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Monte Carlo Simulation For Mac

Monte Carlo Simulation Software Mac Download And. Our built-in antivirus scanned this Mac download and rated it as 100 safe. RiskEngine.dmg is the most frequent filename for this programs installer. The application lies within Productivity Tools, more precisely Office Tools. In this video, I cover the basics of Monte Carlo simulation, and show how to make a Monte Carlo simulator in Excel.The concepts found in this video are some.

The XLSTAT-Monte Carlo Simulations module for XLSTAT allows you to create models with assessed risk in Microsoft Excel and uses simulation methods such as Monte Carlo and Latin Hypercubes simulations to estimate the distribution (including confidence intervals) of important variables.

XLSTAT-Monte Carlo Simulations module is a key decision making tool for people working on statistical risk analysis of models which may contain uncertain values. These uncertainties can be expressed through more than 30 distributions.

For example, in a financial model for establishing a budget, the sales volume of a product is not certain, but we can estimate that it should be between two bounds, A and B, with a most likely value M. This can be statistically represented by a triangular distribution. The total revenue for all products is a sum of triangular distributions. XLSTAT-Sim can produce in mere seconds, an estimated distribution of the revenue, its median, average and a 95% confidence interval.

Note on XLSTAT-Monte Carlo Simulations: The Sim module runs under all Windows versions of Excel, but not on the Mac.

Demo version

A trial version of XLSTAT-Monte Carlo Simulations is included in the main XLSTAT download.

Prices and ordering

These analyses are included in the XLStat-Forecast, XLSTAT-Marketing, XLSTAT-Quality and XLStat-Premium packages.

The simulation methods available in XLSTAT-Monte Carlo Simulations are Monte Carlo and Latin Hypercubes.

Simulation models

Simulation models allow to obtain information, such as mean or median, on variables that do not have an exact value, but for which we can know, assume or compute a distribution. If some 'result' variables depend of these 'distributed' variables by the way of known or assumed formulae, then the 'result' variables will also have a distribution. XLSTAT-Monte Carlo Simulations allows you to define the distributions, and then obtain through simulations an empirical distribution of the input and output variables as well as the corresponding statistics.

Mac

Simulation models are used in many areas such as finance and insurance, medicine, oil and gas prospecting, accounting, or sales prediction.

Four elements are involved in the construction of a simulation model:

  • Distributions are associated to random variables. XLSTAT gives a choice of more than 20 distributions to describe the uncertainty on the values that a variable can take. For example, you can choose a triangular distribution if you have a quantity for which you know it can vary between two bounds, but with a value that is more likely (a mode). At each iteration of the computation of the simulation model, a random draw is performed in each distribution that has been defined.
  • Scenario variables allow to include in the simulation model a quantity that is fixed in the model, except during the tornado analysis where it can vary between two bounds.
  • Result variables correspond to outputs of the model. They depend either directly or indirectly, through one or more Excel formulae, on the random variables to which distributions have been associated and if available on the scenario variables. The goal of computing the simulation model is to obtain the distribution of the result variables.
  • Statistics allow to track a given statistic a result variable. For example, we might want to monitor the standard deviation of a result variable.

A correct model should comprise at least one distribution and one result. Models can contain any number of these four elements.

Options for simulation models

A model can be limited to a single Excel sheet or can use a whole Excel folder.

Monte Carlo Simulation Software For Mac

Simulation models can take into account the dependencies between the input variables described by distributions. If you know that two variables are usually related such that the correlation coefficient between them is 0.4, then you want that, when you do simulations, the sampled values for both variables have the same property. This is possible in XLSTAT-Monte Carlo Simulations.

Add Monte Carlo Simulation to Your Spreadsheet Models

This guide describes how to convert a static Excel spreadsheet model into a Monte Carlo simulation, and the kind of information you can learn from the simulation. It will walk through the basic techniques, and the functions you will need to use. The full model, including each of the steps below, is available for download.

The examples in this guide use the RiskAMP Monte Carlo simulation functions.

  • You can download a free trial of our Excel add-in from our download page, and run these examples in Excel.
  • Or you can run the examples in your browser using RiskAMP web.

    • You can also use the embedded spreadsheets below to run monte carlo simulations right on this page.

    Investment Portfolio Model

    A typical investment portfolio model includes an opening balance, projections for returns and costs over several years, and a closing balance at some time in the future. A simple spreadsheet model might look like this:

    Figure A: The original model

    In figure A, the model is based on a fixed period (annual) return of 5.4%. Over the course of 5 years, this results in a return of 30.08%.

    While the 5.4% is an expected return, we know that actual returns can vary greatly. The first step in building the Monte Carlo model is replacing these fixed returns with randomly distributed values, to better approximate the real world.

    Step 1: Adding Random Data

    In the Monte Carlo model, instead of a fixed 5.4% return, we anticipate that the return will be normally distributed with a mean (average) of 5.4% and a standard deviation of 7.3%. For each return cell in the spreadsheet (column D), we use the random function NormalValue:

    Figure B: Adding random data

    In figure B, the return in each period has been changed from a fixed 5.4% to a randomly distributed return, using the function seen in the function bar. The returns in each period are randomly generated. If you recalculate the model at this step, you will see each return change. The total return (F11) can also differ significantly from the original value (30.08%).

