Risk Analysis Software

When doing risk analysis simulation modeling, it’s critical to represent the fact that many variables are related. Very rarely are all variables completely independent of one another.  For example, when interest rates are low, housing starts tend to go up. This relationship is represented by a correlation coefficient, a number between -1 and 1. -1 means the two variables are perfectly negatively correlated;  that is, when interest rates go up 1 unit, housing starts go down 1 unit. A coefficient of 1 is exactly the opposite, and 0 means there is no relationship between the variables at all. Typical coefficients are between these extremes. This correlation between input variables can and must be captured in your financial risk analysis models. Even if you are using expert opinion, estimating these relationships is better than ignoring them completely.

@RISK provides an easy way to define correlation coefficients. Through the use of a slider bar, you can dynamically update coefficients between variables as well as scatter plots representing those variables. This helps you visualize and more easily define these relationships.

For a quick video demonstrating this, see:



» View short videos on recently added @RISK risk software features

Presented at the Palisade Risk & Decision Analysis Conference New York City
Chris Albright, author of the book VBA for Modelers, presented a number of examples of how to automate @RISK, RISKOptimizer, and StatTools in Excel using Excel's VBA and Palisade's built-in object-oriented Excel Developer Kit. These examples include production applications, scheduling applications, World Series simulation, and more. All examples include macros written by Dr. Albright, so you'll need to enable macros when you open them.

» Download the examples
» Order Dr. Albright's book "VBA for Modelers"

Abbott suggests a compromise: streamline your plot by masking (Abbott says "removing") fields that contribute to the robustness of the analysis but involve statistical twists and turns that are distracting to decision makers who may not be fascinated with technique and just want to see how the story turns out. This, he explains, allows you to work from a model both you and the decision makers can believe in.

Your thoughts? 

The UK's new coalition government has said that, as part of its 'Green Deal', it will encourage home energy efficiency improvements paid for by savings from energy bills. It seems likely that, in the year that energy regulator Ofgem warned of 20 percent electricity price hikes by 2020, this initiative will include solar panel technology

Currently the UK still lags behind many other countries in Europe and the rest of the world when it comes to harnessing solar power. Not only do we have less hours of sunshine than many regions, but there is a lack of clarity as to the 'payback' time when it comes to users seeing a return on investment.

This is where Palisade customer, the California-based Tioga Energy, makes an interesting case study. Whilst it may seem unfair to compare the UK with the west coast of America when talking about solar-related matters, the sunnier climate does not reduce the need to prove ROI for customers with solar energy agreements.

Tioga Energy provides project financing through its solar Power Purchase Agreements (PPAs), and maintains and operates solar systems on behalf of its customers. Tioga’s offering delivers predictably priced power and enables organisations to to both 'green' their operations and reduce energy costs. To illustrate the benefits of solar, estimating future electricity prices and making comparisons by showing the savings from a new solar system, Tioga enlisted the help of @RISK for risk analysis solutions.

To forecast possible price increases, Tioga Energy inputs California's historical electricity rate data into a quantitative risk analysis model developed using @RISK. This generates a probability distribution for electricity rate rises over the 20-year PPA period, which shows that there is a 25 percent likelihood that price increases will be less than 4.8 percent, and a 25 percent chance that rate rises would be more than 8.7 percent.

The @RISK risk analysis model therefore helps Tioga Energy evaluate the likelihood that a customer will save money for a variety of PPA scenarios (i.e. the rate at which electricity would initially be charged and the amount by which it would then increase each year). It also calculates the magnitude of savings for the different combinations of first year costs and subsequent rises. Consumers are therefore able to better understand the pricing and make an informed decision about whether to sign up for a PPA.

Using historical data and @RISK's risk modelling software capacity, Tioga offers consumers a robust view of the potential benefits of a solar PPA. This enables them to hedge against rising electricity rates, as well as feel confident that they are playing a part in tackling global warming.

