Statistical Methods in Finance 2019

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Abstract

Multi-period Portfolio Optimization with Bayesian Updating

by Shubhangi Sikari, IIT Madras

For 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 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 makes the model more adaptive to the market conditions. This updated parameters are put into dynamic mean-variance problem to arrive at optimal information efficient portfolios. An implementation of this model to the S&P500 illustrates that the bayesian updating is strongly favoured by the data and that it is practically implementable.

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