Black Scholes and Monte Carlo implementations in Java [duplicate]

Question

Possible Duplicate:
Is there an all Java options-pricing library (preferably open source) besides jquantlib?

Can anyone recommend a library with an implementation of Black Scholes and Monte Carlo in Java? Ideally something that is Java only and doesn't require a C++ dll or .so or .lib etc..

I've posted a similar question about open source and the choices appear to be quite limited thus far:

Quantlib - C++ using SWIG or similar to communicate with JVM JQuantLib - port of QuantLib to pure Java, but can't access site for 2 days now. finmath.net - Appears to be all java and has promise, but finding problems running applets using it.

Presently, this is for pricing very simple vanilla euro-style options. But must be open to the complexity that's sure to come prob. requiring Monte Carlo or other pricing models.

Thanks in advance.

Answer 1

This one is in C#, but it could help you create yours in Java: Divergence issue with my monte carlo pricer...

using System;
using System.Threading.Tasks;
using MathNet.Numerics.Distributions;
using MathNet.Numerics.Random;

namespace MonteCarlo
{
    class VanillaEuropeanCallMonteCarlo
    {
        static void Main(string[] args)
        {
            const int NUM_SIMULATIONS = 10000000;
            const decimal strike = 50m;
            const decimal initialStockPrice = 52m;
            const decimal volatility = 0.2m;
            const decimal riskFreeRate = 0.05m;
            const decimal maturity = 0.5m;
            Normal n = new Normal();
            n.RandomSource = new MersenneTwister();


            VanillaEuropeanCallMonteCarlo vanillaCallMonteCarlo = new VanillaEuropeanCallMonteCarlo();

            Task<decimal>[] simulations = new Task<decimal>[NUM_SIMULATIONS];

            for (int i = 0; i < simulations.Length; i++)
            {
                simulations[i] = new Task<decimal>(() => vanillaCallMonteCarlo.RunMonteCarloSimulation(strike, initialStockPrice, volatility, riskFreeRate, maturity, n));
                simulations[i].Start();
            }

            Task.WaitAll(simulations);

            decimal total = 0m;

            for (int i = 0; i < simulations.Length; i++)
            {
                total += simulations[i].Result;
            }

            decimal callPrice = (decimal)(Math.Exp((double)(-riskFreeRate * maturity)) * (double)total / (NUM_SIMULATIONS * 2));

            Console.WriteLine("Call Price: " + callPrice);
            Console.WriteLine("Difference: " + Math.Abs(callPrice - 4.744741008m));
        }


        decimal RunMonteCarloSimulation(decimal strike, decimal initialStockPrice, decimal volatility, decimal riskFreeRate, decimal maturity, Normal n)
        {
            decimal randGaussian = (decimal)n.Sample();
            decimal endStockPriceA = initialStockPrice * (decimal)Math.Exp((double)((riskFreeRate - (decimal)(0.5 * Math.Pow((double)volatility, 2))) * maturity + volatility * (decimal)Math.Sqrt((double)maturity) * randGaussian));
            decimal endStockPriceB = initialStockPrice * (decimal)Math.Exp((double)((riskFreeRate - (decimal)(0.5 * Math.Pow((double)volatility, 2))) * maturity + volatility * (decimal)Math.Sqrt((double)maturity) * (-randGaussian)));
            decimal sumPayoffs = (decimal)(Math.Max(0, endStockPriceA - strike) + Math.Max(0, endStockPriceB - strike));
            return sumPayoffs;
        }
    }
}

Answer 2

You could try to see if NAG could suit your needs as I suggested in this post.

It's not free, but I think it's a pretty good tool to tackle problems of any complexity.

I haven't personally tested it though.