Data Analysis & Simulation

Because risk and uncertainty are a part of literally all areas of our life, with the finance being one of the most important areas, scientifically based risk management methods are gaining more and more popularity among the finance industry professionals. Currency fluctuations affect all businesses dealing with multiple currencies, so having at least some degree of certainty about the future exchange rates can be a significant success factor for any international enterprise. A wide range of currency forecasting methods have been developed, however, not many of them can pretend to be reliable in the long run: most algorithms only work for a short period of time, and need to be tweaked as the market conditions change.

Brijen Hathi, a Research Fellow at the Planetary & Space Sciences Research Institue, performs his own research in the field and publishes the results in the Currency Forecasting Blog. The forecasting methodology employed by Mr. Hathi is in part based on the same techniques used in probabilistic risk analysis. Like with most modern forecasting methods, in this approach, he uses historical data to predict the future, but the big difference here is that he also assigns specific probabilities to the predictions. For example, for a US-based company doing business in the UK, it doesn’t really matter what the exact GBP/USD exchange rate is going to be during the next 30 days, as long as it stays within a specific interval with a high probability (95% or more). Recently Mr. Hathi has published an article highlighting the use of EasyFit to model pricing probability of the Pound Sterling versus the US Dollar from historical data. It is fascinating to see how EasyFit is being used in (what we believe) a truly scientific approach to data analysis, and we hope to see new developments in this area soon.

The beta testing of our new product, the Simulation & Probabilistic Analysis SDK, is now over, and we want to thank our beta testers for their effort and valuable feedback. One of the most exciting things is that during the beta testing phase, we have not detected any bugs in the SDK, indicating the initial high quality of the product.

The production version of the SDK is now available for public download, so if you are a software developer and need to add distribution fitting or simulation features to your software with no hassle, feel free to download the fully functional version of the SDK and try it free for 30 days.

Another good news is that the Christmas is coming, so we decided to make a gift to software developers who are considering to purchase the SDK: until the end of December, the SDK Developer License can be ordered at a $500 discount – click here details.

The new Software Development Kit enabling you to easily add Monte Carlo simulation and distribution analysis features to your applications is now available for public beta testing – please download the free beta version and take a look at the code examples (available in several languages, including C#, VB.NET, C++, and Visual Basic for Applications).

We would be glad to receive any feedback, questions or suggestions from you, so please feel free to drop us a line and let us know what you think about this product and how we can make it better.

On April 17 2005, the Millstone nuclear generating plant in Connecticut shut down when a circuit board monitoring a steam pressure line short-circuited. “Tin whiskers” – microscopic growths of the metal from soldering points on a circuit board – were blamed for causing the problem. These whiskers are comprised of nearly pure tin, and are therefore electrically conductive.

Field failures attributable to tin whiskers have cost individual programs many millions of dollars each. As a result, manufacturers of high-reliability systems are forced to use Monte Carlo simulation models to decide whether the use of tin poses an acceptable risk in a given application.

Recently a group of NASA scientists lead by Karim J. Courey, a Principal Engineer with the Orbiter Sustaining Engineering Office, Lyndon B. Johnson Space Center, used our distribution fitting software EasyFit to better understand the underlying process and develop a probability model that can be used to improve existing Monte Carlo risk simulation models… read the full case study

There are many probability distributions developed by statisticians to model random data of different kinds, ranging from business data, finance data (stock prices) to engineering data (system failures) and environmental data (max. flood flows). While the standard Excel package includes some basic statistical functions, its support for probability distributions is very limited and almost useless for real world modelling applications. This article discusses the worksheet functions provided by EasyFitXL, the distribution fitting add-in for Excel that can be applied to perform a range of decision-making calculations using a variety of probability distributions… read the full article

In EasyFit 3.0 – back in 2006 – we introduced StatAssist, the built-in distribution viewer tool that closely integrates with the distribution fitting features of EasyFit. Since then, StatAssist has proven to be quite a useful feature, so we decided to include it into EasyFitXL, our distribution fitting add-in for Excel.

