Dynamic Portfolio Allocation in High Dimensions using Sparse Risk Factors

TL;DR

This paper introduces a scalable, dynamic risk factor model for high-dimensional portfolio allocation that improves prediction stability, sparsity, and investor utility over traditional benchmarks.

Contribution

It presents a novel dynamic risk factor model with time-varying sparsity for high-dimensional volatility prediction and portfolio optimization.

Findings

More stable and sparse volatility predictions.

Significant portfolio performance improvements.

Higher utility gains for mean-variance investors.

Abstract

We propose a fast and flexible method to scale multivariate return volatility predictions up to high-dimensions using a dynamic risk factor model. Our approach increases parsimony via time-varying sparsity on factor loadings and is able to sequentially learn the use of constant or time-varying parameters and volatilities. We show in a dynamic portfolio allocation problem with 452 stocks from the S&P 500 index that our dynamic risk factor model is able to produce more stable and sparse predictions, achieving not just considerable portfolio performance improvements but also higher utility gains for the mean-variance investor compared to the traditional Wishart benchmark and the passive investment on the market index.