Wednesday, March 7th, 2018
10am CST Mexico City / 1pm BRT Rio de Janeiro / 4pm GMT London / 5pm CET Paris / 7pm MSK Moscow
There are many features of @RISK that make modelling much simpler, if only you knew they were there! In this free live webinar, we explore tips for using @RISK — for the beginner and experienced user alike. Learn how to overlay distribution graphs for comparison, change default settings to affect reports, and expertly share models and results with others. For efficient use of your time, you need to know how to get the most out of @RISK with minimal effort!
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Palisade expert trainer Rafael Hartke discusses how to use @RISK for cost estimation–more specifically, how to transform your deterministic models into probabilistic ones. This recorded webcast shows you how to add uncertainty as well as a risk register to your model.
About the trainer: Rafael Hartke is an Oil and Energy Industry Consultant at Palisade Corporation, where he works in the development and strategy of quantitative risk analysis methods. He has particular experience in the energy industry, having served as a Financial Engineer in Risk Management at Brazilian-based energy corporation, Petrobras in the Financial Planning and Risk Management department. There, he created risk models for complex investments and assessed project risks for medium and large projects, including Brazilian Pre-Salt giant fields, projects in the Gulf of Mexico, and offshore infrastructure projects.
Rafael has an MSc degree in Mechanical Engineering and is also a Global Association of Risk Professionals-certified Energy Risk Professional.
In this webcast, Palisade trainer Rishi Prabhakar explores tips for using @RISK that will benefit the beginner and experienced user alike. There are many features of @RISK that can make your modeling life much simpler, if you only knew they were there!
Rishi covers topics such as overlaying distribution graphs for comparison, changing default setting to affect reports and the way functions are inputted, and sharing models and results with others. Save yourself time and learn how to get the most out of @RISK with minimal effort!
About the trainer: Rishi brings a broad range of experience and expertise to the Palisade team. He has worked in and consulted to the energy industry, telecommunications, scientific research, banking and finance with an emphasis on operational risk and Basel II. Rishi has expert skills in the areas of statistical analysis, simulation, time series forecasting, risk/capital modelling, extreme value theory, survey design and analysis. He holds a BSc Mathematics from the University of Technology, Sydney.
@RISK can be very effectively used to forecast the production of oil and gas reserves about which little is known. This is a simple model forecasting production for a particular oil well. The estimated reserves within the well are uncertain and are represented with a Lognormal distribution function. The mean is 500,000 STB and the standard deviation is 50,000 STB.
The output in the model is the NPV of the reserves for the first 10 years of production. Other factors considered include the decline rate, the gas-oil ratio (GOR), the prices of oil and gas, as well as the rate of increase of the prices of oil and gas. The only input factor that contains an @RISK probability distribution function is reserves, but you could make the model more realistic by using distribution functions to describe the decline rate, GOR, price of oil, etc.
» Download the example
It is important for an insurance company to estimate the amount of claims it will incur in a given year. This series of models assumes that there are three types of claims: auto, general liability, and worker's comp. Historical data for the company is included. The top section lists the numbers of claims for the previous 20 years. Summary measures across years are shown to the right. These include correlations, since it is conceivable that the number of claims of one type might be correlated with the number of claims of another type. The bottom section lists the claim dollar amounts for all claims in the most recent year. Summary measures for these are also listed to the right.
The base model assumes that the numbers of claims and the amounts of typical claims are known for the coming year, and that they are equal to the means of the historical data. For example, it assumes that there will be 994 auto claims, each for the amount $3,409. Then the total of all claims, shown in the red cell, is the sum of products of the values in rows 4 and 5. Of course, there is a lot of uncertainty in the values in rows 4 and 5, so later models that include this uncertainty are much more realistic than this deterministic model.
Additional models are included using the RiskCompound function to summarize claims, and resampling to deal with minimal historical data.
» Download the series of models
» Watch a video about the insurance model series
In the histograms of my @RISK distributions and results, what do the numbers on the vertical axis mean? Can I make the scale more user friendly?