» Read the Tioga Energy case study

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

@RISK risk analysis software using Monte Carlo simulation is used for a wide variety of applications. In this model, we have an example of a general usage to address Operational Risk.

In many circumstances one wishes to calculate the aggregate impact of many possible yes/no type events. For example, it is often important to answer questions such as "What is the loss amount that will not be exceeded in 95% of cases?" @RISK simulation can be used to answer such questions.

» Download the example: EventandOperationalRisks.xls

With the New York State primaries coming up September 14 and the general election on November 2, I predict that as soon as summer turns the corner into September, we'll start hearing lots and lots about polls that predict election outcomes.  To find out if there was any early discussion of polls, polling, and outcomes, I returned to my favorite election forecast site from the 2008 presidential elections, FiveThirtyEight: Politics Done Right.

You can quickly generate a graph of any simulated output relative to a given input using the tornado graphs in @RISK risk analysis software. Just drag a bar from the tornado graph onto a blank space, and a scatter plot of that input relative to the simulated output appears. This is a great communication tool when you’re trying to assess the most important risk variables in your risk analysis model and want to “zoom in” on a particular input to see what’s going on. It can really help understand relationships in your modeling.

See this video for a quick demo:



» View "Getting Started in @RISK" video tutorials

In @RISK (risk analysis software using Monte Carlo simulation), you can easily overlay two graphs directly from any graph window.  Just click on a button to identify the variables to overlay, and you’re done. Alternatively, you can drag thumbnails of different simulated results onto the same graph to create overlays. These overlays in the risk analysis models are useful for comparing different strategies, showing trends over time, and more.

A quick video showing how to do this:



» View short videos on recently added @RISK features

@RISK, Palisade's risk analysis software using Monte Carlo simulation, has many features that help you create powerful models for decision making under uncertainty.

This model demonstrates the use of the alternate, percentile parameter formulation. In this case we assume that we have decided to use a Normal distribution to represent the arrival time of someone at work. The use of traditional parameters would require knowledge of the standard deviation of the arrival time, which may be hard to estimate. The use of the alternative parameter formulation allows data to be estimated by others in a more natural way. In the first case, the traditional parameters are used (mean and standard deviation). In the second case, the mean is still used, and the P90 is used in place of the standard deviation, i.e. the time before which the person arrives in 90% of cases. In the second case, the P10 and the P90 is used in place of the standard deviation i.e. the time before which the person arrives in 10% of cases, and in 90% of cases respectively.

» Download the example: AltPars.ArrivalTime.xls

One of the side-effects of the recession appeared to be a reduction in the demand for electricity as businesses and consumers alike looked to make savings on their outgoings. However, economic recovery seems to render this trend as temporary, meaning that the global need to tackle energy-consumption is as pressing as ever.

BC Hydro, Canada's third largest electrical utility provides an interesting case study in how to  ascertain the most effective ways to tackle the gap between supply and demand of electricity in British Columbia. Trends such as an expanding population growth, and the increase in energy usage per customer, have led to a rise in the demand for electricity across the region. By legislation, BC Hydro must aim to meet these energy needs through implementing cost-effective energy conservation approaches before it can turn to increasing the supply. 

The company has set itself one of the most aggressive targets in North America, with a plan to meet almost 75 percent of its incremental load through Demand Side Management (DSM) over the next 20 years. DSM projects include compact fluorescent light promotions; subsidies for energy efficient appliances; variable speed motor promotions (for home furnaces); and promotional activity aimed at motivating customers to use less energy.

BC Hydro uses @RISK risk analysis software to measure the uncertainty around its energy conservation efforts, both at the project stage and at a higher portfolio level. Around 60 projects were analysed on a case-by-case basis, and a probability distribution around the forecast outcome was developed. @RISK helps BC Hydro to capture the level of uncertainty of the estimated savings for each individual DSM venture.

In recognition that projects do not operate in isolation, BC Hydro also uses @RISK to explore the interrelationships between key uncertainties: the participation and savings per participant, and the participation across projects. The analysis showed that if a 'conservation culture' was developed in the province, it would result in an increase in energy savings across all programmes. However, it also illustrated that, if this culture failed to materialise, the performance of all programmes will be dragged down.