StatAssist displays graphs and other useful properties of all the probability distributions available in EasyFit. Even though it has initially been designed as an essential part of EasyFit, StatAssist can be used as a separate application – for example, to take a quick look at a distribution curve, or to calculate the distribution statistics… read the full article

Excel has become the de facto standard application for data analysis and presentation across a variety of industries, so if you deal with random data of any kind, chances are your data is stored in Excel workbooks. However, analyzing probability data in Excel can be tricky as the standard Excel package includes no facilities for fitting probability distributions to data. That is when EasyFitXL, the distribution fitting add-in for Excel, comes in handy… read the full article

Since 1991, the European Union has been promoting the use of numerical models to assess the environmental fate and risk of pesticides. Recently a group of scientists from the Catholic University and the Marche Polytechnic University (Italy) in association with Informatica Ambientale, the Milan-based research and computer science company, developed a tool that integrates one of the pesticide fate models with GIS software. Several distribution fitting software products were tested to introduce distribution functions in the risk assessment study, and EasyFit was selected as the most appropriate tool for analyzing annual mean pesticide concentration and determining the most suitable distribution… read the full case study

We are pleased to announce that a new version of EasyFit is now available for download.

EasyFit and EasyFitXL (distribution fitting add-in for Excel) now include support for 15 new probability distributions used in economics, finance, actuarial science, hydrology, communications theory, mechanical engineering, and other fields. To learn more about new features in this release, click here.

Upgrading From Previous Versions

If you purchased EasyFit on 23 October 2007 or later, you are entitled to a free upgrade, otherwise you qualify for a discounted upgrade. In either case, please contact us for upgrade information.

It’s been a while since we updated our blog – that’s because we have been working on a new version of EasyFit planned to be released later in October 2008. Below is a quick summary of new features in EasyFit.

(Note: The price for the new version is likely to increase, but since all licenses come with one year of free updates, you can still order EasyFit at the current price this month and upgrade to the upcoming new version for free.)

New Probability Distributions

EasyFit will include 15 new distributions:

The Burr (Singh-Maddala), Dagum (Inverse Burr), Pareto Type II (Lomax), and Log-Gamma distributions are mainly used in economics and actuarial science. We have had numerous requests for these models from some of our customers – actuaries and finance industry professionals.

The Generalized Gamma distribution is an advanced 4-parameter model including the Gamma, Weibull, and Half-Normal distributions as special cases.

The Pearson Type V (Inverse Gamma) and Pearson Type VI (Beta Prime) distributions are a part of the Pearson family of distributions often used in financial applications.

The Levy distribution is one of the few distributions that are stable and that have probability density functions that are analytically expressible, the others being the Normal distribution and the Cauchy distribution.

The Log-Pearson III (often referred to as “LP3″) distribution is the standard model recommended by the U.S. Water Resources Council for use by all U.S. Government agencies for flood frequency analysis.

The Nakagami (Nakagami-m) and Rice (Nakagami-n) distributions are used in communications theory to model signal fading under specific conditions.

The Kumaraswamy distribution is used in electrical, civil, mechanical, and financial engineering applications dealing with variables that are lower and upper bounded.

Some other new models include the Hyperbolic Secant, Error (Exponential Power), and Reciprocal distributions.

New Excel Functions

In EasyFit 4.0, we added the ability to analyze probability data in Excel, which has proven to be quite a useful feature. EasyFit 5.0 (Professional Edition) extends this functionality by introducing several new functions making it even easier to use distributions in your worksheet models.

The DistFit function allows to fit a specified distribution to your data and display the resulting model right in a worksheet. This function can be useful if you already know the underlying distribution (e.g. the Normal model) and only need to estimate the distribution parameters. The return value of this function can be supplied to several other new functions such as DistPdf, DistCdf, DistRand etc. For example, specifying

=DistCdf(DistFit(“Normal”; B1:B100); 2)

in a worksheet will fit the Normal distribution to the data in cells B1:B100, and then evaluate the CDF of the resulting Normal distribution at x=2.