# Does entropy pooling apply to distributions with time-varying drift?

I have a returns process that is drawn from a normal distribution with a nonlinear time-varying drift, so I was wondering if the entropy pooling method still applies or if I need an invariant ?

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I can't get access to the full version to Meucci's original paper on Entropy Pooling (EP), Fully Flexible Views: Theory and Practice, and I hence had a look again at the abstract:

We propose a unified methodology to input non-linear views from any number of users in fully general non-normal markets, and perform, among others, stress-testing, scenario analysis, and ranking allocation. We walk the reader through the theory and we detail an extremely efficient algorithm to easily implement this methodology under fully general assumptions. As it turns out, no repricing is ever necessary, hence the methodology can be readily applied to books with complex derivatives. We also present an analytical solution, useful for benchmarking, which per se generalizes notable previous results. Code illustrating this methodology in practice is available through author's homepage.

This confirmed my initial thought that one of the great advantage of EP is that the approach is very general and can adapt to various models.

So I think the answer is yes, I believe you can use EP even with nonlinear time-varying drift.

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I also think it's possible, however it didn't show clearly in his paper that his model parameters (normal or fat-tailed) could be time-varying. I can send you the paper if you want to have a look at it. –  Bytesize Nov 27 '12 at 11:24

Meucci's original paper doesn't state any limitations on prior distribution to which Entropy Pooling (EP) is applied. However, I see two possible issues.

The first problem is that it currently seems to be no place for incorporating time changing parameters or views on a prior distribution in the EP. Therefore, some additional work is required to apply it in your case (although it doesn't look not very complicated).

Assuming that your goal is to include complex views in order to use the posterior distribution in portfolio optimization, another potential problem is that you most probably want to have a market invariant (as per my understanding it's still the cornerstone of the (strategic) asset allocation theory)

The invariants are market variables that can be modeled as the realization of a set of independent and identically distributed random variables at least over the investment horizon. For example, equity invariants are compounded returns, ﬁxed-income invariants are changes in yield to maturity (for a detailed treatment, see the book "Risk and Asset Allocation" by Meucci, chapter 3).

Nonlinear time-varying drift definitely can violate the assumption of identical distribution, therefore making the asset allocation under such distribution meaningless (unless you constantly rebalance your portfolio to make it optimal under the updated drift).

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I had the same concern that we need a market invariant for asset allocation, but from what I see in the Meucci paper "The Prayer", invariance is needed to project the invariant distribution to the investment horizon. In my case however, I already have the projection and the pricing step at the investment horizon with my time-varying drift. Provided I fix my investment horizon, my drift becomes known. I don't think the quest for invariance should be something to always strive for, since there is large evidence that bubble regimes, for example, typically break the invariance. –  Bytesize Nov 27 '12 at 11:20
@Bytesize Yes, I agree with you. If your drift is known in your investment horizon, then you are fine. But you still need to adapt the method to account for the time dimension. –  Alexey Kalmykov Nov 27 '12 at 11:28
You normally would simulate to $\widetilde{X}_{t+k}$ and apply EP to that. I'm saying simulate $\widetilde{X}_{t+1},\ldots,\widetilde{X}_{t+k}$ and concatenate them into one matrix $\widetilde{Y}\equiv\left[\begin{array}{ccc} \widetilde{X}_{t+1} & \cdots & \widetilde{X}_{t+k}\end{array}\right]$ and treat that as though it is one distribution. –  John Nov 27 '12 at 14:34