentation

Investment decisions in R&D, early stage technology companies, and the licensing or sale of technology require a quantitative value be assigned to the technology. This is usually a difficult management problem because:

  • Decisions can be made at several milestones to abandon, delay, escalate or otherwise change the technology development process
  • Projected sales of the technology product may be quite far in the future and are subject to uncertainty around market size and growth, as well as competition

The uncertainty surrounding the development process and subsequent product sales contribute to the difficulty is assigning a value to the technology at early stages of its development. The problem is particularly acute for biopharmaceutical products which may cost up to $1 bil to develop and less than 1 in 5000 candidate drugs reach the market.

This presentation will describe a generalised model for valuing early stage technology using an
@RISK model. The model combines:

  • Binomial functions to choose between development stage options, using user-defined probability Estimates
  • Growth models for product sales using probability distribution functions for uncertain parameters

By combining these two @RISK features in one model an expected Net Present Value can be
estimated for early stage technologies; currently eNPV is the most widely used valuation method for pre-market technology. The model allows the effects of user defined probabilities on eNPV to be evaluated. It represents a more intuitive approach to valuation than Real Option valuation methods which require a non-intuitive estimate of volatility.

Overvaluing technology at the early stages of development is a frequent problem, particularly with university spin-out technology, which can result in valuation problems for subsequent, later rounds of investment. The model presented serves as an effective communication tool demonstrating the impact of uncertainty on technology value at its early stages of development.

Design and Evaluation of a New Revenue Insurance Product for Strawberry Producers in Huelva, Spain

Salomon Aguado-Manzanares
Technical University of Madrid
Dept. of Economics and Agricultural Social Sciences

Spain is the world's third largest producer and major exporter of strawberries. Production is primarily concentrated within a single zone, which has made Huelva the most important strawberry-producing region in the world. At present, risk-management instruments in this sector do not offer strawberry farmers an adequate degree of satisfaction, and they are demanding an insurance policy that will offer them full cover for the risks that they run. The objective of the study presented here was to develop a global income insurance product that gathers together all the risks borne by the strawberry sector, responding sufficiently to the accidents and other sources of loss that affect this industry. The insurance product, whose design is based on the information supplied by companies that are responsible for 44 percent of production in the province, copies the income model of each individual company, and by means of a Monte Carlo simulation determines the pure premium level appropriate for different insurance strategies.

 

Introduction to the DecisionTools Suite 5.0


Erik Westwig
Palisade Corporation

This session will show you how to use the elements of the new DecisionTools 5.0 Suite as a comprehensive risk analysis, optimisation, and statistical analysis toolkit. Each of the products in the suite, @RISK, RISKOptimizer, Evolver, PrecisionTree, TopRank, StatTools, and NeuralTools, will be presented, showing how they can be used to solve practical problems in the real-world.

Introduction to NeuralTools and StatTools


Dr Mirek Janusz
Palisade Corporation

In this session we will learn how to use Palisade’s two data analysis tools: NeuralTools and StatTools.

NeuralTools imitates brain functions in order to “learn” the structure of your data. Once NeuralTools understands the data, it can take new inputs and make intelligent predictions. The new predictions are based on the patterns in known data, and offer uncanny accuracy. NeuralTools can automatically update predictions when input data changes, and it can even be combined with Palisade’s Evolver or Excel’s Solver to optimize tough decisions and achieve desired goals.

StatTools is a Microsoft Excel statistics add-in. This session will cover how to perform the most common statistical tests, and will include topics such as: Statistical Inference, Forecasting, Data Management, Summary Analyses, and Regression Analysis.

Selecting the Right Distribution


Dr Michael Rees
Palisade Corporation

This session covers the choice of the appropriate distribution in @RISK. A variety of approaches are presented and compared, including pragmatic, theoretical and data-driven methods. The use of distributions to treat a variety of risk modelling situations is discussed.

Oil and Gas Applications using @RISK and PrecisionTree


Dr Michael Rees
Palisade Corporation

This session discusses basic oil and gas modelling applications using @RISK and PrecisionTree. Models in the areas of reserves estimation, portfolio modelling, production-decline modelling and drill testing will be covered.

