Bayesian Filtering for Multi-period Mean-Variance Portfolio Selection

TL;DR

This paper introduces a Bayesian filtering approach for multi-period mean-variance portfolio optimization, allowing dynamic parameter estimation and adaptive rebalancing over long investment horizons.

Contribution

It develops a Bayesian filtering framework integrated with dynamic programming for portfolio selection when model parameters are unknown and need real-time updating.

Findings

Bayesian updating improves portfolio performance with market data.

The method adapts effectively to changing market conditions.

Implementation on S&P 500 data demonstrates practical feasibility.

Abstract

For a long investment time horizon, it is preferable to rebalance the portfolio weights at intermediate times. This necessitates a multi-period market model in which portfolio optimization is usually done through dynamic programming. However, this assumes a known distribution for the parameters of the financial time series. We consider the situation where this distribution is unknown and needs to be estimated from the data that is arriving dynamically. We applied Bayesian filtering through dynamic linear models to sequentially update the parameters. We considered uncertain investment lifetime to make the model more adaptive to the market conditions. These updated parameters are put into the dynamic mean-variance problem to arrive at optimal efficient portfolios. Extensive simulations are conducted to study the effect of varying underlying parameters and investment horizon on the performance of the method. An implementation of this model to the S&P500 illustrates that the Bayesian updating is strongly favored by the data and that it is practically implementable.