Dynamic Black-Litterman

ArXiv ID: 2404.18822 “View on arXiv”

Authors: Unknown

Abstract

The Black-Litterman model is a framework for incorporating forward-looking expert views in a portfolio optimization problem. Existing work focuses almost exclusively on single-period problems with the forecast horizon matching that of the investor. We consider a generalization where the investor trades dynamically and views can be over horizons that differ from the investor. By exploiting the underlying graphical structure relating the asset prices and views, we derive the conditional distribution of asset returns when the price process is geometric Brownian motion, and show that it can be written in terms of a multi-dimensional Brownian bridge. The components of the Brownian bridge are dependent one-dimensional Brownian bridges with hitting times that are determined by the statistics of the price process and views. The new price process is an affine factor model with the conditional log-price process playing the role of a vector of factors. We derive an explicit expression for the optimal dynamic investment policy and analyze the hedging demand for changes in the new covariate. More generally, the paper shows that Bayesian graphical models are a natural framework for incorporating complex information structures in the Black-Litterman model. The connection between Brownian motion conditional on noisy observations of its terminal value and multi-dimensional Brownian bridge is novel and of independent interest.

Keywords: Black-Litterman Model, Dynamic Portfolio Optimization, Brownian Bridge, Graphical Models, Affine Factor Model, Multi-Asset / Equities

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 2.0/10
• Quadrant: Lab Rats
• Why: The paper is mathematically dense, deriving the conditional distribution of asset returns as a multi-dimensional Brownian bridge, solving a nonlinear matrix Riccati equation for the optimal policy, and using Bayesian graphical models, which requires advanced stochastic calculus and probability theory. However, the empirical validation is limited to a theoretical experiment on rebalancing without providing real-world backtests, code, or implementation-heavy data analysis, making it less ready for practical trading deployment.

  flowchart TD
    A["Research Goal<br>Dynamic Black-Litterman Model"] --> B["Methodology<br>Bayesian Graphical Model<br>with Affine Factor Structure"]
    B --> C["Key Process<br>Derive Multi-Dimensional Brownian Bridge<br>from Asset Prices & Views"]
    C --> D{"Outputs"}
    D --> E["Optimal Dynamic Investment Policy<br>Closed-form Solution"]
    D --> F["Hedging Demand Analysis<br>Response to Covariate Changes"]
    D --> G["Conditional Return Distribution<br>Expressed via Brownian Bridge"]