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Open AccessOpen Access JUSTC Management 04 March 2024

The impact of macroprudential cross-border capital flow management on the linkage of domestic and foreign bond markets

  • International Institute of Finance, School of Management, University of Science and Technology of China, Hefei 230031, China

Cite this:
https://doi.org/10.52396/JUSTC-2023-0050
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  • Author Bio:

    Pengwei Zhao is a graduate student under the tutelage of Prof. Xiao Wang at the University of Science and Technology of China. His research mainly focuses on international finance and corporate finance

    Xiao Wang is a Professor in the School of Management and an Assistant Dean of International Institute of Finance at the University of Science and Technology of China. She received her Ph.D. degree in Economics from University of Wisconsin-Madison in 2011, M.A. in Economics in 2005, and B.A. in Economics in 2003 from Peking University. Her research mainly focuses on international finance, international trade, and applied econometrics. Her work has appeared in some leading economics and finance journals, such as Journal of International Economics and Journal of Banking and Finance. In 2016, she received the Pushan Academic Award for Excellent Papers on International Economics

  • Corresponding author: E-mail: iriswx@ustc.edu.cn
  • Received Date: 24 March 2023
  • Accepted Date: 17 July 2023
    • Available Online: 04 March 2024
    Abstract

  • Abstract

    Cross-border financing activities in China have increased significantly in recent years, and the inflow of capital may lead to accumulated financial risks. To mitigate financial risks and promote the opening of financial markets, macroprudential management policies for cross-border financing have been implemented since 2016. This paper examines the effectiveness of macroprudential management policies in opening financial markets and managing foreign financial risks. We employ a time-varying parameter vector autoregressive (TVP-VAR) model to quantitatively analyze changes in the spillover effects between Chinese bond market and foreign bond markets under different implementation stages of cross-border financing macroprudential policies. Our analysis reveals that the implementation of macroprudential management of cross-border financing has increased the total spillover effect between different bond markets, as well as the spillover effect from other bond indices to the Chinese RMB Bond Index and the spillover effect from other indices to the Chinese USD index. Moreover, our findings indicate that macroprudential management has reduced the total volatility spillover effect and the volatility spillover effect from other indices to the Chinese RMB Bond Index. These results highlight the importance of preventing external risk transmission when China’s financial market is opening to the world.

    Graphical abstract

    The TVP-VAR method is used to study the influence of macroprudential management on the bond market under different periods.

    Abstract

    Cross-border financing activities in China have increased significantly in recent years, and the inflow of capital may lead to accumulated financial risks. To mitigate financial risks and promote the opening of financial markets, macroprudential management policies for cross-border financing have been implemented since 2016. This paper examines the effectiveness of macroprudential management policies in opening financial markets and managing foreign financial risks. We employ a time-varying parameter vector autoregressive (TVP-VAR) model to quantitatively analyze changes in the spillover effects between Chinese bond market and foreign bond markets under different implementation stages of cross-border financing macroprudential policies. Our analysis reveals that the implementation of macroprudential management of cross-border financing has increased the total spillover effect between different bond markets, as well as the spillover effect from other bond indices to the Chinese RMB Bond Index and the spillover effect from other indices to the Chinese USD index. Moreover, our findings indicate that macroprudential management has reduced the total volatility spillover effect and the volatility spillover effect from other indices to the Chinese RMB Bond Index. These results highlight the importance of preventing external risk transmission when China’s financial market is opening to the world.

    • Public Summary

    • We employ a TVP-VAR model to analyze quantitatively changes in the spillover effects between Chinese bond market and foreign bond markets under different implementation stages of cross-border financing macroprudential management.
    • We examine the impact of macro-prudential management of cross-border financing on the risk of the RMB bond market and explores the changes in bond market risk in the context of the gradual opening of the financial market, and our study provides a crucial supplement to the role of macroprudential management in the transmission of bond market risk.
    • The macroprudential management has increased the total spillover effect among different bond markets, the spillover effect from other bond markets to Chinese RMB bond market, and the spillover effect from other bond markets to Chinese USD bond market.
    • The macroprudential management has reduced the total volatility spillover effect and the volatility spillover effect from other bond markets to Chinese RMB bond market.