Introduction to Risk Analysis with @RISK 5.0


Manuel Carmona
Palisade Corporation

This hands-on introduction will briefly recap the main benefits and uses of risk analysis before walking you through key new features in @RISK 5.0. You will experience the all-new interface as you define distributions, compare distributions using overlays, fit distributions to data, and correlate input distributions. Review and edit your entire model in the new @RISK Model window. Swap distributions out for non-@RISK users using the new Function Swap feature, edit your model, and swap them back in again. Simulate in the new Demo Mode and watch all charts, thumbnails, and reports update in real time. View results using the new graphing engine, Scatter Plots, and Tornado Regression – Mapped Value charts. There is so much to see, we’ll cover as much as time permits.

PrecisionTree 5.0 as part of DecisionTools Suite 5.0


Erik Westwig
Palisade Corporation

This presentation combines an introduction of the enhanced user interface, tighter Excel integration, and new features of PrecisionTree 5.0, with demonstrations of how PrecisionTree can be used to analyse various problems in decision analysis.

 

Meet Palisade Developers


Palisade's developers have seen @RISK and other Palisade tools applied in dozens of different industries. Join us for this exciting roundtable discussion, where you’ll be able to provide feedback about Palisade tools and seek advice for your particular modeling issues. So bring your spreadsheet models and your software wish list while you get to know the people behind @RISK, the DecisionTools Suite, and more.

Getting More from @RISK for Free

Manuel Carmona
Director of Sales
Palisade Europe

Problem: Many customers  simply don’t know about the many places where they can get answers to their most common @RISK questions, as well as, good reference models and samples to look at.
 
Subject: There’s a lot of information available which is FREE. This presentation is about the many resources available to customers to develop and improve their @RISK  models and obtain answers to simple (and not so simple) technical and modeling questions . We will cover the following subjects:

  • How to use the knowledge databases.
  • What do I get from the maintenance and support contract.
  • The tutorial ; your private @RISK teacher
  • Read the manuals!
  • How to benefit from the samples featured on the manuals.
  • Palisade books available and what they cover.
  • On line presentations via Webex
  • Benefits of attending a Palisade seminar/on-site seminar/consulting.
  • Real examples of on-site training benefits.

Real Options Modelling with @RISK and PrecisionTree

Dr Michael Rees
Palisade Corporation

This session introduces the topic of real options modelling as an extension of risk modelling. The link to general decision making under uncertainty and financial market options is also discussed. A variety of examples using @RISK and PrecisionTree is presented.

@RISK for Project Refresher

Ian Wallace
Palisade Corporation

The aim of this seminar is to give people a basic understanding of how @RISK for Microsoft Project works, including hands-on experience for setting up and running simulations, and interpreting the results.

Attendees will learn about the key functionality within @RISK for Project in step-by-step method, enabling them to quickly become familiar with basic concepts and terminology.

In addition to graphing and quantifying the risk in a business plan, you will learn how @RISK for Project, using Monte Carlo simulation enables you to:

  • Calculate the probability of success
  • Graph the margin of error around the most likely outcome
  • Quantify and prioritise the risk drivers
  • Quantify the amount ‘@RISK’
 

David Abrahams
Consultant
Brand Mediation

David Abrahams spent twenty-three years in brand management, new product development, and business management with global consumer goods and service companies. For the last decade he has advised corporate clients on brand-related aspects of risk management. A graduate in law from Cambridge University, he is a member of The Chartered Institute of Arbitrators with accreditation as a commercial mediator. He is the author of “Brand Risk – Adding Risk Literacy to Brand Management” to be published in March 2008.

Salomon Aguado-Manzanares
Actuary
CEIGRAM (Research Center for the Management of Agricultural and Environmental Risk)

Salomon Aguado-Manzanares has an M.Sc degree in Financial Economics from the University Complutense of Madrid, and in Agricultural Economics from the Technical University of Madrid. He has a B.Sc degree in Business Administration, and in Actuarial and Financial Science, both from University Charles III of Madrid. He obtained the First National Award of Excellent Academic Record by Government.

Aguado-Manzanares is an actuary and has worked as a researcher for five years at CEIGRAM (Research Center for the Management of Agricultural and Environmental Risk). He is preparing his PhD thesis on Risk Management in Agriculture, specifically about evaluation and prospecting revenue insurance in Spain, and he uses simulations for designing agricultural insurance.

