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A Volatility Spillover Analysis Between Bond and Commodity Markets as an Indicator for Global Liquidity Risk

Author

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  • Aysegul Kırkpınar
  • Pınar Evrim Mandacı

Abstract

This study aims to analyze the volatility spillover between bond and commodity markets in terms of global liquidity risk. The data covers the daily closing prices of bond markets in specified countries - Brazil, Russia, India, China, and Turkey - and certain commodities - gold and oil - for the period January 2008 to January 2022. We utilized the DCC-GARCH model to analyze volatility spillover between these markets and the Copula DCC-GACRH model to determine dependence structures between them. Additionally, we applied the Hong Causality in Variance Test to determine the direction of the causal relationships between these markets. Our empirical findings indicate the existence of significant volatility spillovers between gold and most of these bond markets (Brazil, China, Russia, and Turkey), and between oil and some of these bond markets (Russia, India and Turkey). Our results indicate a limited diversification benefit for investors and portfolio managers. JEL: G10, G15, C32.

Suggested Citation

  • Aysegul Kırkpınar & Pınar Evrim Mandacı, 2023. "A Volatility Spillover Analysis Between Bond and Commodity Markets as an Indicator for Global Liquidity Risk," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 70(1), pages 71-100.
    • Handle: RePEc:voj:journl:v:70:y:2023:i:1:p:71-100:id:604
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    File URL: https://panoeconomicus.org/index.php/jorunal/article/view/604/761
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    More about this item

    Keywords

    Volatility spillover; Bond markets; DCC-GARCH; Copula DCC-GARCH; Hong causality test;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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