The default scale is probability density. This is adjusted so that the areas (height × width) of all the bars add up to 1.
You may prefer a relative frequency scale, where the height of each bar equals the proportion of the distribution that falls within that bar. To set relative frequency, click the histogram icon at the bottom of the window. See the blue highlight in this illustration:
A drop-down appears with your formatting choices. Select relative frequency — that's all there is to it!
You'll find more about probability density and relative frequency in this Knowledge Base article.
Can I change the default, so that @RISK always shows histograms in relative frequency?
Absolutely! In Utilities » Application Settings » Simulation Graph Defaults, change Preferred Distribution Format to Relative Frequency.
Click OK, and answer Yes to the confirming prompt. This setting will affect all input and output histograms in all your models. Naturally, you can still use the histogram icon at the bottom of any graph to change the format of that graph.
I administer my company's concurrent network license. How can I tell who has a license checked out?
On the server, run LMTools and click the Config Services tab. Select the Palisade service and click the View Log button. After the startup messages, you'll see a series of lines starting OUT or IN.
Each time a user opens the software, a line is written to the log with OUT and the user and machine IDs. When that user closes Excel, the license is released and an IN line is written in the log. Anybody with an OUT line and no IN line still has Excel open.
We have a Knowledge Base article with more details:
The @RISK — Excel Reports command selects reports to be generated on the active simulation results, or the current model definition.
A variety of different pre-built simulation reports are available directly in Excel at the end of a simulation. The Quick Report is a report on simulation results designed for printing. This report contains a single page report for each output in a simulation. The other available reports, starting with Input Results Summary, contain the same information as the equivalent report in the Results Summary Window or other Report windows.
You can also use template sheets to create your own custom simulation report. Simulation statistics and graphs are placed in a template using @RISK statistics functions (such as RiskMean) or the graphing function RiskResultsGraph When a statistics function or graphing function is located in a template sheet, the desired statistics and graphs are then generated at the end of a simulation in a copy of the template sheet when you choose the Template Sheets option in the Excel Reports dialog. The original template sheet with the @RISK functions remains intact for use in generating reports from your next simulation.
» Read more about generating @RISK reports in Excel
» View a short video demonstrating reports in Excel
» Download the example file Template.xls to see how to set up your own report
You can easily flag any cell that contains @RISK input distribution functions or @RISK output functions using the Application Settings window of @RISK. Application Settings is located under the Utilities menu in the @RISK ribbon:
Here you can apply default cell formats to cells in your workbook where @RISK inputs and outputs are located. You can select a color for cell font, border or background.
Using the Application Settings Dialog, a wide variety of @RISK settings can be set at default values that will be used each time the program runs. These include graph color, displayed statistics, coloring of @RISK cells in Excel, and others.
@RISK is an add-in to Microsoft Excel. @RISK performs risk analysis using Monte Carlo simulation to show you many possible outcomes in your Microsoft Excel spreadsheet—and tells you how likely they are to occur. This means you can judge which risks to take and which ones to avoid, allowing for the best decision making under uncertainty.
» Read more about Application Settings in @RISK
@RISK risk analyis software gives you multiple options for saving Monte Carlo simulations you have run and comparing them with other simulations.
Storing simulations in your Excel workbook
Saving simulations as a separate .RSK5 file outside the workbook
Using the @RISK Library for storing and comparing different simulations.
When you want to store simulation results and graphs, @RISK allows you to keep all data in your Excel workbook. This makes it easy for you to give simulations to others, without worrying about sharing a separate simulation file. However, if you want to store the simulation results in a separate .RSK5 file in order to reduce the file size of the Excel workbook, you have that option, too.
When a simulation is saved in your workbook, all data and graphs are stored and will be automatically opened the next time you open the workbook in Excel with @RISK running.
You can also use the Application Settings command in the @RISK Utilities menu to specify the default location where you wish to store your @RISK data. These options make it easy to manage your risk analysis models.
» Read more about Saving Simulations