Stock Monte Carlo Simulation using GBM in R (more efficient code)

Question

I am running a MC simulation on around 500 stocks in order to generate 252 days of stock prices. I am using the function GBM from the sde package.

For this I have a data.frame of 500 stocks that give me the average volume, average return, daily volatility and Price as the example bellow :

Teste=data.frame(matrix(runif(25), nrow=5, ncol=4))
colnames(Teste) <- c("AVG_VOLUME","AVG_RETURN","VOL","PRICE")

          AVG_VOLUME AVG_RETURN    VOL         PRICE      
 Stock 1  0.7028197  0.09264265    0.002169411 100 
 Stock 2  0.7154557  0.03314615    0.004839466 100 
 Stock 3  0.4038030  0.04347487    0.003441471 100  
 Stock 4  0.5392530  0.06414982    0.004482528 100 
 Stock 5  0.8720084  0.09615865    0.008081017 100

I have also a function that takes as parameters the data.frame above and the number of simulations required :

Monte_Carlo_Stocks <- function(Dados_Acoes,Numero_Simulacoes){
    start.time <- Sys.time()
    MC <- matrix(0,nrow = 253,ncol=nrow(Dados_Acoes))
    for (n in 1:Numero_Simulacoes){
        MC1 <- MC
        MC <- apply(Dados_Acoes,1,function(x) {
            GBM(x["PRICE"],0,x["VOL"]*100,1,252) 
        })
        MC <- MC/Numero_Simulacoes
        MC <- MC+MC1
    }
    end.time <- Sys.time()
    time.taken <- end.time - start.time
    print(time.taken)
    return(MC)
} 

The function takes the data on each line of the matrix and pass it as parameters values to the GBM function, then I average the results in order to return a matrix of the averaged stock price simulation for the 500 stocks in 252 days.

I am using 0 drift, but i can also pass the avarage return as the second parameter on GBM

This is giving me the result that I expect, but I was wondering if there is a more efficient way to do it.

Parameters are in Portuguese :

Dados_Acoes = Stock Data

Numero_Simulacoes = Number of Simulations

Any Toughts ?