Michael Brand
Captum Capital
Michael Brand is a founding director of Captum Capital Limited, an innovative consulting company which provides business development services to early stage life science companies. Captum offers a series of training courses in business development, including the highly successful Valuing Life Science Technology MasterClass, which has been attended by over 350 executives. Michael spent most of his career in the USA holding senior executive level positions in multi-national corporations, including a venture capital company. He has successfully negotiated international joint venture, licensing and investment agreements. He holds a PhD from Imperial College, London, a MBA from the Sloan School, MIT, Boston, and the Investment Management Certificate of the CFA Society of the UK; he is a Fellow of the Royal Society of Chemistry.

Ujjwal Bharadwaj
TWI Ltd
Cambridge/ Loughborough University

Ujjwal is pursuing an Engineering Doctorate at Loughborough University and is on secondment to TWI Ltd, Cambridge. His research interest lies in risk based assessment of equipment and structures. After completing his basic degree in Electrical and Electronics engineering, he served in industry in the maintenance sector. He then studied Risk Management at the London School of Economics at the Master's level before taking up the engineering doctorate.

Dr Douglas Cardoso
ArcelorMittal

Dr Douglas Cardoso concluded his doctorate in Business Administration at Federal University of Minas Gerais (Brasil) in 2007, emphasis on Strategy. Some years before, he had concluded his Master Science degree in the same University, emphasis on Finance. He has 9 years experience as a teacher in Brasilian universities, both in college and in post graduation courses. Today he is Corporate Risk Manager at ArcelorMittal Corporate in Luxembourg, having started in the company 21 years ago, in ArcelorMittal Monlevade (former Belgo Mineira), in Brasil. He published many articles in specialized journals as also as works in events’ annals. He participated in several international and Brasilian events. He oriented 49 final works in the administration area. He received 9 awards and/or honours. Between 2003 and 2004 he coordinated one research project. He works in the area of administration, with emphasis on: Finance, Strategy and Risk Management. In his academic activities interact with 4 collaborators as co-authors of scientific papers. In his Curriculum Lattes (http://lattes.cnpq.br/0419939495412217) the terms most frequently in the contextualization of his scientific, technological and artistic-cultural production are: ERP, Neural Networks, Steel, SAP R/3, Long Steel, Structural Equations, Strategy, Finance, Information Systems and Continuous Casting.

Manuel Carmona
Director of Sales
Palisade Europe

Manuel joined Palisade UK Ltd in 2003 as European sales director. He currently works with Palisade’s European customers to help them implement @RISK and other Palisade products in their organisations. He has spent most of his career in Europe working in various management and business development positions in the software industry. In 1994 he received a diploma in Computing for Commerce and Industry from the University of Barcelona, and an MBA from the University of Westminster in 2002.

Dr Jennifer I Considine
Senior Research Associate, Center for Energy Petroleum and Mineral Law and Policy
University of Dundee

Dr Jennifer I Considine is a Senior Research Associate at the Center for Energy Petroleum and Mineral Law and Policy at the University of Dundee, Scotland. She is actively pursuing a number of research projects in the area of Resource Curse, Scottish Energy Policy, Emissions Trading, Russian Crude Oil Supply, and Global Energy Policy. Her latest lists of projects includes acting rapporteur for the Economic and Social Research Council, "Econometric Modelling of Energy and Soft Commodity Prices with Applications". Award Holder: Dr S. E. Satchell and Dr A. Sancetta, Cambridge University, United Kingdom. Dr Considine is a Staff Scientist with Innervision Medical Technologies Inc. in Calgary, Alberta Canada. In this capacity, she is responsible for the mathematical and statistical modeling, signal processing and design of a new Ultra High Resolution Imaging technology for the early detection of breast cancer. She currently holds the position of Chief Editor, Energy Politics, an energy newsletter dealing with commercial strategies, and strategic planning in the global energy industry. Dr Considine is a member of the Board of Directors for Canada Post, and founding member of a number of initiatives to promote Scottish-Canadian relations including the Alberta Friends of Elgin, and the Canadian Friends of Scotland.

Dr Considine holds a Ph.D. in resource economics from the University of Aberdeen, Scotland, and a M.A. in International Finance from the University of Chicago. On the research side, Dr Considine has published a number of books and numerous articles concerning the global energy industry, with a focus on the history and development of the upstream petroleum industry in the Russian Federation. Her latest project: "Transformation and Reform in the Russian Energy Sector", Joint Project on Revitalizing Russian Industry, University of Calgary-Gorbachev Foundation Public Policy Project (UCGF'99), has recently been accepted for publication.

