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Application of network vector autoregression model in volatility spillover analysis

  • Department of Statistics and Finance, School of Management, University of Scienceand Technology of China, hefei 230026, China

Cite this:
https://doi.org/10.52396/JUST-2021-0097
  • Received Date: 06 April 2021
  • Rev Recd Date: 27 April 2021
  • Publish Date: 30 June 2021
    Abstract

  • Abstract

    Measuring the network connectedness of the financial system is of great importance in systemic risk analysis, and has drawn great attention in recent years. In this paper, we apply the transfer entropy method to analyze the volatility spillover network connectedness of the U.S. stock market. Based on the network structure, we apply the network vector autoregression model (NVAM) and are interested in identifying the influential firms in volatility spillover network of the financial system. In addition, by using rolling windows, the dynamics of total volatility spillover network connectedness indices are obtained, which shows a sharp rise at the beginning of the financial crisis, while it only fluctuates within a controllable range in the steady economic period. The results show that transfer entropy has great potential for understanding the correlation and information flow of financial markets.

    Abstract

    Measuring the network connectedness of the financial system is of great importance in systemic risk analysis, and has drawn great attention in recent years. In this paper, we apply the transfer entropy method to analyze the volatility spillover network connectedness of the U.S. stock market. Based on the network structure, we apply the network vector autoregression model (NVAM) and are interested in identifying the influential firms in volatility spillover network of the financial system. In addition, by using rolling windows, the dynamics of total volatility spillover network connectedness indices are obtained, which shows a sharp rise at the beginning of the financial crisis, while it only fluctuates within a controllable range in the steady economic period. The results show that transfer entropy has great potential for understanding the correlation and information flow of financial markets.
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    • References(23)
  • [1]
    Acemoglu D, Carvalho V, Ozdaglar A, et al. The network origins of aggregate fluctuations. Econometrica, 2012, 80: 1977-2016.
    [2]
    Babus A. The formation of financial networks. The RAND Journal of Economics, 2016, 47(2): 239-272.
    [3]
    Fang L B, Sun B Y, Li H J, et al. Systemic risk network of Chinese financial institutions. Emerging Markets Review, 2018, 35: 190-206.
    [4]
    Wang G J, Jiang Z Q, Lin M, et al. Interconnectedness and systemic risk of China's financial institutions. Emerging Markets Review, 2018, 35(12): 1-18.
    [5]
    Billio M, Lo A W, Sherman M, et al. Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics, 2012, 104(3): 535-559.
    [6]
    Barigozzi M, Brownlees C. Network estimation for time series. Journal of Applied Econometrics, 2018, 34(3): 347-364.
    [7]
    Diebold F, Yilmaz K. On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 2014, 182: 119-134.
    [8]
    Giglio S, Kelly B, Pruitt S, et al. Systemic risk and the macroeconomy: An empirical evaluation. Journal of Financial Economics, 2016, 119: 457-471.
    [9]
    Adrian T, Brunnermeier M K. CoVaR. American Economic Review, 2016, 102: 59-64.
    [10]
    Gong X L, Liu X H, Xiong X, et al. Financial systemic risk measurement based on causal network connectedness analysis. International Review of Economics & Finance, 2019, 64: 290-307.
    [11]
    Mazzarisi P, Zaoli S, Campajola C, et al. Tail Granger causalities and where to find them: Extreme risk spillovers vs spurious linkages. Journal of Economic Dynamics and Control, 2020, 121: 104022.
    [12]
    Restrepoa N, Uribea J M, Manotasa D. Financial risk network architecture of energy firms. Applied Energy, 2018, 215(2): 630-642.
    [13]
    Wen T, Wang G J. Volatility connectedness in global foreign exchange markets. Journal of Multinational Financial Management, 2020, 54: 100617.
    [14]
    Demirer M, Diebold F X, Liu L, et al. Estimating global bank network connectedness. Journal of Applied Econometrics, 2018, 33(1): 1-15.
    [15]
    Chen Y, Hu J, Zhang W P. Too connected to fail? Evidence from a Chinese financial risk spillover network. China & World Economy, 2020, 28(6): 78-100.
    [16]
    Schreiber T. Measuring information transfer. Physical Review Letters, 2000, 85 (2): 461-464.
