Stock Price Simulation R code - Slow - Monte Carlo

问题

I need to perform a stock price simulation using R code. The problem is that the code is a little bit slow. Basically I need to simulate the stock price for each time step (daily) and store it in a matrix.

An example assuming the stock process is Geometric Brownian Motion

for(j in 1:100000){
    for(i in 1:252){
        S[i] <- S[i-1]*exp((r-v^2/2)*dt+v*sqrt(dt)*rnorm(1))
    }
    U[j,] <- S
}

Any suggestion to improve and speed up the code?

回答1:

Assuming S[0] = 1, you can build U as a follows:

Ncols <- 252

Nrows <- 100000

U <- matrix(exp((r-v^2/2)*dt+v*sqrt(dt)*rnorm(Ncols*Nrows)), ncol=Ncols, nrow=Nrows)

U <- do.call(rbind, lapply(1:Nrows, function(j)cumprod(U[j,])))

EDIT: using Joshua's and Ben's suggestions:

product version:

U <- matrix(exp((r-v^2/2)*dt+v*sqrt(dt)*rnorm(Ncols*Nrows)), ncol=Ncols, nrow=Nrows)

U <- t(apply(U, 1, cumprod))

sum version:

V <- matrix((r-v^2/2)*dt+v*sqrt(dt)*rnorm(Ncols*Nrows), ncol=Ncols, nrow=Nrows)

V <- exp( t(apply(V, 1, cumsum)) )

EDIT: as suggested by @Paul:

Execution time for each proposal (using 10000 rows instead of 10^5):

Using apply + cumprod

 user  system elapsed 
0.61    0.01    0.62

Using apply + cumsum

 user  system elapsed 
0.61    0.02    0.63

Using OP's original code

 user  system elapsed 
67.38    0.00   67.52

Notes: The times shown above are the third measures of system.time. The first two measures for each code were discarded. I've used r <- sqrt(2), v <- sqrt(3) and dt <- pi. In his original code, I've also replaced S[i-1] for ifelse(i==1,1,S[i-1]), and preallocated U.

来源：https://stackoverflow.com/questions/15534270/stock-price-simulation-r-code-slow-monte-carlo

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montecarlo