[R-SIG-Finance] Exponential smoothing and WLS
Eric Zivot
ezivot at u.washington.edu
Mon May 9 21:58:34 CEST 2011
My understanding is that regression on exponentially weighted data and
one-step ahead prediction (sometimes called discounted least squares) is a
"poor man's" Kalman filtering technique in which the underlying state space
model is a related to an exponential smoothing model. The optimal way to do
estimation is to explicitly write the underlying state space model (e.g. use
the dlm package) and then estimate the parameters by the prediction error
decomposition of the log-likelihood, and then do prediction from the
estimated model. For example, you could write your regression model as a
time varying parameter model where the regression coefficient follow pure
random walk or stationary autoregressive processes.
-----Original Message-----
From: r-sig-finance-bounces at r-project.org
[mailto:r-sig-finance-bounces at r-project.org] On Behalf Of riccardo visca
Sent: Monday, May 09, 2011 10:47 AM
To: r-sig-finance at r-project.org
Subject: [R-SIG-Finance] Exponential smoothing and WLS
Hello all,
I am estimating a gaussian linear model
y_t+1 = X_t B + u_t
and I noticed that I can actually predict very well a transformation of y_t
(R^2=0.82)
where yhat_t is in fact an exponential smoothed version of y_t with some
smoothing parameter H
y_t is stationary in mean, eteroschedastic and full of outliers daily
returns
series
X_t are (optionally) smoothed values too
It is quite straightforward using weighted least squares to predict the
trend
value Et( yhat_t+1 ) but apparently and quite obviously
y_t+1 = E(yhat_t+1)+N_t+1
N_t+1 = y_t+1 - E(yhat_t+1)
were the noise N_t is full of jumps, autocorrelated, nearly unit root and
eteroschedastic
Now my question is:
is there a way to estimate efficiently this family of models in one step
and/or
can we forecast efficiently yt+1-E(yhat_t+1) ?
Other than Crane-Crotty model that uses exponential weights?
Maybe an obvious R implementation I missed?
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