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ok, going to add more in hope that someone could help out.
i use the following log range stochastic volatility model,
Rn = mr + p(Rn-1 - mr) + b*et
where
R is normally distributed with mean mr
Rn-1 last bar log range
mr is mean of log range
p - is persistence parameter ( mean reg. coeff )
Rn - current bar log range
et - normally distributed innovations ( disturbances ) N[0,1]
b - innovation std parm
this is a near gaussian log model with three parameters:
mr, p and b. mr need not to be estimated.
1. i need to be able to estimate p and b with mr known.
2. i need to be able to estimate b only with p and mr known.
the second case is more important to me...
from what i can infer, in case 1. the best estimator for this model
is quasi-maximum likelihood estimator or straight max.
likelihood estimator. however in case 2. i am not sure that i need
mle/qmle for that since i only need to estimate one parm, might be
able to accomplish that with transformation.
so,
i need to be bounce a few mainly math questions, ideas with someone
who knows mle or qmle or stoch volatility models parameter estimation
( similar to arch, garch ). qmle uses kalman but i got some questions
on that technique.
thanks. hope someone out there can help out a bit.
bilo.
ps. this is not garch p,q type model.
> working on adaptive entries and exits for
> a trading system i ran into a wall on
> parameter estimation of my stochastic
> volatility model, specifically,
> qmle/kalman and mle based parm estimation for
> this specific model.
> need minor clarifications there.
> if anyone is familiar with those parm estimation methods or
> know those econometric techniques or are interested in adaptive
> enties / exits for a trading system ( risk modeling ),
> please e-mail back asap.
> good trading.
> bilo.
>
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