Industry Talk: Optimization of Strategic and Tactical Asset Allocation for Multi-Asset Portfolios

Join Dr. Artur Sepp as he presents a systematic risk-based asset allocation framework which bypasses explicit returns forecast.

Abstract:

Traditional asset allocation frameworks require the estimation of expected returns and covariance matrices for constructing multi-asset portfolios. In practice, these estimations pose challenges that make them unreliable for optimization. In this paper, we introduce a systematic risk-based asset allocation framework which bypasses explicit returns forecast.

For the estimation of the covariance matrix, we employ Hierarchical Group Lasso (HGL) using a factor risk model with enhanced stability imposed by the sparsity on factor loadings. To refine the construction of the tactical asset allocation, we then introduce price-based signals, including momentum and low beta for traditional investments. For alternative investments, such as private assets and hedge funds, which typically have less frequent return observations and exhibit heavy-tailed return distributions, we incorporate their specific alphas to account for their systematic and idiosyncratic risks. These enhancements dynamically adjust exposures in response to market conditions without using return forecasts.

Our methodology bridges the gap between strategic asset allocation (SAA) and tactical asset allocation (TAA) decisions, offering a scalable and adaptable solution for institutional portfolios. By prioritizing a risk-based optimization and dynamic tactical adjustments, our framework enhances robustness and flexibility in asset allocation. 

About the Speaker:

Dr. Artur Sepp is the head quant at LGT bank in Zurich focusing on quantitative asset allocation and systematic investment strategies. Artur has over 15 years of experience in financial markets including heading quant research and portfolio management at a systematic hedge fund and a family office as well as leading development of front-office quant strategies and derivatives at private (Julius Baer) and investment banks (Merrill Lynch/BofA). Artur has a PhD in Mathematical Statistics from University of Tartu, an MSc in Industrial Engineering and Management Sciences from Northwestern University, and a BA cum laude in Mathematical Economics from Tallinn University of Technology. His expertise covers quantitative investing and asset allocation, quantitative modelling of derivative securities, machine learning and data science, and blockchain applications within decentralised finance. He is the author and co-author of several research articles on quantitative finance published in key journals. Artur won Risk Magazine’s Quant of the Year Award (2024).