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    • References(25)
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    [2]
    Diebold F X, Yilmaz K. Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 2012, 28(1): 57–66. doi: 10.1016/j.ijforecast.2011.02.006
    [3]
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    [4]
    Akhtaruzzaman M, Boubaker S, Sensoy A. Financial contagion during COVID-19 crisis. Finance Research Letters, 2021, 38: 101604. doi: 10.1016/j.frl.2020.101604
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    [11]
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    [12]
    Wu H, Han Y, Zheng Z. An experimental construction of the monitoring system of bond market fragility in China. Financial Regulation Research, 2018 (6): 31–47. (in Chinese) doi: 10.13490/j.cnki.frr.2018.06.003
    [13]
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    [15]
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    [16]
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    [17]
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    [18]
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    [19]
    Bekiros S D, Paccagnini A. Macroprudential policy and forecasting using hybrid DSGE Models with financial frictions and state space Markov-switching TVP-VARs. Macroeconomic Dynamics, 2014, 19 (7): 1565–1592. doi: 10.1017/S1365100513000953
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    [22]
    Chesney M, Reshetar G, Karaman M. The impact of terrorism on financial markets: An empirical study. Journal of Banking and Finance, 2011, 35(2): 253–267. doi: 10.1016/j.jbankfin.2010.07.026
    [23]
    Jiang X, Zhao Y. Research on the risks and opportunities faced by Chinese enterprises in issuing US dollar bonds. Economic Review Journal, 2017 (7): 112–117. (in Chinese) doi: 10.16528/j.cnki.22-1054/f.201707112
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    Ferreira M A, Miguel A F. The determinants of domestic and foreign bond bias. Journal of Multinational Financial Management, 2011, 21(5): 279–300. doi: 10.1016/j.mulfin.2011.07.004
    [25]
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    Figure  1.  Total spillover and directional spillover. The total spillover effect equals 1– (Σ the spillover effect from A to A), where A represents any bond market. The spillover effect from others to CH means that the Chinese RMB bond market received spillover effects from other bond markets. The spillover effect from CH to others means that the Chinese RMB bond market imposes a spillover effect on other bond markets. The spillover effect of CHUSD is the same as that of CH.

    A1.  RMB exchange rate in 2017.

    Figure  2.  Net pairwise spillover. Note: Taking the first picture as an example, a positive net paired spillover effect means that the spillover effect from the Chinese USD Bond Index to the FTSE Russel US Bond Index is greater than the spillover effect from the FTSE Russel US Bond Index to the Chinese USD Bond Index.

    Figure  3.  Total and directional volatility spillover.

    Figure  4.  Net pairwise volatility spillover. Taking the first figure as an example, a positive net pairwise spillover effect means that the spillover effect from Chinese RMB Bond Index volatility to US Bond Index volatility is greater than the spillover effect from US Bond Index volatility to Chinese RMB Bond Index volatility; net pairwise spillovers.

    Figure  5.  Index robust 1: FRUS change to FREU.

    Figure  6.  Index robust 2: TVP-VAR change to VAR.

    Figure  7.  Volatility robust 1: FRUS change to FREU.

    Figure  8.  Volatility robust 2: TVP-VAR change to VAR.

