Showing posts with label financial. Show all posts
Showing posts with label financial. Show all posts

Friday, September 26, 2008

Financial Contracts, Haskell and Probability

This article brings together the ideas presented in the paper 'How to write a financial contract' (HWFC) and Martin Erwig's FPF module.

We are going to deal with a simple but common situation in finance - if I have a contract where I am going to receive $100 dollars in 3 years time what is that 'contract' worth to me now. How much would I pay to obtain that contract? In order to calculate the worth we need to consider what else I would do with the money and the most obvious action is to deposit it into a bank account that attracts interest.

The question is reposed then as: if I put x into a bank account then what is x if the final amount after 3 years is $100. This is easy if the interest is fixed, not so easy if it varies.

This blogpiece will provide a fragment of the implementation of HWFC that answers the above.

As this is literate Haskell some preliminaries:

> module Main where
>
> import Probability

HWFC introduces the concept of a value process which is a function from time to a random variable. We shall equate a random variable with a probability distribution and a definition of a value process is:

> type PR a = Int -> Dist a

For our interest rate model let us say that from year to the next the interest rate can either stay the same, increase by 1% or decrease by 1% all with equal likelihood. We can express this as:

> interest :: Floating a => a -> PR a
> interest i n = (n *. one) i where one start = uniform [start+1/100,start,start-1/100]

The *. function allows us to repeat a random process n times. The process here is to start with an interest rate and to move to the next years rate.

If this year the rate is 10%, after a couple of years the distribution looks like:

interest 10 2
10.0 33.3%
9.99 22.2%
10.01 22.2%
9.98 11.1%
10.02 11.1%

Let us put that to one side and look at the contracts side of things. I will short circuit the approach in the paper and dive directly into the valuation

> data Obs a = O { evalObs :: PR a }
>
> konst k = O (\t -> certainly k)
> lift f (O pr) = O (\t -> fmap f (pr t))
> lift2 f (O pr1) (O pr2) = O (\t -> joinWith f (pr1 t) (pr2 t))
> date = O (\t -> certainly t)
>
> data Contract = C { evalContract :: PR Float }
> cconst k = C $ \ _ -> certainly k
> when o c = C $ disc (evalObs o) (evalContract c)
>
>
> at t = lift2 (==) date (konst t)
> zcb t x = when (at t) x
>
> whenFirstTrue :: PR Bool -> Int
> whenFirstTrue prb = f 0 where f i = if prb i == certainly True then i else f (i+1)
>
> baseRate = 10

This is a value process such that if when the first argument is true, return the second otherwise calculated the discounted value of the first argument.

> disc :: PR Bool -> PR Float -> PR Float
> disc prb prd t = if prb t == certainly True then prd t else let s = prd t
> t' = whenFirstTrue prb
> in discount baseRate s (t'-t)
>
> discount :: Floating a => a -> Dist a -> PR a
> discount int final time = let intspread = interest int time
> in joinWith (\i s -> s / (1+i/100)) intspread final
>

Lets start with a trivial example to make sure that things are working as planned

> ex1 = cconst 100

The value of this contract, as a random variable, is:

evalContract ex1 0

100.0 100.0

> ex2 = zcb 3 (cconst 100)

The value of this contact is:

evalContract ex2 0

90.90909 25.9%
90.900826 22.2%
90.91736 22.2%
90.89256 11.1%
90.92562 11.1%
90.88431 3.7%
90.93389 3.7%

The PFP library has a function to provide the expected value which can be ask of a distribution. The expected value of our contract is:

expected $ evalContract ex2 0

90.9091

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Melbourne, Australia
I work for GE in Melbourne Australia. All views do not necessarily represent GE.