Macroeconomic factors and government bond yield in Indonesia
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Author(s)Naning FatmawatieLink to ORCID Index: https://orcid.org/0000-0002-3647-051X
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Endri EndriLink to ORCID Index: https://orcid.org/0000-0002-8613-6149
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Destyanah Husein
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DOIhttp://dx.doi.org/10.21511/pmf.13(1).2024.08
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Article InfoVolume 13 2024, Issue #1, pp. 95-105
- 419 Views
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126 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
The issuance of bonds by the government attracts the interest of many investors, including foreigners. The government must understand the factors determining bond yields for managing government debt. This study aims to investigate the effect of domestic and global macroeconomic variables on government bond yields in Indonesia. The paper uses monthly data from November 2014 to December 2022. The research sample comprises government bonds with 5, 10, and 15-year tenor bonds. The GARCH (1,1) and GARCH-M (1,1) models are applied to estimate and analyze the determinants of government bond yields. Research findings reveal that Indonesian interest rates significantly affect the yield of 10- and 15-year tenor bonds. Inflation has no impact on bond yields across all tenors. The increase in foreign exchange reserves reduces bond yields in all tenors. The Indonesian stock exchange index is detrimental to long-term bond yields. The exchange rate has a positive impact on bond yields in all tenors. World oil prices significantly impact yields on 5- and 10-year tenor bonds. The Fed’s interest rate positively affects the yield on the 15-year tenor bond. The implication of these findings for the Indonesian government is the implementation of several aspects of economic and financial policies that can improve state debt management and financial market stability.
- Keywords
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JEL Classification (Paper profile tab)G12, G23, E43, C58
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References52
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Tables4
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Figures1
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- Figure 1. Conceptual model
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- Table 1. Variable operationalization
- Table 2. Descriptive statistics
- Table 3. AIC and SC test results
- Table 4. GARCH model estimation results
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Anthony Olugbenga Adaramola ,
Oluwabunmi Dada
doi: http://dx.doi.org/10.21511/imfi.17(2).2020.01
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Gusni ,
Silviana ,
Faisal Hamdani
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