Andrea Dickens
Decision Analysis Group, Unilever

Andrea Dickens joined Unilever in 1988 as a statistician. Since then she has had a number of roles in Unilever, but all with one thing in common: managing and analysing uncertainty.

Andrea now leads the Decision Analysis Group, which has been developing and applying a wide range of decision analysis techniques. These techniques have been deployed on probabilistic business cases and complex decision problems throughout Unilever. The group also leads the development and rollout of training courses for Unilever Managers in Decision Making techniques, and provides coaching to course participants. In addition, the Decision Analysis Group has an internal consultancy role where they provide facilitative leadership to analyse complex business decisions. These tend to be the large, difficult and sensitive problems, high stake one-off decisions, or problems that cross organisational boundaries.

David Edison
Senior Consultant, Forecasting
Moore Stephens Consulting LLP

David Edison graduated in 1993 from the University of Kent at Canterbury with a degree in Actuarial Science before spending his early career with a Lloyd’s of London syndicate. He then worked with two leading firms of consulting actuaries, one in the insurance field, the other in pensions. He joined Moore Stephens in 1998. David has spent the last ten years using @RISK to apply actuarial, statistical and stochastic risk modeling techniques to a range of industries. He is responsible for statistical modeling teams for both Moore Stephens Consulting in London and Moore Stephens Business Solutions in New York, and services clients throughout the world, including Latin America. Of the challenges he has faced in a decade of consulting, the greatest has often been explaining results to non-statistically minded clients and maximizing the benefits that can be gained by presenting modeling outputs using ever-advancing technologies and techniques.

Frank Lyhne Hansen
PricewaterhouseCoopers

Frank Lyhne Hansen is a MSc Math. + Econ. from Copenhagen University and Associate Professor at Copenhagen Business School (department of finance). He has more than 15 years experience working with risk management and finance. He has lead a number of Enterprise Risk Management projects in major companies in Europe and USA. Also Basel II and Solvency II projects have been part of his work during the last 15 years. He has contributed to the academic literature and been a speaker at several international conferences. Teaching risk management courses at Copenhagen Business School, London Business School and the University of New York. He is the leader of the ERM-team at PwC Denmark and a member of the steering group in PwC Eurofirm.

Knut Hollund
Consultant
StatoilHydro

Knut Hollund has a master of science in Mathematical Statistics from the University of Oslo (1991). His first working year was military service as a research scientist at the Norwegian Defense Research Establishment, and since then he has worked within the oil and gas business with decision support and statistical modeling. Knut worked for Norsk Hydro from 1992 to 2000, and has since been employed by Norwegian Computing Center. During this period he has worked for The Norwegian Petroleum Directorate, and for a range of oil companies, primarily Norsk Hydro and Statoil. Knut’s current focus is working for the merged StatoilHydro.

Dr Mirek Janusz
Software Engineer
Palisade Corporation

Mirek Janusz is a software engineer at Palisade Corp. He holds a masters degree in computer science from Cornell University. He helped develop Palisade applications for risk analysis, statistical analysis and optimization, and has assisted other companies in integrating Palisade Developer's Kits into their own applications. He was the lead developer for Palisade's new Excel add-in for neural networks, NeuralTools.

Sam McLafferty
President and CEO
Palisade Corporation

Sam McLafferty is Palisade's founder, president, and CEO. He started the company in 1984 with the release of PRISM, a stand-alone Monte Carlo simulation package for DOS on the PC. PRISM later evolved into @RISK for Lotus 1-2-3, and then for Excel. Sam is Palisade's lead developer, with over thirty years of programming experience. He works closely with the technical and sales staff, ensuring that customer feedback is heard. He personally oversees the development and evolution of every one of the fifteen software products Palisade sells. Prior to Palisade, he was a risk analysis consultant.

Dr Raj Nataraja
Head of Quantitative Analysis
Chase Cooper

Dr Raj graduated in 1966 and obtained his doctorate in aeronautical engineering in 1974 from Loughborough University in the UK. He has over ten years teaching and academic research experience, and over twenty eight years in the oil and gas industry. He was R&D Manager at Lloyd’s Register, Brown and Root, Kaverner Earl and Wright, and during the latter years he worked for Noble Denton. Since late 2005, he has been the head of Quantitative Analysis at Chase Cooper. He has worked as an advisor on a number of academic and industry committees involved in R&D (SERC, MTD, Den, HSE, Offshore Research Strategy Board etc).

