I’ve introduced the Asian Option before, it is similar to a vanilla option but its payoff is based on the average price over a period of time, rather than solely the price at expiry. We saw that it was exotic – it is not possible to give it a unique price using only the information available from the market.

Today I’m going to introduce another type of option – the geometric asian option. This option is similar, but its payoff is now based on the geometric average, rather than the arithmetic average, of the spot over the averaging dates

This option is exotic, just like the regular (arithmetic-average) asian option. At first sight it seems even more complicated; and what’s more, it is almost never encountered in practice, so why bother with it? Well, it’s a very useful as an exercise because in many models where the arithmetic asian’s price has no closed form, the geometric asian happens to have one! Further, since an arithmetic average is ALWAYS higher than a geometric average for a set of numbers, the price of the geometric asian will give us a strict lower bound on the price of the arithmetic asian.

Considering the Black-Scholes model in its simplest form for the moment (although the pricing formula can be extended to more general models), let’s consider what the spot will look like at each of the averaging times, and as we did in the earlier post, considering a simple geometric asian averaging over only two times and so that the payoff is

At , . At ,

where . At ,

where also, and importantly is uncorrelated with due to the independent increments of the Brownian motion.

Now the reason we couldn’t make a closed form solution for the arithmetic asian was that is the sum of two lognormal distributions, which itself is NOT lognormally distributed. However, as discussed in my post on distributions, the product of two lognormal distributions IS lognormal, so valuing an asian that depends on the product of these two is similar to pricing a vanilla, with a slight change to the parameters that we need

where is another normal variable (we’ve used the result about the sum of two normally distributed variables here).

If we re-write this as

where and are the forwards at the respective times and and are defined below. This is just the same as the vanilla pricing problem solved here. So, we can use a vanilla pricer to price a geometric asian with two averaging dates, but we need to enter transformed parameters

In fact this result is quite general, we can price a geometric asian with any number of averaging dates, using the general transformations below (have a go at demonstrating this following the logic above)

I may be overthinking but would like to clarify the following with respect to pricing a geometric Asian, specifically on how the simulation should be coded into excel

For Geometric, lets say I have 100 timesteps between inception till maturity of the option, instead of taking the 100.root(S1.S2.S3.etc…S100), I simulate the stock price in each timesetp by taking the Ln of the Black Scholes:

LnS1 = LnS0 +(r – 0.5sigma.sq)dt + sigma*sqrt(dt)*x

Now if if were to code this in excel, technically I would only have to take Ln on S0??? and the rest of the stock prices from S1 to S100 should be just based on:

Let S0 = Ln100 (Ln of stock price at S0),

therefore, S1 = S0 +(r – 0.5sigma.sq)dt + sigma*sqrt(dt)*x