- #Excel for mac vba correlation how to#
- #Excel for mac vba correlation code#
- #Excel for mac vba correlation series#
After all, if one stock falls, you don’t want your other investments to drop like a stone as well. Well, a diverse portfolio contains uncorrelated assets – in other words, assets that don’t go up and down in tandem. But what do we mean by a diverse portfolio? This can never be stressed too much or too often. It’s an age-old mantra, but investors need to diversify.
#Excel for mac vba correlation code#
Instead we arrange for this information to be contained in a VBA module.Īs far as possible we want this module to contain only user-specified information with a minimum of other code cluttering it up.Learn why investors should know about correlation matrices, and download an Excel-VBA spreadsheet to calculate this important asset allocation tool For a project like this such an approach would be too difficult to implement. Commercial packages use specially designed “random” worksheet functions entered into cells to identify the cells concerned and to specify the distribution to be used.
#Excel for mac vba correlation how to#
Having written functions to generate ‘latin hypercube’ arrays and some inverse distribution functions, we can now put the two together to generate the uncorrelated sample arrays (tomorrow’s post will explain how to make them correlated).įirstly, we need a way for the user to specify the input variables. ' which is a uniformly distributed on the range (i - 1)/n 0
' Returns an array of n random numbers in ascending order, the ith member of Public Function unifun(ByVal n As Long) As Double() The code to implement the function goes something like this: Will have the cumulative distribution function \(F_ & 0 0Īnd the remaining values using a for loop from 2 to n. In symbols, if \(U\) is uniformly distributed on the interval (0, 1) then the random variable \(X\), defined by The inverse transform method is based on the idea that if you take a random variable that is uniformly distributed on the interval (0, 1) and put it into the inverse distribution function of a desired distribution, the resulting random variable will have that distribution. Consequently, this is the method I have chosen to use in this project. This is exactly what we need for ‘latin hypercube’ sampling. The two most general methods, in the sense that they can be made to return distributions with any possible functional form, are the acceptance-rejection method and the inverse transform method, the latter has the further advantage that it can easily be made to return values from equal-probability subdivisions of the range of our desired random variable. Other methods are more general and can produce random numbers conforming to many different distributions.
Most of them are specific to particular distributions with, as you might expect, the normal having the most. We want to convert a random variable that is uniformly distributed on the range [0, 1), as supplied by VBA’s Rnd() function, into a random variable distributed according to any desired distribution. Ways of generating non-uniform random variables As we are trying to develop a tool that is of fairly general applicability, we need a method that will generate samples distributed according to any distribution we may choose. Out there ‘in the wild’ random variables occur in all shapes and sizes, not just the standard ones. Excel and many other applications natively provide a random function that returns values that are uniformly distributed on the range [0, 1). The object of the exercise is to generate an array of random numbers that are distributed according to a given frequency distribution. Today’s post describes how to generate random numbers according to arbitrarily chosen probability distributions using the inverse transform method. It follows on from yesterday’s, where I gave an overview of the problem and how I propose to tackle it.
#Excel for mac vba correlation series#
Welcome back! This is number two in a series of five blog posts that describe how to construct a Monte Carlo risk analysis application in Excel VBA.
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