Using simulation
p <- 100
sd <- 0.2
X <- 90
N <- 10
n <- 1000000
hit <- double(n)
for (i in 1:n) {
hit[i] <- as.numeric(any(p * exp(cumsum(
rnorm(N, sd = sd / sqrt(250))
)) < X))
}
sum(hit)/n
Instead of using rnorm you may want to use e.g. rt() or an (G)ARCH
process or...
Best Adrian
On 25.11.2015 12:00, r-sig-finance-requ...@r-project.org wrote:
> Message: 1
> Date: Tue, 24 Nov 2015 18:27:19 -0600
> From: Ernest Stokely <wizardc...@gmail.com>
> To: R-SIG-Finance@r-project.org
> Subject: [R-SIG-Finance] Computing stop probability
> Message-ID: <ca1cb4f2-72e7-45b6-a124-a12bdab33...@gmail.com>
> Content-Type: text/plain; charset=us-ascii
>
> Maybe a naive question but given the price and SD of an asset, is there a way
> to calculate the probability of hitting a stop set at X over the next N days?
> I know making appropriate assumptions, this is a Wiener process but can't
> find the correct equation.
>
> A) Is there a closed form solution for this?
> B) Is there an R function related to this?
>
> Ernie
>
> Sent from my iPhone
--
Dr. Adrian Trapletti
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CH-8610 Uster
Switzerland
Phone : +41 (0) 44 994 56 30
Mobile : +41 (0) 79 103 71 31
Email : adr...@trapletti.org
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