Normal Distribution Best Approach

Question

I'm trying to build a simple program to price call options using the black scholes formula http://en.wikipedia.org/wiki/Black%E2%80%93Scholes. I'm trying to figure our the best way to get probabilities from a normal distribution. For example if I were to do this by hand and I got the value of as d1=0.43 than I'd look up 0.43 in this table http://www.math.unb.ca/~knight/utility/NormTble.htm and get the value 0.6664.

I believe that there are no functions in c or objective-c to find the normal distribution. I'm also thinking about creating a 2 dimensional array and looping through until I find the desired value. Or maybe I can define 300 doubles with the corresponding value and loop through those until I get the appropriate result. Any thoughts on the best approach?

Answer 1

You need to define what it is you are looking for more clearly. Based on what you posted, it appears you are looking for the cumulative distribution function or P(d < d1) where d1 is measured in standard deviations and d is a normal distribution: by your example, if d1 = 0.43 then P(d < d1) = 0.6664.

The function you want is called the error function erf(x) and there are some good approximations for it.

Apparently erf(x) is part of the standard math.h in C. (not sure about objective-c but I assume it probably contains it as well).

But erf(x) is not exactly the function you need. The general form P(d < d1) can be calculated from erf(x) in the following formula:

P(d<d1) = f(d1,sigma) = (erf(x/sigma/sqrt(2))+1)/2

where sigma is the standard deviation. (in your case you can use sigma = 1.)

You can test this on Wolfram Alpha for example: f(0.43,1) = (erf(0.43/sqrt(2))+1)/2 = 0.666402 which matches your table.

There are two other things that are important:

1. If you are looking for P(d < d1) where d1 is large (greater in absolute value than about 3.0 * sigma) then you should really be using the complementary error function erfc(x) = 1-erf(x) which tells you how close P(d < d1) is to 0 or 1 without running into numerical errors. For d1 < -3*sigma, P(d < d1) = (erf(d1/sigma/sqrt(2))+1)/2 = erfc(-d1/sigma/sqrt(2))/2, and for d1 > 3*sigma, P(d < d1) = (erf(d1/sigma/sqrt(2))+1)/2 = 1 - erfc(d1/sigma/sqrt(2))/2 -- but don't actually compute that; instead leave it as 1 - K where K = erfc(d1/sigma/sqrt(2))/2. For example, if d1 = 5*sigma, then P(d < d1) = 1 - 2.866516*10^-7

2. If for example your programming environment doesn't have erf(x) built into the available libraries, you need a good approximation. (I thought I had an easy one to use but I can't find it and I think it was actually for the inverse error function). I found this 1969 article by W. J. Cody which gives a good approximation for erf(x) if |x| < 0.5, and it's better to use erf(x) = 1 - erfc(x) for |x| > 0.5. For example, let's say you want erf(0.2) &approx; 0.2227025892105 from Wolfram Alpha; Cody says evaluate with x * R(x²) where R is a rational function you can get from his table.

If I try this in Javascript (coefficients from Table II of the Cody paper):

 // use only for |x| <= 0.5
 function erf1(x)
 {
    var y = x*x;
    return x*(3.6767877 - 9.7970565e-2*y)/(3.2584593 + y);
 }

then I get erf1(0.2) = 0.22270208866303123 which is pretty close, for a 1st-order rational function. Cody gives tables of coefficients for rational functions up to degree 4; here's degree 2:

 // use only for |x| <= 0.5
 function erf2(x)
 {
    var y = x*x;
    return x*(21.3853322378 + 1.72227577039*y + 0.316652890658*y*y)
      / (18.9522572415 + 7.8437457083*y + y*y);
 }

which gives you erf2(0.2) = 0.22270258922638206 which is correct out to 10 decimal places. The Cody paper also gives you similar formulas for erfc(x) where |x| is between 0.5 and 4.0, and a third formula for erfc(x) where |x| > 4.0, and you can check your results with Wolfram Alpha or known erfc(x) tables for accuracy if you like.

Hope this helps!