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Total Posts: 23
Joined: Dec 2014
Posted: 2018-10-03 09:23
I am trying to approximate the returns of asset A by means of a linear combination of other assets A'=a*B0+b*B1+c*B2....

I have this quite figured out but I'm not sure what a good metric for goodness of fit would be, so far I am only considering relative error (e=(rA-rA')/rA), and I'm concerned with distortions when rA is close to 0.

What would a better metric could be? Ideally it would penalize sign errors more than absolue value errors (ie, it is worse that rA' is +ve when rA is -ve).


Total Posts: 1
Joined: Nov 2018
Posted: 2018-11-24 20:07
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Total Posts: 573
Joined: Jun 2005
Posted: 2018-11-24 21:57
it is difficult to understand specs of your model.

what is rA, rA' ?
a, b - are model parameters?
A and B_i - are assets?
What is the meaning of ' (accent sign) ?


Total Posts: 384
Joined: May 2006
Posted: 2018-11-27 12:25
This is just linear regression.

There is a decent coverage of linear regression in a general setting in The Elements of Statistical Learning by Hastie et al, or just google "lasso regression", "ridge regression" etc.

But I think you will need to modify your approach. You seem to want to regress prices, and that is a bit of a dead end. You are better off regressing returns or log returns, especially if you are worried about small price levels.

I don't know why you want an asymetric penalty function. It is not very difficult to do, but I don't think that will take you in the right direction.

"There is a SIX am?" -- Arthur


Total Posts: 23
Joined: Dec 2014
Posted: 2018-12-04 23:08
I hadn't noticed that the thread got this traction. Let me try to explain better:
We have an asset A whose log returns we want to approximate by a linear combination of assets forming the synthetic asset A’ in this way

Coefficients may or may not be computed via linear regression or any other regression type, but my question is related to a good metric to measure the fitness of approximation.

At the moment I'm considering RMSE but I'd like something more fit that penalizes situations A going up +3% and synthetic asset going down say -1% more, such that when combined with a predictor synthetic asset may adequately replicate the PnL distribution of asset A.


Total Posts: 3
Joined: Apr 2017
Posted: 2018-12-04 23:35
If you don’t want to fit the model wrt this loss, i.e. estimate the coefficients by its minimizing, can’t you just design whatever metric you want via indicator function on residual sign?

For instance similarly to quantile regression asymmetric loss.
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