    Randomly-distributed returns seem like a better approximation of the real world, but taking a single random return isn’t useful. The key to using Monte Carlo simulation is to take many random values, recalculating the model each time, and then analyze the results.

    Step 2: Running a Monte Carlo Simulation

    A Monte Carlo simulation calculates the same model many many times, and tries to generate useful information from the results. To run a Monte Carlo simulation, click the “Play” button next to the spreadsheet. (In Excel, use the “Run Simulation” button on the Monte Carlo toolbar).

    The RiskAMP Add-in includes a number of functions to analyze the results of a Monte Carlo simulation. To start, we’ll look at the average results of the simulation using the SimulationMean function.

    Note: the first time you enter these functions in a spreadsheet, you'll see an #N/A error. This is because the simulation hasn't collected data for the cell yet. Once you run a simulation, this error will go away.

    Figure C: Average simulation results

    In Figure C, we’ve added average simulation results in column H using the function seen in the function bar. In this example, cell H11 calculates the average value of cell F11 over all the trials, or iterations, of the Monte Carlo simulation.

    When you run a Monte Carlo simulation, at each iteration new random values are placed in column D and the spreadsheet is recalculated. This results in a different value in cell F11. The Monte Carlo simulation runs hundreds or thousands of times, and at each iteration the RiskAMP Add-in stores and remembers the value of cell F11. Once the simulation is complete, the average value can be calculated from this set of stored values.

    You’ll see that the average value, returned in cell H11, is close to the original fixed value of 30.08% (see cell F11 in Figure A). This is as expected, because the random data we’re using for returns has an average of 5.4%, which was the fixed value in the original model.

    Step 3: Analyzing Data

    As noted above, the average return given by the Monte Carlo simulation is close to the original, fixed model. If that were the only thing we could learn from the simulation, it wouldn’t have much use. However, we can get much more useful information from the Monte Carlo simulation by looking at ranges and percentiles.

    To begin with, we can look at the minimum and maximum values identified during the simulation using the SimulationMin and SimulationMax functions:

    Figure D: Minimum and maximum simulation results

    In Figure D, cell I11 contains the minimum value of cell F11 seen during the simulation. This is significantly worse then the average, and represents the risk contained in the portfolio model. This means that there is some possibility that this portfolio, over 5 years, will wind up with a net loss of 33%.

    Looking at the absolute minimum and maximum values tends to overstate the outliers, or tails, of the possible outcomes of the portfolio model. We can also look at percentile probabilities, using the SimulationPercentile function:

    Figure E: Percentile results

    In Figure E, cell I11 contains the SimulationPercentile function as seen in the function bar. To understand what the percentiles mean, imagine that we take every result seen in cell F11 over the Monte Carlo simulation, and place them in order (lowest to highest). The first value would be the minimum, as seen above; no values in the results are lower than the minimum value. The last value would be the maximum; 100% of the values in the results are equal to, or lower than, the last value. Therefore the maximum value is the 100th Percentile.

    The 25th Percentile, then, represents a value that is equal to or higher than 25% of the results seen during the simulation. Another way to say it is, there is a 25% chance that any value in the simulation will be lower to or equal to this amount; and at the same time, there is a 75% chance that any value in the simulation will be higher than or equal to this amount.

    In Figure E, cell I11 shows the 25th Percentile result for cell F11. This tells us that, over the simulation, 75% of the time the value of F11 is higher than or equal to 16.61%. Or, there is a 75% chance that our model will have a total return of 16.61% or higher.

    By changing the percentile values, we can determine the expected return of the portfolio with different probabilities. This kind of analysis can be useful in determining the real levels of risk associated with an investment portfolio.

    Step 4: Determining Confidence Levels

    Instead of finding the expected return at different percentiles, we can turn the analysis around and find the probability of reaching a particular target return with the SimulationInterval function:

    Monte Carlo Simulation For Mac Free

    Figure F: Confidence Levels

    Monte Carlo Simulation Formula

    This analysis answers the question, what is the likelihood, over all trials of the simulation, that the model will return at least 50%? In Figure F, this probability is about 16%; that is, there is a 16% probability the model will result in a return of 50% or more*.

    This kind of analysis can be useful in determining confidence levels. For example, in evaluating alternative investments, we can compare the probabilities of reaching certain minimum returns. Or we can understand the probability of loss associated with an investment by finding the likelihood that it will return 0%.

    Monte Carlo Simulation For Mac Excel

    * Note that the precise figure will change every time you run a simulation, but in this model it will always be around 16%.

    Conclusion

    The above discussion describes converting a simple fixed portfolio model into a Monte Carlo simulation, and the kinds of analysis that can be done with a Monte Carlo simulation. This is a very simple example; many different analysis functions are available, and there are many different ways to generate random data in a model. See the RiskAMP Help Manual (available in Excel or from the Start Menu) for more information about the various functions.

    Of course any analysis is only as good as the model and the data that are entered. This model is very simple in that it ignores investment costs and inflation. The model is also very sensitive to the mean and standard deviation of our expected return.

    Monte Carlo Simulation For Capital Budgeting

    Nevertheless, it’s evident from the analysis that the simple fixed model hides much of the risk associated with the portfolio. By using a Monte Carlo simulation, and with some basic analysis of the results, we have a lot more detailed information about the possible outcomes of this portfolio.