Strange


Total Posts: 1557 
Joined: Jun 2004 


Here is a problem and, as usual, I am being stupid.
I am trying to predict an overnight interest rate in one of the emerging markets in several days (up to 5 weeks). There is a relatively large history of observations (say 56 thousand observations) and I found a few predictive models.
Model 1: reversion to the longterm mean  produces a forecast for time now + n
Model 2: shortterm microreversion produces forecast for tomorrow (now + 1)
Model 3: day of week seasonality  produces adjustment based on the day of the week
What I am trying to do now is merge these models into a single forecasting method. The problem is that (a) the models are not strictly independent and (b) I want to understand if I am getting an improvement (the base line is unchanged from todays rate).

Eher Ende mit Schrecken als Schrecken ohne Ende 


tabris


Total Posts: 1261 
Joined: Feb 2005 


wouldn't you just use some form of ensemble learning to combine the forecast? 
Dilbert: Why does it seem as though I am the only honest guy on earth?
Dogbert: Your type tends not to reproduce. 

ronin


Total Posts: 457 
Joined: May 2006 


d Prediction = ShortTimeScale * (ShortTerm  Prediction) dt + dSeasonality + dW d ShortTerm = LongTimeScale * (LongTerm  ShortTerm) dt + dZ
?

"There is a SIX am?"  Arthur 


nikol


Total Posts: 729 
Joined: Jun 2005 


Minimize Sum of error normalized by combined error prediction ~ R2.
pred_i (i=1,2) depends on model parameters.
R21 ~ abs(pred1price)/err1 R22 ~ abs(pred2price)/err2
combined R2 ~ [(pred1price)^2+(pred2price)^2]/[err1^2+err2^2]
R2 behaves well, i.e. it roughly follows Chi2.
You also can apply inverseCDF transformations (it's also called PIT) and combine probabilities (using ML/NN?). Usually I check something like:
if 2*p1 \in [1,1] then (p1+p21) \in [1,1]
or something like...
PS. typos.. 


Zoho


Total Posts: 22 
Joined: Feb 2018 


Could Fernholz with his hedged strategy from "Statistics of statistical arbitrage" be of any help?



