My experience learning ocaml has been pretty enjoyable so far. A friend told me about Jason Hickey's pdf book Introduction to the Objective Caml Programming Language which is commendably brief and has really helped as I go on to try ocaml further. The second thing that has helped is that I have discovered rlwrap, so the toploop is no longer such an unfriendly place to be.
Furthermore, I have split the pricer that I began developing yesterday into a number of files and build them into a single executable using ocamlc. First, gaussian.ml, containing the random number functions:
open Random;; (* initialize the random number generator *) Random.self_init;; (* get a random gaussian using a Box-Muller transform, described * here http://en.wikipedia.org/wiki/Box-Muller_transform *) let rec get_one_gaussian_by_box_muller () = (* Generate two uniform numbers from -1 to 1 *) let x = Random.float 2.0 -. 1.0 in let y = Random.float 2.0 -. 1.0 in let s = x*.x +. y*.y in if s > 1.0 then get_one_gaussian_by_box_muller () else x *. sqrt (-2.0 *. (log s) /. s) ;; (* get a gaussian through oversampling and subtraction *) let get_one_gaussian_by_summation () = let rec add_one limit count so_far = if count==limit then so_far else add_one limit (count+1) (so_far +. (Random.float 1.0)) in (add_one 12 0 0.0) -. 6.0 ;; let get_one_gaussian = get_one_gaussian_by_box_muller
I added the summation method because when I first tried the pricer on real data the numbers were hopeless (now they are just somewhat out of line with market observables), and I suspected a bug in my random numbers. I was correct, I did have incorrect random numbers.
Then I have payoff.ml, containing my payoff functions. I have added a few more simple payoffs, and moved to named function arguments:
(** a vanilla option pays off the difference between the spot price ** and the strike, or expires worthless *) let call ~strike ~spot = max (spot -. strike) 0.0;; let put ~strike ~spot = max (strike -. spot) 0.0;; let digital payoff = if payoff> 0.0 then 1.0 else 0.0;; let digital_call ~strike ~spot = digital (call ~strike:strike ~spot:spot);; let digital_put ~strike ~spot = digital (put ~strike:strike ~spot:spot);; (** A double digital pays 1 if spot is between two barriers, zero ** otherwise *) let double_digital ~low ~high ~spot = assert (low < high); if (low <= spot && spot <= high) then 1.0 else 0.0;;
mc1c.ml contains the actual Monte Carlo simulator, and it is unchanged except to use named function arguments, and to qualify the name of the get_one_gaussian function, which is now in a seperate file:
(* Price an option with a flexible payoff using Monte Carlo. *) let sim ~payoff ~expiry ~spot ~vol ~r ~num_paths = let variance = vol *. vol *. expiry in let root_variance = sqrt variance in let ito_correction = -0.5 *. variance in let moved_spot = spot *. exp (r *. expiry +. ito_correction) in let rec do_path i running_sum = if i < num_paths then begin let this_gaussian = Gaussian.get_one_gaussian () in let this_spot = moved_spot *. (exp (root_variance *. this_gaussian)) in let this_payoff = payoff ~spot:this_spot in do_path (i+1) (running_sum +. this_payoff) end else (running_sum /. (float_of_int num_paths)) *. (exp (-1.0 *. r *. expiry)) in do_path 0 0.0 ;;
Finally I have my test file which runs the test cases. Now that I use named function args I can partially-apply function args in any order, so I make a test harness that sets up a particular marketdata scenario and runs the pricer:
let print_mc ?(num_paths=100000) label payoff = let mc payoff = Mc1c.sim ~payoff:payoff ~expiry:0.2 ~spot:161.3 ~vol:0.35 ~r:0.045 ~num_paths:num_paths in Printf.printf "%s: %f\n" label (mc payoff) ;; print_mc "call" (Payoff.call ~strike:160.0);; print_mc "digital call" (Payoff.digital_call ~strike:160.0);; print_mc "put" (Payoff.put ~strike:170.0);; print_mc "digital put" (Payoff.digital_put ~strike:170.0);; print_mc "double digital" (Payoff.double_digital ~low:160.0 ~high:170.0) ~num_paths:250000;; (* price one option, test the payoff against a target price and * print the result *) let test_mc ?(num_paths=1000) ?(expiry=1.0) ?(r=0.0) label payoff target = let mc payoff = Mc1c.sim ~payoff:payoff ~expiry:expiry ~spot:161.3 ~vol:0.35 ~r:r ~num_paths:num_paths in let price = mc payoff in let tolerance = 0.00001 in Printf.printf "Test %s - price: %f target: %f\n" label price target; assert( abs_float(price-.target) < tolerance ) ;; test_mc "Spot to one" (fun ~spot->1.0) 1.0;; test_mc "Spot to zero" (fun ~spot->0.0) 0.0;; let r = 0.05 in let npv = exp(-1.0 *. r) in test_mc "Spot to one with rates" (fun ~spot->1.0) npv ~r:r;; (* vim: set sw=4 ts=4 expandtab: *)
This is pretty cool. As you can see, I made the number of mc paths an optional parameter. I have added a few assertions that trivial payoff functions price correctly and that discounting works.
Now I build an object from each ml file using ocamlc and then compile them all to a built object at the end. You need to be careful to do them all in the correct order where you have functions defined in one file, called in another, as we have here.
permalink Updated: 2007-06-13