Hi, I'm Marcin, technical support engineer here at NAG and I've recently encountered an interesting problem. I coauthored a recent paper on option pricing with the NAG Library (Extending, imitating and collaborating). Now it's time for the next step.
While working on a demonstration of NAG random number generators in Excel and their application in option pricing using the Monte Carlo method I noticed a considerable discrepancy in results while using unscrambled Sobol quasirandom numbers. Well, to start with, how do you price a European option when the underlying asset follows geometric Brownian motion? Basically for each random number you simulate what the price of the asset will be in, say T=1 year, moving in one step from the starting time point t=0 to the end point t=T, calculate the payoff, and the average value of all payoffs will be our desired option price (Monte Carlo methods for option pricing). I'm not going to get into the details of Monte Carlo method, that will be covered in a paper I'm currently working on. The idea is that this simulation works very well and gives good convergence to the result obtained using the close-form formula, no surprises here. The stock prices have log-normal distribution, as expected. This is illustrated in the figure below. This histogram groups the stock prices from 20000 simulations.
One could however want to price Asian options, which are path-dependent. The payoff of such options depends not on the final price of the asset (as in European options), but on the average price of the asset throughout the life of the option. We therefore need to generate a path of stock prices, so we'll move from t=0 to t=T not in one step as previously, but in many steps, say 252 (this is the average number of trading days per year). In practice we generate 252 random numbers, generate a path of the asset price and for the final step we calculate the payoff. This is done for every Monte Carlo simulation, so as you've probably noticed we need 252 times more random numbers than in case of the European option. The quantity is not an issue, the Sobol sequence is long enough. What happens is that in case of such pathwise Monte Carlo simulation the distribution is not log-normal, it doesn't actually resemble any distribution, as can be seen below.
Why is that? Why do the quasirandom numbers fail? The simulation using the same data, but pseudorandom numbers instead of quasirandom, does return stock prices that are log-normally distributed (below).
What is going on here? Well, I know the answer, it took me some time to realise, and it would have taken more time if not the help of my colleagues Jacques and Robert. Do you, dear reader of this blog, know why it is like this? Is this just an easter egg, or an interesting property of quasirandom numbers? Happy Easter!