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Bond portfolio optimization using dynamic factor models

Author

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  • Caldeira, João F.
  • Moura, Guilherme V.
  • Santos, André A.P.
Abstract
A general class of dynamic factor models is used to obtain optimal bond portfolios, and to develop a duration-constrained mean-variance optimization, which can be used to improve bond indexing. An empirical application involving two large data sets of U.S. Treasuries shows that the proposed portfolio policy outperforms a set of yield curve strategies used in bond desks. Additionally, we propose a dynamic rule to switch among alternative bond investment strategies, and find that the benefits of such dynamic rule are even more pronounced when the set of available policies is augmented with the proposed mean-variance portfolios.

Suggested Citation

  • Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Bond portfolio optimization using dynamic factor models," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 128-158.
    • Handle: RePEc:eee:empfin:v:37:y:2016:i:c:p:128-158
    DOI: 10.1016/j.jempfin.2016.03.004
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    4. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    5. Awe Olushina Olawale & Adepoju Abosede Adedayo, 2020. "Change-point detection in CO2 emission-energy consumption nexus using a recursive Bayesian estimation approach," Statistics in Transition New Series, Statistics Poland, vol. 21(1), pages 123-136, March.
    6. Tu, Anthony H. & Chen, Cathy Yi-Hsuan, 2018. "A factor-based approach of bond portfolio value-at-risk: The informational roles of macroeconomic and financial stress factors," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 243-268.
    7. Kapetanios, George & Serlenga, Laura & Shin, Yongcheol, 2021. "Estimation and inference for multi-dimensional heterogeneous panel datasets with hierarchical multi-factor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 504-531.
    8. Rui Wang, 2019. "Unconventional Monetary Policy in Japan: Empirical Evidence from Estimated Shadow Rate DSGE Model," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-29, June.
    9. Ma, Shuai & Ma, Xiaoteng & Xia, Li, 2023. "A unified algorithm framework for mean-variance optimization in discounted Markov decision processes," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1057-1067.
    10. Santos, André A.P. & Moura, Guilherme V., 2014. "Dynamic factor multivariate GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 606-617.
    11. Hamill, Philip A. & Li, Youwei & Pantelous, Athanasios A. & Vigne, Samuel A. & Waterworth, James, 2021. "Was a deterioration in ‘connectedness’ a leading indicator of the European sovereign debt crisis?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    12. Mouloud El Hafidi & Marouane Daoui, 2019. "Chocs de la politique monétaire et croissance économique au Maroc : une approche en terme de modèles FAVAR," Post-Print hal-03311354, HAL.
    13. Choi, Ahjin & Kang, Kyu Ho, 2023. "Modeling the time-varying dynamic term structure of interest rates," Journal of Banking & Finance, Elsevier, vol. 153(C).
    14. Hsiang-Hsi Liu & Chien-Kuo Tseng, 2022. "Common Components in Co-integrated System and Its Estimation and Application: Evidence from Five Stock Markets in Asia-Pacific Chinese Region," Bulletin of Applied Economics, Risk Market Journals, vol. 9(2), pages 101-121.
    15. Aysu Celgin & Mahmut Gunay, 2020. "Weekly Economic Conditions Index for Turkey," CBT Research Notes in Economics 2018, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    16. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Predicting the yield curve using forecast combinations," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 79-98.
    17. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Switching Nelson-Siegel Models," BAFFI CAREFIN Working Papers 19106, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    18. Rueben Ellul & Germano Ruisi, 2022. "Nowcasting the Maltese economy with a dynamic factor model," CBM Working Papers WP/02/2022, Central Bank of Malta.
    19. repec:cte:wsrepe:27047 is not listed on IDEAS
    20. Wulan Anggraeni & Sudradjat Supian & Sukono & Nurfadhlina Abdul Halim, 2023. "Catastrophe Bond Diversification Strategy Using Probabilistic–Possibilistic Bijective Transformation and Credibility Measures in Fuzzy Environment," Mathematics, MDPI, vol. 11(16), pages 1-30, August.
    21. Konstantinos Bisiotis & Stelios Psarakis & Athanasios N. Yannacopoulos, 2022. "Affine Term Structure Models: Applications in Portfolio Optimization and Change Point Detection," Mathematics, MDPI, vol. 10(21), pages 1-33, November.
    22. Jiahe Lin & George Michailidis, 2019. "Regularized Estimation of High-dimensional Factor-Augmented Vector Autoregressive (FAVAR) Models," Papers 1912.04146, arXiv.org, revised May 2020.

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    More about this item

    Keywords

    Bond indexing; Dynamic policy selection; Kalman filter; Out-of-sample evaluation; Portfolio optimization; Yield curve forecasts;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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