Dr Raj has managed and supervised a number of R&D projects related to front end technology leading to innovative design in the oil and gas industry. Many of these projects required development of analytical tools for stochastic modeling, probabilistic analysis, risk and reliability analysis, and safety engineering. In his present capacity he is applying his analytical experience to the development of operational risk management tools for the financial industry, and provides quantitative analysis consultancy.

Dr Michael Rees
Senior Consultant
Palisade Corporation

Michael has 20 years’ of business and finance experience, including roles such as Principal (Partner) at the strategy consultants Mercer Management Consulting (now Oliver Wyman) and Vice-President of Equity Research at J.P. Morgan. He has worked independently for 8 years, for 6 of which he was retained by Palisade Corporation to act as their Director of Training and Consulting. He is the author of Financial Modelling in Practice: A Concise Guide for intermediate and Advanced Level, John Wiley & Sons, 2008).

His academic credentials include a Doctorate in Mathematical Modelling and Numerical Algorithms, and a B.A. with First Class Honours in Mathematics, both from Oxford University. He has an MBA with Distinction from INSEAD in France, as well as holding the Wilmott Certificate of Quantitative Finance, where he graduated top of the course for class work and also received the Wilmott Award for the highest final exam mark. Michael is based in the UK and speaks fluent French and German.

Sven Roden
Decision Analysis Group
Unilever
Dr Sven Roden is a senior Decision Analyst within Unilever’s Finance Academy, acting as an internal consultant leading decision analysis evaluations on problems where teams have been struggling to find a solution. He is also involved in developing new methodologies and providing expert training and coaching to Unilever's financial managers. Prior to joining Unilever, Sven worked for BNFL as a Technology Strategist and research physicist.

Dr Loai Saadah
Tawam Hospital
Dr. Saadah is a Board Certified Pharmacotherapy Specialist (BCPS) from the Board of Pharmaceutical Specialties in the USA since December 2007. He is currently working as a clinical pharmacist in critical care settings in Tawam Hospital, The United Arab Emirates, in affiliation with Johns Hopkins Medicine, USA. He received his Bachelor of Science in Pharmacy (May 1999) from Jordan University for Science and Technology, a Master of Science in Hospital Clinical Pharmacy (December 2002) from the University of Iowa, and a Doctor of Pharmacy (PharmD) with special honors and high distinction from the University of Iowa (July 2006), Iowa, USA. Dr. Saadah’s main area of clinical practice is in the pediatrics and neonatal intensive care units where he is responsible, together with medical teams, for devising, implementing, monitoring, and modifying patient-individualized care plans. Dr. Saadah is interested in an array of medical and pharmaceutical fields; including infectious diseases, cardiology, and neurology, where he aims at solving clinical problems with artificial neural networks and genetic algorithms software applications. He brings an experience of almost a decade in practice, research, education, and innovation in pharmacy, has been an invited speaker to a number of scientific meetings and was involved in the publication of several medical articles.

Fátima Santos
TiS.pt

Fátima Santos is a Transportation Consultant, graduated in Civil Engineering, field of Transportation and Territorial Planning, at Instituto Superior Técnico (IST), Technical University of Lisbon. She complemented her academic qualification with the scholar part of the Master in Transportation and she post-graduated in Marketing Research and Customer Relationship Management. Since 1997 she participated in several consultancy projects and studies in the domain of urban mobility and design of public transport networks, high speed train networks operational studies, transport surveys and marketing research, mobility surveys, transport economics, environmental and social impact of transport systems, transport demand estimation, regional planning. She is a TIS member since 1999.

Jaime Gabriel Silva
Director of Engineering
Águas do Douro e Paiva S.A.

Silva has a MSc degree in Building Construction, and graduated as Civil Engineer from the Faculty of Engineering of Oporto’s University. He received the Prof. Barbosa Abreu Award, distinguishing the best student of the Masters in Building Construction programme, 5th edition (1994-96). He serves as an Adjunct Professor at the Instituto Superior de Engenharia do Porto in the Civil Engineering Department.

In August 1998, Silva joined Águas do Douro e Paiva, a water supply company for Oporto’s region, becoming the Director of Engineering in 2000. He is responsible for the company’s systems project management.

Previous experience includes: seven years at engineering enterprise Fase; one year at a Construction Laboratory CICCOPN; two years as system engineer at IBM Corporation; and three years at a Portuguese regional administration organization - CCRN.

