Monte Carlo simulations of correlated stocks by Geometric Brownian motion

I am trying to simulate using a Geometric Brownian Motion process three autocorrelated stocks.

In particular, I need to simulate three different matrices with 1000 scenarios each using a Monte Carlo technique. How could I simulate them in order to be autocorrelated using R Studio?

I saw some posts where it is suggested to use the function mvrnorm() but that it is applied in the generation of a single matrix where the different rows of the matrix are autocorrelated each other. I am looking for a solution where I need to simulate three different matrices that are autocorrelated to each other.

• Thinking “Principal Component Analysis”. you – demully Oct 15 '20 at 21:02
• Hi: You need to explain what you mean by one matrix being correlated to another. I've never heard of such a thing but Ithat doesn't mean that it can't exist. Usually, what are correlated as far as I know are vector RV's or scalar RV's. – mark leeds Oct 15 '20 at 23:01
• what do you mean by matrices? Matrices of what – develarist Oct 16 '20 at 23:17
• what do you mean by "three autocorrelated stocks". don't you mean "three correlated stocks"? – develarist Oct 17 '20 at 4:24

Let $$n$$ be the number of stocks (here $$n=3$$)

Let $$T$$ be the number of sequential returns to generate (for example $$T=12$$ if you want to generate a year's worth of monthly returns)

Let $$M$$ be the number of alternative scenarios to generate (for example $$M=1000$$ to generate 1000 different outcomes)

Then,

Step 1. You generate a $$n \times T$$ matrix RETS of random correlated returns using mvrnorm()

Step 2. From the RETS you generate a $$n \times T$$ matrix PRICES by assuming an initial price of 100 for each stock and applying the formula prices(i,t)=prices(i,t-1)*(1+rets(i,t))

Step 3. We have generated one set of correlated outcomes. We append the first row of PRICES to PRICE_OUTCOMES_A, the second row to PRICE_OUTCOMES_B and the third row to PRICE_OUTCOMES_C. If these three matrices already have $$M$$ or more rows, we STOP, else we go back to Step 1 to generate another scenario.

At the end the 3 "price outcome matrices" (one matrix for each stock) will be $$M$$ by $$T$$, and each row will have the desired return correlation to the corresponding row of the other matrices.

• price outcome matrices? – develarist Oct 16 '20 at 23:17
• Those are the "three different matrices" that the OP requested, each keeping track of price performance of one of the stocks (stock A, stock B, stock C) being simulated. – noob2 Oct 17 '20 at 9:46
• And the same process could work also for different processes like mean-reverting Vasicek one? – Mark Marconi Oct 18 '20 at 9:07