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Bond market and macroeconomic stability in East Asia: a nonlinear causality analysis

  • S.I.: Networks and Risk Management
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A Correction to this article was published on 21 June 2021

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Abstract

This study investigates whether dynamic bond markets lead to more macroeconomic stability in eight East Asian countries by distinguishing between advanced and emerging economies. Contrary to previous studies, we relax the strong assumption of homogenous investors by adopting a frequency approach based on the wavelet methodology to measure the relationship between the bond market and macroeconomic stability through both the time and frequency dimensions. Our analysis reveals three main findings. First, the interaction between the bond market and macroeconomic variables is more pronounced in emerging economies than in developed economies. Second, the relationship between the bond market and macroeconomic indicators varies over time, as it is more pronounced during turmoil periods, and across frequencies, as we show that the relationship is observed in the short and medium (long) term for emerging (advanced) East Asian economies. Third, there is divergence in the relationship regarding the level and volatility of the macroeconomic indicators. Indeed, for emerging economies, the relationship is more pronounced in the first moments; however, for advanced economies, it is more pronounced in the second moments.

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  1. Although many studies conclude that financial integration does generate growth benefits, this relationship is not always found to be robust.

  2. Although Park and Lee (2011) analyze the impacts of global and regional shocks on Asian currency bond markets, they do not investigate the causality between bond markets and macroeconomic stability.

  3. Here, c indicates the eight studied countries and t refers to time.

  4. For details on the theoretical distribution of wavelet power and cross-wavelet power, see Torrence and Webster (1998).

  5. The bond market return \( \left( {K_{1c} } \right) \) and studied macroeconomic indicator \( \left( {K_{2c} } \right) \) are in phase in the case of \( \varphi_{{K_{1c} ,K_{2c} }} \left( s \right) \in \left( { - \frac{\pi }{2},0} \right) \cup \left( {0,\frac{\pi }{2}} \right) \). In this case, the variable \( \left( {K_{1c} } \right) \) is leading if \( \varphi_{{K_{1c} ,K_{2c} }} \left( s \right) \in \left( { - \frac{\pi }{2},0} \right) \) and the variable \( \left( {K_{2c} } \right) \) is leading if \( \varphi_{{K_{1c} ,K_{2c} }} \left( s \right) \in \left( {0,\frac{\pi }{2}} \right) \). However, the commodity cycle \( \left( {K_{1c} } \right) \) and credit cycle \( \left( {K_{2c} } \right) \) are in phase if \( \varphi_{x,y} \left( s \right) \in \left( {\frac{\pi }{2}, \pi } \right) \cup \left( { - \pi , - \frac{\pi }{2}} \right) \). Here, the variable \( \left( {K_{1c} } \right) \) is leading if \( \varphi_{{K_{1c} ,K_{2c} }} \left( s \right) \in \left( {\frac{\pi }{2}, \pi } \right) \) and the variable \( \left( {K_{2c} } \right) \) is leading if \( \varphi_{x,y} \left( s \right) \in \left( { - \pi , - \frac{\pi }{2}} \right) \).

  6. This classification is based on the IMF classification in 2018.

  7. Economic regionalism is formed based on several initiatives such as bilateral FTAs, sub-regional FTAs, and regional surveillance mechanisms.

  8. Please see Ftiti (2010) for more details about the stability of the macroeconomic environment.

  9. We retain only these indicators, as the data on debt are not available for all countries, while interest rates are correlated with the bond market and various interest rates might be used. The REER is expressed in logarithm form.

  10. To save space, we do not present the results of the GARCH estimations. These results are available from the authors upon request.

  11. For more details, see Torrence and Compo (1998) and Veleda et al. (2012).

  12. To save space, we do not report all the tables and figures for this analysis. They are available from the authors upon request.

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Authors and Affiliations

  1. College of Islamic Economics and Finance, Umm al-Qura University, Mecca, Saudi Arabia

    Jamel Boukhatem

  2. Faculty of Economic Sciences and Management, University of Tunis el Manar, Tunis, Tunisia

    Jamel Boukhatem

  3. EDC Paris Business School, OCRE-Lab, Paris, France

    Zied Ftiti

  4. IDRAC Business School, Lyon, France

    Jean Michel Sahut

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  1. Jamel Boukhatem
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  2. Zied Ftiti
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  3. Jean Michel Sahut
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Correspondence to Jean Michel Sahut.

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Boukhatem, J., Ftiti, Z. & Sahut, J.M. Bond market and macroeconomic stability in East Asia: a nonlinear causality analysis. Ann Oper Res 297, 53–76 (2021). https://doi.org/10.1007/s10479-020-03519-6

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