    [17]
    Kim J, Kim G, An S, et al. Entropy-based analysis and bioinformatics-inspired integration of global economic information transfer. PLoS ONE, 2013, 8(1): e51986.
    [18]
    Gong C, Tang P, Wang Y. Measuring the network connectedness of global stock markets. Physica A: Statistical Mechanics and its Applications, 2019, 535: 122-351.
    [19]
    Shannon C. A mathematical theory of communication. The Bell System Technical Journal, 1948, 27(3): 379-423.
    [20]
    Zhu X, Chang X, Li R, et al. Portal nodes screening for large scale social networks. Journal of Econometrics, 2019, 209(2): 145-157.
    [21]
    Dou B, Parrella M L, Yao Q. Generalized yule-walker estimation for spatio-temporal models with unknown diagonal coefficients. Journal of Econometrics, 2016, 194(2): 369-382.
    [22]
    Garman M B, Klass M J. On the estimation of security price volatilities from historical data. Journal of Business, 1980, 53(1): 67-78.
    [23]
    Alizadeh S, Brandt M, Dieebold F. Range-based estimation of stochastic volatility models. Journal of Finance, 2002, 57(3): 1047-1091.
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    [1]
    Acemoglu D, Carvalho V, Ozdaglar A, et al. The network origins of aggregate fluctuations. Econometrica, 2012, 80: 1977-2016.
    [2]
    Babus A. The formation of financial networks. The RAND Journal of Economics, 2016, 47(2): 239-272.
    [3]
    Fang L B, Sun B Y, Li H J, et al. Systemic risk network of Chinese financial institutions. Emerging Markets Review, 2018, 35: 190-206.
    [4]
    Wang G J, Jiang Z Q, Lin M, et al. Interconnectedness and systemic risk of China's financial institutions. Emerging Markets Review, 2018, 35(12): 1-18.
    [5]
    Billio M, Lo A W, Sherman M, et al. Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics, 2012, 104(3): 535-559.
    [6]
    Barigozzi M, Brownlees C. Network estimation for time series. Journal of Applied Econometrics, 2018, 34(3): 347-364.
    [7]
    Diebold F, Yilmaz K. On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 2014, 182: 119-134.
    [8]
    Giglio S, Kelly B, Pruitt S, et al. Systemic risk and the macroeconomy: An empirical evaluation. Journal of Financial Economics, 2016, 119: 457-471.
    [9]
    Adrian T, Brunnermeier M K. CoVaR. American Economic Review, 2016, 102: 59-64.
    [10]
    Gong X L, Liu X H, Xiong X, et al. Financial systemic risk measurement based on causal network connectedness analysis. International Review of Economics & Finance, 2019, 64: 290-307.
    [11]
    Mazzarisi P, Zaoli S, Campajola C, et al. Tail Granger causalities and where to find them: Extreme risk spillovers vs spurious linkages. Journal of Economic Dynamics and Control, 2020, 121: 104022.
    [12]
    Restrepoa N, Uribea J M, Manotasa D. Financial risk network architecture of energy firms. Applied Energy, 2018, 215(2): 630-642.
    [13]
    Wen T, Wang G J. Volatility connectedness in global foreign exchange markets. Journal of Multinational Financial Management, 2020, 54: 100617.
    [14]
    Demirer M, Diebold F X, Liu L, et al. Estimating global bank network connectedness. Journal of Applied Econometrics, 2018, 33(1): 1-15.
    [15]
    Chen Y, Hu J, Zhang W P. Too connected to fail? Evidence from a Chinese financial risk spillover network. China & World Economy, 2020, 28(6): 78-100.
    [16]
    Schreiber T. Measuring information transfer. Physical Review Letters, 2000, 85 (2): 461-464.
    [17]
    Kim J, Kim G, An S, et al. Entropy-based analysis and bioinformatics-inspired integration of global economic information transfer. PLoS ONE, 2013, 8(1): e51986.
    [18]
    Gong C, Tang P, Wang Y. Measuring the network connectedness of global stock markets. Physica A: Statistical Mechanics and its Applications, 2019, 535: 122-351.
    [19]
    Shannon C. A mathematical theory of communication. The Bell System Technical Journal, 1948, 27(3): 379-423.
    [20]
    Zhu X, Chang X, Li R, et al. Portal nodes screening for large scale social networks. Journal of Econometrics, 2019, 209(2): 145-157.
    [21]
    Dou B, Parrella M L, Yao Q. Generalized yule-walker estimation for spatio-temporal models with unknown diagonal coefficients. Journal of Econometrics, 2016, 194(2): 369-382.
    [22]
    Garman M B, Klass M J. On the estimation of security price volatilities from historical data. Journal of Business, 1980, 53(1): 67-78.
    [23]
    Alizadeh S, Brandt M, Dieebold F. Range-based estimation of stochastic volatility models. Journal of Finance, 2002, 57(3): 1047-1091.
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