The Impact of Different Bond Types on Mean-Reversion Strategies for Bond Portfolio Management
- DOI
- 10.2991/978-94-6463-246-0_16How to use a DOI?
- Keywords
- Mean-reversion Strategy; Bonds; Sharpe Ratio
- Abstract
The research aims to investigate the mean-reversion strategy for three types of bonds: government bonds, corporate bonds, and municipal bonds. The analysis is based on 10 different bonds for each type. The descriptive statistical analysis includes computing the mean, standard deviation, skewness, kurtosis, and Sharpe ratio of the portfolio returns. Moreover, the inferential statistical analysis involves computing the high-water mark, drawdown, and maximum drawdown of the portfolio returns. The results indicate that the traditional mean-reversion strategy is more effective for government bonds than corporate and municipal bonds. Furthermore, the strategy has a negative Sharpe ratio, suggesting that the risk-adjusted returns are not favorable. The high drawdown and maximum drawdown suggest that the strategy can result in significant losses for investors. Therefore, investors should exercise caution when using the traditional mean-reversion strategy for bonds.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Yuning Zhang PY - 2023 DA - 2023/09/26 TI - The Impact of Different Bond Types on Mean-Reversion Strategies for Bond Portfolio Management BT - Proceedings of the 3rd International Conference on Economic Development and Business Culture (ICEDBC 2023) PB - Atlantis Press SP - 138 EP - 150 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-246-0_16 DO - 10.2991/978-94-6463-246-0_16 ID - Zhang2023 ER -