Macroeconomic perspective on constructing financial vulnerability indicator in China
Abstract
This paper attempts to develop a financial vulnerability indicator for China as a barometer for the state of financial vulnerability in the Chinese financial market, possibly for real-time application. Twelve variables from different sectors are utilised to extract a common vulnerability component using a dynamic approximate factor model. Through the implementation of a Markovswitching Bayesian vector autoregression (MSBVAR) model, the empirical results indicate that a high-vulnerability episode is associated with substantially lower economic activity, but a low-vulnerability episode does not incur substantial changes in economic activity. Notably, the constructed indicator can serve as a real-time early warning system to signify vulnerabilities in the Chinese financial market.
First published online 20 November 2020
Keyword : financial vulnerability indicator, financial crises, early warning system, dynamic factor model, Markov-switching model, China
How to Cite
Kuek, T.-H., Puah, C.-H., Arip, M. A., & Habibullah, M. S. (2021). Macroeconomic perspective on constructing financial vulnerability indicator in China. Journal of Business Economics and Management, 22(1), 181-196. https://doi.org/10.3846/jbem.2020.13220
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Pasricha, G., Roberts, T., Christensen, I., & Howell, B. (2013). Assessing financial system vulnerabilities: an early warning approach. Bank of Canada Review, (Autumn 2013), 10–19.
Puah, C. H., Kuek T. H., & Arip, M. A. (2017). Assessing Thailand’s financial vulnerability: An early warning approach. Business and Economic Horizons, 13(4), 496–505. https://doi.org/10.15208/beh.2017.34
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Sims, C. A., Waggoner, D. F., & Zha, T. (2008). Methods for inference in large multi-equation Markovswitching models. Journal of Econometrics, 146(2), 255–274. https://doi.org/10.2139/ssrn.962420
Sims, C. A., & Zha, T. (1999). Error bands for impulse responses. Econometrica, 67(5), 1113–1156. https://doi.org/10.1111/1468-0262.00071
Stona, F., Morais, I. A. C., & Triches, D. (2018). Economic dynamics during periods of financial stress: Evidences from Brazil. International Review of Economics & Finance, 55, 130–144. https://doi.org/10.1016/j.iref.2018.02.006
Tanaka, K., Kinkyo, T., & Hamori, S. (2018). Financial hazard map: Financial vulnerability predicted by a random forests classification model. Sustainability, 10(5), 1530. https://doi.org/10.3390/su10051530
Suidarma, I. M., Indrawati, Y., Diatmika, I. G. N. D., & Anggaradana, I. N. (2017). Financial system vulnerability indicators in Indonesia. International Journal of Economics and Financial Issues, 7(5), 299–306.
Thakor, A. (2015). Lending booms, smart bankers, and financial crises. American Economic Review: Papers and Proceedings 2015, 105(5), 305–309. https://doi.org/10.1257/aer.p20151090
Tng, B. H., & Kwek, K. T. (2015). Financial stress, economic activity and monetary policy in the ASEAN-5 economies. Applied Economics, 47(48), 5169–5185. https://doi.org/10.1080/00036846.2015.1044646
van Roye, B. (2014). Financial stress and economic activity in Germany. Empirica, 41(1), 101–126. https://doi.org/10.1007/s10663-013-9224-0
Yu, Y. D. (2010). The impact of the Global Financial Crisis on the Chinese economy and China’s policy responses. Third World Network, Penang, Malaysia.
Arip, M. A., Kuek, T. H., & Puah, C. H. (2019). Forecasting financial vulnerability in Malaysia: A nonparametric indicator approach. Asian Journal of Business Research, 9(2), 113–120. https://doi.org/10.1407/ajbr.190063
Banbura, M., & Modugno, M. (2014). Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data. Journal of Applied Econometrics, 29(1), 133–160. https://doi.org/10.1002/jae.2306
Bruggemann, A., & Linne, T. (2002). Are the central and Eastern European transition countries still vulnerable to a financial crisis? Results from the signals approach. (IWH Discussion Papers No. 157). Halle Institute for Economic Research, Saale, Germany. https://doi.org/10.2139/ssrn.1015699
Bussiere, M., & Fratzscher, M. (2006). Towards a new early warning system of financial crises. Journal of International Money and Finance, 25, 953–973. https://doi.org/10.1016/j.jimonfin.2006.07.007
Cardarelli, R., Elekdag, S., & Lall, S. (2011). Financial stress and economic contractions. Journal of Financial Stability, 7, 78–97. https://doi.org/10.1016/j.jfs.2010.01.005
Cecchetti, S. G., Mohanty, M. S., & Zampolli, F. (2011). The real effects of debt (BIS Working Paper No. 352). Bank for International Settlements, Basel, Switzerland.
