A Dynamic Asset Allocation Strategy with Macroeconomic Indicators
Date
2023-04-12
Authors
Yu, Lijun
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
This thesis proposes a novel approach to strategic asset allocation (SAA) that uses macro-econometric factors and a regime-switching regression model to capture systemic risk, which is crucial in setting target allocations for various asset classes and measuring associated risk premiums. The proposed model employs a Hidden Markov Model (HMM) to filter the four macro-econometric factors, which are the main source of market risk, and uses a mean-variance-based dynamic selection model to increase the portfolio's risk-adjusted return. The empirical analysis utilizes global bond and equity ETFs, as well as sector ETFs, to test the asset pricing model and evaluate the performance of the portfolio. Weekly financial data from January 01, 2002, to December 31, 2022, are used to construct the four macro-econometric factors, and the four weekly macro-econometric factors from September 06, 2016, to January 1, 2019, are employed to estimate the HMM and asset return parameters. The results show that the proposed model consistently outperforms the benchmark MSCI ACWI Index in the out-of-sample period from January 1, 2019, to December 31, 2022, and outperforms the selected ETFs and the mean-variance models in terms of the Sharpe Ratio. The proposed approach to SAA provides a promising avenue for investors to achieve superior risk-adjusted returns in the current global financial landscape.
Description
The thesis proposes a novel investment model that integrates macroeconomic indicators into portfolio management. The model employs a MS-VAR model to identify financial market regimes and filter financial risk premiums affected by macroeconomic indicators. A regime-switching regression model is applied to model conditional asset returns with generated macro-econometric factors. The optimal portfolio performance is proposed through a foresight regime strategy with dynamic adjustments of portfolio weights based on the current regime and regime-dependent macro-econometric factors to capture associated systemic risk. The out-of-sample test shows that the proposed strategy outperforms other strategies and the baseline index in terms of the Sharpe ratio. This study contributes to the literature on macroeconomic factor investments by proposing a comprehensive framework that dynamically explains cross-sectional and time-series asset returns. The proposed model and dynamic asset allocation strategy offer a more comprehensive understanding of financial risk premiums and provide a foundation for further research in this field.
Keywords
SAA, macroeconomic factor investing, dynamic filtrated factors, foresight regime strategy, portfolio management, regime-switching VAR