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 optimization of multi-asset portfolios. A particular challenge arises for portfolio optimization with allocations to liquid public assets and illiquid private assets and hedge funds.

We develop a systematic risk-based asset allocation framework that incorporates instruments with varying liquidity profiles. We introduce the Hierarchical Clustering Group Lasso method for the estimation of the asset covariance matrix using a set of risk factors.

We apply risk-budgeting optimization for the strategic asset allocation to a universe of public and private benchmarks without using explicit return forecasts. To implement tactical asset allocation, we introduce price-based signals, including momentum and low beta, for traditional investments. For alternative investments, such as private assets and hedge funds, we incorporate the managers' alphas to account for their systematic and idiosyncratic risks. We design our optimization engine to incorporate instruments with different rebalancing schedules as well as different turnover requirements and tracking error constraints.

Our extensive empirical analysis clearly demonstrates that the proposed methodology provides substantial improvements in terms of risk-adjusted performance metrics, including superior Sharpe ratios and reduced drawdown risks relative to static weight benchmarks.

This presentation is based on the joint work with Ivan Ossa and Mika Kastenholz.

About the Speaker:

Artur Sepp is the Global Head of Investment Services Quant Group at LGT bank in Zurich focusing on quantitative asset allocation and systematic investment strategies. Artur has almost 20 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 the 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 coauthor of several research articles on quantitative finance published in key journals. Artur won the Quant of the Year Award from Risk Magazine (2024). He is an active martial arts practitioner in his free time.