Exploring uncertainty using @RISK allowed BC Hydro to find the best balance between the uncertainties of supply side resources and those of relying heavily on energy conservation. Employing Decision Making Under Uncertainty helps BC Hydro to meet both financial risk analysis and environmental risk analysis goals. As a result, it expects to meet the majority of its incremental load growth through conservation measures.

» Read the BC Hydro case study

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

@RISK, risk analysis software using Monte Carlo simulation, has many powerful features that help you create powerful models for decision making under uncertainty.

For example, you can use the RiskTheo function in @RISK to determine the parameters of a discrete distribution based on a continuous one. In this example, the RiskTheo functions of @RISK work out the P10, P50, and P90 percentiles of a continuous distribution (in this case the LogNormal), and the Mean and Standard Deviation of a RiskDiscrete distribution which has these X-values and some assumed probabilities (or weights). It then uses Excel's Solver to work out the probabilities required so that the discrete distribution based on these percentiles and probabilities would have the same mean and standard deviation as the continuous distribution.

» Download the example: CtsToDiscrete.xls
» See "Uses of the RiskTheo functions in
   @RISK to match distributions"
 » See "DMAIC Failure Rate using RISKTheo" for a
    Six Sigma application of the RISKTheo function

Just after I posted my last blog questioning a recent Investopedia column in the San Francisco Chronicle, I had a congenial note from the author of that column, David Harper.  His column compared Monte Carlo Simulation with two other methods of calculating Value-at-Risk, and I was concerned that its view of risk and risk analysis techniques was overly simplified. David   was surprised to discover that column had just appeared because he wrote it five years ago!

The five-year lag explains a lot--Monte Carlo simulation was not nearly so widely adopted or carried about by so many software tools as it is today--and I should have suspected the article was a vintage piece before I started carping.

So I happily retract my concerns to introduce to you David Harper, CPA and certified Financial Risk Manager.  In response to my comment about the attitudes and techniques that led to last year's collapse of the financial markets, David says that, now that the black swan has flown, "the crisis should implicate both HistoricalSim VaR and parametric VaR (at least multivariate normal!) and point toward Monte Carlo Sim. I've been thinking for a while that all of this [I think he means lack of accuracy in specifying risk] should really boost Monte Carlo."

Investment commentary is only one of David's activities.  He is the founder of Bionic Turtle, a business devoted to e-learning about financial risk and preparation for the certification exam for financial risk managers. This is a worthy enterprise--I was relieved to discover that there are hoops financial risk managers have to got through to be called that--and for anyone who would like to know more about quantitative techniques for risk analysis, its website is worth prowling. 

Thank you, David, for setting me straight.  

On Thursday, July 15, 2010, Andy Sleeper will present a free live webcast entitled. "Why be Normal? Selecting the Best Distribution Models "

Distribution models are important aspects of many types of statistical analysis, including Monte Carlo analysis. The choice of model is vitally important, since the wrong model can be worse than no model at all. But with dozens of distribution families to choose from, the choice of distribution model can be confusing and mystifying. This free live webcast takes the mystery out of distribution model selection and explains the powerful tools built into @RISK and StatTools. How often have you wondered which type of graph is best suited for selecting distribution models? Which goodness-of-fit test is best for you? Is Kolmogoroff-Smirnov a new kind of vodka? All this and much, much more shall be revealed with demonstrations of Palisade software during this unique webcast.

» Register now (FREE)
» View archived webcasts

The UK finally seems to be heading into summer after an unusually long and cold winter.  However, despite the amount of rain that falls on our 'green and pleasant land' (to the extent of major flooding on occasions), one of the anomalies of the UK weather system is that any prolonged warm period seems to be accompanied by the underlying threat of a hosepipe ban.