Inês Teles
TiS.pt

InĂŞs graduated in Territorial Engineering by Instituto Superior TĂ©cnico, Technical University of Lisbon (1999). She has complemented her academic qualifications with scholar Master Programme in Operational Research and System Engineering. She has developed her professional career as a planner and manager road projects, specializing in traffic demand studies for new road infra structures. In TIS.pt enterprise she is working on research and consultancy projects covering areas such as cost-benefit analyses and transport model development.

Viktor Thorisson
Analyst
Enex
Viktor Thorisson is an industrial engineer and works as an analyst at the Icelandic geothermal energy company Enex. His work consists in feasibility analysis of potential projects, development of financial model and simulation applications (@RISK), where input variables to financial modeling are with high degree of randomness, such as what to expect from a geothermal well, application of decision theory to real life problems and other analytical problem solving. Viktor studied both in Iceland (University of Iceland) as well as in Argentina (Universidad del Salvador) and has experience as a support teacher in computer science at the University of Iceland.

Ian Wallace
Consultant
Palisade Corporation

Ian is a qualified accountant (ACMA) and spent the first 10 years of his career working in finance functions within the pharmaceutical and precious metal refining industries. Here he gained valuable experience in managing staff, preparing financial and management accounts, and working closely with business managers to help develop project appraisals, corporate plans and annual budgets.

Ian then decided to move into management consultancy with KPMG and worked on a wide variety of interesting projects throughout Europe and the Middle East, both in the private and public sectors. Most of his work was project management related and involved the selection and implementation of the latest systems and business processes within the finance function. Work included preparing strategy studies and business cases for change, PFI and supplier evaluations, recruiting project teams, leading systems design and working with all the management and stakeholders connected with the change. As a result, Ian has considerable experience of project management methodologies such as PRINCE 2 and others.

It was during the course of this work that Ian came across Monte Carlo Simulation and @RISK. He found it such a useful tool for managing expectations, developing risk-efficient plans and getting adequate resources for his projects that in 2001 he decided to specialise in the technique. Ian has now been working closely with Palisade since 2005, and during this time has delivered numerous on-site customer training sessions, public classroom courses, web-conference training sessions, and consultancy projects.

Ian's wide experience, together with his communication skills, enable him to communicate complex subjects using everyday, easy to understand language.

Tim Wells BSc.
Consultant, AIEMA
Maritime Business Group, Halcrow Group Ltd
Tim Wells is a consultant and project manager with Halcrow Group Ltd. Halcrow specialises in the provision of planning, design, and management services for infrastructure development worldwide. With interests in transportation, water, maritime, and property, the company is undertaking commissions in over 70 countries from a network of more than 70 offices.

Tim provides Group wide support and advice on risk and decision analysis in a variety of disciplines including, flood risk, project risk, strategic planning, and formal uncertainty analysis. As part of this work Tim has helped apply @RISK software in a variety of projects to support decision making and add value to internal and external clients.

Erik Westwig
Software Engineer
Palisade Corporation

Erik Westwig receivedhis B.Sc. in 1991 and M.Sc. in 1994from the applied and engineering physics department at Cornell University. In 1998 he published the book, Mathematical Physics with co-author Bruce Kusse, which was just re-released in its second edition. Since 1995 Eric has worked as a software engineer at Palisade aspart of the DecisionTools Suite development team.

Dr Paul Wilmott, Keynote Speaker
Dr Paul Wilmott is a financial consultant, specializing in derivatives, risk management and quantitative finance. He has worked with many leading US and European financial institutions. He is a member of the Physics in Finance Committee of the Institute of Physics, and is on the editorial boards of several academic journals. Paul studied mathematics at St Catherine's College, Oxford, where he also received his D.Phil. He founded the Diploma in Mathematical Finance at Oxford University and the journal Applied Mathematical Finance. He is the author of Paul Wilmott Introduces Quantitative Finance (Wiley 2007), Paul Wilmott On Quantitative Finance (Wiley 2006), Frequently Asked Questions in Quantitative Finance (Wiley 2006) and other financial textbooks. He has written over 100 research articles on finance and mathematics.

Dr Paul Wilmott was a founding partner of the volatility arbitrage hedge fund Caissa Capital which managed $170 million. His responsibilities included forecasting, derivatives pricing, and risk management.

Dr Wilmott is the proprietor of www.wilmott.com, the popular quantitative finance community website, the quant magazine Wilmott and is the Course Director for the Certificate in Quantitative Finance.

Palisade Corporation
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