# JuliaLang/julia

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 ## Test case from Issue #445 #STOCKCORR - The original, unoptimised code that simulates two correlated assets function stockcorr() ## Correlated asset information CurrentPrice = [78. 102.] # Initial Prices of the two stocks Corr = [1. 0.4; 0.4 1.] # Correlation Matrix T = 500 # Number of days to simulate = 2years = 500days n = 100000 # Number of simulations dt = 1/250 # Time step (1year = 250days) Div=[0.01 0.01] # Dividend Vol=[0.2 0.3] # Volatility ## Market Information r = 0.03 # Risk-free rate ## Define storages SimulPriceA = zeros(T,n) # Simulated Price of Asset A SimulPriceA[1,:] = CurrentPrice[1] SimulPriceB = zeros(T,n) # Simulated Price of Asset B SimulPriceB[1,:] = CurrentPrice[2] ## Generating the paths of stock prices by Geometric Brownian Motion UpperTriangle=chol(Corr) # UpperTriangle Matrix by Cholesky decomposition for i = 1:n Wiener = randn(T-1,2) CorrWiener = Wiener*UpperTriangle for j = 2:T SimulPriceA[j,i] = SimulPriceA[j-1,i]*exp((r-Div[1]-Vol[1]^2/2)*dt+Vol[1]*sqrt(dt)*CorrWiener[j-1,1]) SimulPriceB[j,i] = SimulPriceB[j-1,i]*exp((r-Div[2]-Vol[2]^2/2)*dt+Vol[2]*sqrt(dt)*CorrWiener[j-1,2]) end end return (SimulPriceA, SimulPriceB) end
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