This is in stark contrast to many regions around the world that, despite seeing far less precipitation, manage a robust water supply.  Abu Dhabi for example has no rain for several months of the year, and relies almost completely on desalinated seawater for its potable water requirements.  The desalination process is challenging in terms of operation, costs, and environmental impact.  Whilst over-production capacity is expensive, at the other end of the scale it is essential that Abu Dhabi has sufficient water production capacity to support the Abu Dhabi government development plan (Abu Dhabi Plan 2030). 

This plan means that the Abu Dhabi Water & Electricity Company (ADWEC) is required by the Regulation and Supervision Bureau (RSB), the regulatory body of Abu Dhabi, to use a risk-based methodology to assess the water demand and required capacity.  As a result ADWEC uses @RISK risk analysis software to help it to forecast as accurately as possible the demand for water and electricity across the Emirate in order to plan for the optimum expansion as well as the efficient and effective use of water production plants.

@RISK enables ADWEC to model all feasible uncertainties in the variables that determine the quantity of water required over specific timescales, such as per capita water consumption rates and the rate of population growth.  The variables input into the @RISK risk simulation software model are based on the water demand categories such as domestic, agricultural and industrial. Factors with inherent uncertainties that affect the demand forecast outcome and must be modelled include: seasonal variation, distillers' unplanned outages, water losses, population growth rates and demand for housing.

By undertaking risk analysis of the variables involved in assessing demand and supply, ADWEC minimises the potential for water production capacity to be over or under deployed.  As a result of using @RISK to assist with its forecasting, planning and management strategies, ADWEC has been able to consistently meet with almost complete accuracy the Abu Dhabi Emirate water demand forecasts.

A useful lesson...

» Read the full ADWEC case study

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

Modeling Uncertain Number of Events, Each with Uncertain Parameters
@RISK (risk analysis software using Monte Carlo simulation) is widely used in insurance and reinsurance for premium pricing and loss reserves modeling. A 2006 survey identified @RISK as the third most widely-used software by actuaries, after Microsoft Office and in-house actuarial tools.

@RISK's RiskCompound function allows for the sampling of frequency-severity distributions. This is often required in insurance modelling, as well as in some operations management situations. For example, to determine the total insurance claims payout, one must account for the uncertainty in both the total number of claims (frequency) and the dollar amount of each claim made (severity). 

A powerful feature of the function is that the argument that corresponds to the severity may be a reference to a cell containing a formula (rather than just a single distribution function). 

For example, one could use the function in the form RiskCompound(RiskPoisson(5), A10). The Poisson distribution would describe the frequency (occurrence) of events (e.g. an individual sample may determine that three events occur), and cell A10 would contain a formula that is separately evaluated for each of these three events (before returning the sum of these three as the sampled value of RiskCompound). 

A simple example could be A10 = RiskLognorm(10000,1000)/(1.1^RiskWeibull(2,1)), with the Weibull distribution representing the time to settlement of an insurance claim, which is used to discount the basic claim value sampled from the Lognormal distribution of severity. For example, once a claim is filed for a nominal amount, the actual payment may be delayed due to court actions or disputes, which may reduce the cost of the claim to the insurer.

» Download the model: RiskCompoundCellReferencing.xls

Variance-covariance assumes volatility only in terms of standard deviation, and volatility doesn't come in one flavor or standard deviation.  Neither does risk.   

Working in dynamic groups is a fact of modern life. You may work with different colleagues on different projects, and may work with others in remote offices as well. When you use risk modeling software, being able to share information with others is critical to team projects.

@RISK offers a number of ways to facilitate sharing and collaboration of risk analysis models. This three-part series will highlight popular sharing methods.

Did you know that you can remove @RISK functions from @RISK spreadsheet models in order to share them with others? This enables those without @RISK installed to be able to read and edit the same models. It’s great for sharing and collaborating across various workgroups. Furthermore, changes that non-@RISK users make will be preserved when you go to restore the @RISK functions back into the model.

It’s easy to do – check out this video:



» "Getting Started in @RISK" video tutorials