Due to the nature of the curve (bond curve, swap curve etc), bond traders typically have some model that allows them to measure the "fair value" (FV) of a bond vs other bonds on the curve. This is where RV (relative value) trades come from: observing that a bond looks cheap relative to its FV against other bonds.
Some models can be fairly involved, such as using splines to fit the curve. Lots of these complicated models break down when looking at things such as inflation swaps due to the seasonality in the index causing instability in the front-end, which propagates across the curve in most models.
Is there a way that a basic regression model can be used to observe fair value? We can use regression models to observe richening or cheapening. (Regress the change on day of a security against a bucket of of other securities, and accumulate the residuals of the model each day. A security deviating from the model over a number of days - i.e. richening - will see daily positive residuals that are not averaging to zero.) The issue here is that the deviation might have begun at a "cheap" point, so the residual deviations make the security look like it is "rich", when in actual fact it has just richened from cheap to FV.
So the problem is that we can observe richening or cheapening, but cannot see fair value. How can we develop a level for fair value using a basic model such as this? How can we avoid the problem outlined in the last sentence of the previous section?