- Joined
- 10/12/11
- Messages
- 28
- Points
- 11
Hi All,
I'm trying to calibrate parametres for an Ornstein-Uhlenbeck model:
dx = h (m - x) dt + s dz,
where
h = speed of reversion
m = long-term equilibrium level
s = volatility
So, I created the following timeseries (data from Yahoo!Finance):
LoL.ts <- ts(LoL, start=c(2012,1), frequency=4)
And then I fitted a ar(1)-model
fit.LoL.ts <- ar(LoL.ts, order=1)
Which yielded the following:
> fit.LoL.ts
Call:
ar(x = LoL.ts, order.max = 1)
Coefficients:
1
0.959
Order selected 1 sigma^2 estimated as 0.1269
And
> fit.LoL.ts$x.mean
V1
3.751205
The way I interpret these results is that:
h = 0.959
m = 3.751205
s^2 = 0.1269
And that would give me the following Ornstein-Uhlenbeck process:
dx = 0.959 * (3.7512 - x) dt + 0.3562dz
Is that correct?
Thanks
I'm trying to calibrate parametres for an Ornstein-Uhlenbeck model:
dx = h (m - x) dt + s dz,
where
h = speed of reversion
m = long-term equilibrium level
s = volatility
So, I created the following timeseries (data from Yahoo!Finance):
LoL.ts <- ts(LoL, start=c(2012,1), frequency=4)
And then I fitted a ar(1)-model
fit.LoL.ts <- ar(LoL.ts, order=1)
Which yielded the following:
> fit.LoL.ts
Call:
ar(x = LoL.ts, order.max = 1)
Coefficients:
1
0.959
Order selected 1 sigma^2 estimated as 0.1269
And
> fit.LoL.ts$x.mean
V1
3.751205
The way I interpret these results is that:
h = 0.959
m = 3.751205
s^2 = 0.1269
And that would give me the following Ornstein-Uhlenbeck process:
dx = 0.959 * (3.7512 - x) dt + 0.3562dz
Is that correct?
Thanks