Entropy Pooling with Time-Varying Drift and Simulated Returns
Summary
The document considers whether entropy pooling can be used when returns have a nonlinear, time-varying drift. One answer says it can be applied to simulated log returns or resulting prices at the investor’s horizon, then used as input to portfolio optimization. For multiple horizons, the suggested approach is to represent the distributions across horizons together and account for time dependence generated by the simulation.
The discussion emphasizes practical limitations. Entropy pooling may perform poorly when views are extreme and the simulated prior does not include the relevant tail scenarios. Another response questions whether time-changing parameters or views can be incorporated directly, and notes that a changing drift may conflict with the identically distributed invariant variables often used in strategic allocation. That concern may matter less when portfolios are rebalanced as the drift changes. The document offers viewpoints rather than a tested procedure, so implementation and the suitability of the allocation framework remain model-dependent.
Key ideas
- Entropy pooling can be applied to simulated horizon returns or prices for portfolio optimization.
- Time dependence can be represented through simulations across the relevant horizons.
- Extreme views can be unreliable when the simulated distribution omits tail scenarios.
- A time-varying drift may challenge invariant-based strategic allocation assumptions.
- Frequent rebalancing may address allocation changes as the drift evolves.
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Full text
# Does entropy pooling apply to distributions with time-varying drift? # 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 ? ## Answer by John (score 4) https://quant.stackexchange.com/a/4632 So long as it is possible to simulate the distribution of log returns to the investor's horizon and convert them to prices that serve as an input into the portfolio optimization, it is possible to apply the general Entropy Pooling algorithm to either the log returns or the prices. Whatever care needs to be taken when applying the EP algorithm in its traditional use, should be applied here as well (e.g., the numerical procedure does not work well when you take extreme views and have not simulated the extreme parts of the distribution). It can also be applied to the slightly more complicated case of multiple period optimization (e.g., if you want to account for the fact that in the short-run you might be in a bad regime but in the long-run it will go to the steady state). I have not seen it in the literature, but I have set up EP problems that account for this by treating the distribution at every horizon as one distribution and then applying the EP algorithm. Whatever time dependence that results from the simulation should be accounted for in this fashion. ## Answer by SRKX (score 3) https://quant.stackexchange.com/a/4629 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. ## Answer by Alexey Kalmykov (score 2) https://quant.stackexchange.com/a/4630 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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