    [1]
    Diebold F X, Yilmaz K. Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 2009, 119(1): 158–171. doi: 10.1111/j.1468-0297.2008.02208.x
    [2]
    Diebold F X, Yilmaz K. Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 2012, 28(1): 57–66. doi: 10.1016/j.ijforecast.2011.02.006
    [3]
    Diebold F X, Yilmaz K. On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 2014, 182(1): 119–134. doi: 10.1016/j.jeconom.2014.04.012
    [4]
    Akhtaruzzaman M, Boubaker S, Sensoy A. Financial contagion during COVID-19 crisis. Finance Research Letters, 2021, 38: 101604. doi: 10.1016/j.frl.2020.101604
    [5]
    Corbet S, Meegan A, Larkin C, et al. Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 2018, 165: 28–34. doi: 10.1016/j.econlet.2018.01.004
    [6]
    Antonakakis N, Chatziantoniou I, Gabauer D. Refined measures of dynamic connectedness based on time-varying parameter vector autoregression. Journal of Risk and Financial Management, 2020, 13(4): 84. doi: 10.3390/jrfm13040084
    [7]
    Cortina J, Didier T, Schmukler S. Global corporate debt during crises: Implications of switching borrowing across markets. Journal of International Economics, 2021, 131: 103487. doi: 10.1016/j.jinteco.2021.103487
    [8]
    Luo S, Qiao G, Wang Q. Research on dynamic linkage effect of convertible bonds and stock market. Price: Theory and Practice, 2020 (5): 82–85,175. (in Chinese) doi: 10.19851/j.cnki.CN11-1010/F.2020.05.160
    [9]
    Meng H, Zhang L, Cheng Y. Study on the risk spillover effects of China’s financial market. Statistics & Information Forum, 2021, 36 (11): 63–75. (in Chinese) doi: 10.3969/j.issn.1007-3116.2021.11.006
    [10]
    Fei Z, Liu K. Volatility spillover effects and risk pricing of government bond markets under the condition of financial opening. Economic Research Journal, 2020, 55(9): 25–41. (in Chinese)
    [11]
    Zhao W, Meng X, Xiang X. Research on the mechanism and measurement of systemic risk formation in Chinese bond market. Journal of Financial Development Research, 2022 (10): 82–87. (in Chinese) doi: 10.19647/j.cnki.37-1462/f.2022.10.011
    [12]
    Wu H, Han Y, Zheng Z. An experimental construction of the monitoring system of bond market fragility in China. Financial Regulation Research, 2018 (6): 31–47. (in Chinese) doi: 10.13490/j.cnki.frr.2018.06.003
    [13]
    Zhang A, Pan M, Liu B, et al. Systemic risk: The coordination of macroprudential and monetary policies in China. Economic Modelling, 2020, 93: 415–429. doi: 10.1016/j.econmod.2020.08.017
    [14]
    Huang Y, Cho Y, Tao K, et al. The support of monetary policy and macroprudential on macroeconomic stability. Journal of Financial Research, 2019, 474 (12): 70–91. (in Chinese)
    [15]
    Chen Y, Chen H, Li G, et al. Time-varying effect of macro-prudential policies on household credit growth: Evidence from China. Economic Analysis and Policy, 2021, 72: 241–254. doi: 10.1016/j.eap.2021.08.010
    [16]
    Chari A, Stedman K D, Forbes K. Spillovers at the extremes: The macroprudential stance and vulnerability to the global financial cycle. Journal of International Economics, 2022, 136: 103582. doi: 10.1016/j.jinteco.2022.103582
    [17]
    Wang L, Li B, Ma X, et al. The price volatility spillover effect and its sustainability between Chinese crude oil future and international crude oil futures: Based on the BEKK-MGARCH model. Systems Engineering, 2021, 39(3): 102–120. (in Chinese)
    [18]
    Zhou A, Han F. Research on the risk spillovers between stock and exchange rate markets—Based on the GARCH-TVP Copula-CoVaR model. Studies of International Finance, 2017 (11): 54–64. (in Chinese) doi: 10.16475/j.cnki.1006-1029.2017.11.006
    [19]
    Bekiros S D, Paccagnini A. Macroprudential policy and forecasting using hybrid DSGE Models with financial frictions and state space Markov-switching TVP-VARs. Macroeconomic Dynamics, 2014, 19 (7): 1565–1592. doi: 10.1017/S1365100513000953
    [20]
    Alizadeh S, Brandt M W, Diebold F X. Range-based estimation of stochastic volatility models. Journal of Finance, 2002, 57(3): 1047–1091. doi: 10.1111/1540-6261.00454
    [21]
    Livingston M, Poon W, Zhou L. Are Chinese credit ratings relevant? A study of the Chinese bond market and credit rating industry. Journal of Banking and Finance, 2018, 87 : 216–232. doi: 10.1016/j.jbankfin.2017.09.020
    [22]
    Chesney M, Reshetar G, Karaman M. The impact of terrorism on financial markets: An empirical study. Journal of Banking and Finance, 2011, 35(2): 253–267. doi: 10.1016/j.jbankfin.2010.07.026
    [23]
    Jiang X, Zhao Y. Research on the risks and opportunities faced by Chinese enterprises in issuing US dollar bonds. Economic Review Journal, 2017 (7): 112–117. (in Chinese) doi: 10.16528/j.cnki.22-1054/f.201707112
    [24]
    Ferreira M A, Miguel A F. The determinants of domestic and foreign bond bias. Journal of Multinational Financial Management, 2011, 21(5): 279–300. doi: 10.1016/j.mulfin.2011.07.004
    [25]
    Zhou X, Li M, Liu T. Co-movements between onshore and offshore RMB bond markets. Studies of International Finance, 2015 (3): 44–53. (in Chinese) doi: 10.16475/j.cnki.1006-1029.2015.03.005
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