Cevik, E. I., Dibooglu, S., & Kenc, T. (2013a). Measuring financial stress in Turkey. Journal of Policy Modeling, 35, 370–383. https://doi.org/10.1016/j.jpolmod.2012.06.003
Cevik, E. I., Dibooglu, S., & Kenc, T. (2016). Financial stress and economic activity in some emerging Asian economies. Research in International Business and Finance, 36, 127–139. https://doi.org/10.1016/j.ribaf.2015.09.017
Cevik, E. I., Dibooglu, S., & Kutan, A. M. (2013b). Measuring financial stress in transition economies. Journal of Financial Stability, 9, 597–611. https://doi.org/10.1016/j.jfs.2012.10.001
Chow, G., & Lin, A. (1971). Best linear unbiased interpolation, distribution, and extrapolation of time series by related series. The Review of Economics and Statistics, 53, 372–375. https://doi.org/10.2307/1928739
Claessens, S., & Kose, M. A. (2013). Financial crises: explanations, types, and implications. (IMF Working Paper Series No. 28). International Monetary Fund, Washington D.C., United States. https://doi.org/10.5089/9781475561005.001
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1–22. https://doi.org/10.1111/j.2517-6161.1977.tb01600.x
Ferrer, R., Jammazi, R., Bolos, V. J., & Benitez, R. (2018). Interactions between financial stress and economic activity for the U.S.: A time- and frequency-varying analysis using wavelets. Physica A: Statistical Mechanics and its Applications, 492, 446–462. https://doi.org/10.1016/j.physa.2017.10.044
Illing, M., & Liu, Y. (2006). Measuring financial stress in a developed country: An application to Canada. Journal of Financial Stability, 2, 243–265. https://doi.org/10.1016/j.jfs.2006.06.002
International Monetary Fund. (2010). People’s Republic of China: 2010 Article IV consultation. (IMF Country Report No. 10/238). Washington D.C., United States. https://doi.org/10.5089/9781455205868.002
International Monetary Fund. (2016). The People’s Republic of China: Selected issues. (IMF Country Report No. 16/271). Washington D.C., United States. https://doi.org/10.5089/9781475524383.002
International Monetary Fund. (2018). People’s Republic of China: 2018 Article IV consultation. (IMF Country Report No. 18/240). Washington D.C., United States. https://doi.org/10.5089/9781484338032.002
Ishrakieh, L. M., Dagher, L., & Hariri, S. E. (2020). A financial stress index for a highly dollarized developing country: The case of Lebanon. Central Bank Review, 20(2), 43–52. https://doi.org/10.1016/j.cbrev.2020.02.004
Kaminsky, G. L., & Reinhart, C. M. (1999). The twin crises: the causes of banking and balance-ofpayments problems. The American Economic Review, 89(3), 473–500. https://doi.org/10.1257/aer.89.3.473
Kaminsky, G., & Reinhart, C. M. (1996). The twin crises: The causes of banking and balance-of-payments problems (International Finance Discussion Paper, No. 544). Board of Governors of the Federal Reserve System, Washington D.C., United States. https://doi.org/10.17016/IFDP.1996.544
Kuek, T. H., Puah, C. H., & Arip, M. A. (2019). Predicting financial vulnerability in Malaysia: Evidence from the signals approach. Research in World Economy, 10(3), 89–98. https://doi.org/10.5430/rwe.v10n3p89
Li, F. C., & Xiao, H. Y. (2016). Early warning system of financial stress events: a credit-regime-switching approach. (Working Paper No. 21). Bank of Canada, Ottawa, Canada.
Louzis, D. P., & Vouldis, A. T. (2012). A methodology for constructing a financial systemic stress index: An application to Greece. Economic Modelling, 29, 1228–1241. https://doi.org/10.1016/j.econmod.2012.03.017
Mishkin, F. S. (1991). Anatomy of a financial crises. (NBER Working Paper Series, No. 3934). National Bureau of Economic Research, Massachusetts, United States.
Monin, P. J. (2019). The OFR financial stress index. Risks, 7(25), 1–21. https://doi.org/10.3390/risks7010025
Pasricha, G., Roberts, T., Christensen, I., & Howell, B. (2013). Assessing financial system vulnerabilities: an early warning approach. Bank of Canada Review, (Autumn 2013), 10–19.
Puah, C. H., Kuek T. H., & Arip, M. A. (2017). Assessing Thailand’s financial vulnerability: An early warning approach. Business and Economic Horizons, 13(4), 496–505. https://doi.org/10.15208/beh.2017.34
Sahoo, J. (2020). Financial stress index, growth and price stability in India: Some recent evidence. Theoretical and Applied Economics, 1(622), 105–124. https://doi.org/10.1080/19186444.2020.1768789
Sims, C. A., Waggoner, D. F., & Zha, T. (2008). Methods for inference in large multi-equation Markovswitching models. Journal of Econometrics, 146(2), 255–274. https://doi.org/10.2139/ssrn.962420
Sims, C. A., & Zha, T. (1999). Error bands for impulse responses. Econometrica, 67(5), 1113–1156. https://doi.org/10.1111/1468-0262.00071
Stona, F., Morais, I. A. C., & Triches, D. (2018). Economic dynamics during periods of financial stress: Evidences from Brazil. International Review of Economics & Finance, 55, 130–144. https://doi.org/10.1016/j.iref.2018.02.006
Tanaka, K., Kinkyo, T., & Hamori, S. (2018). Financial hazard map: Financial vulnerability predicted by a random forests classification model. Sustainability, 10(5), 1530. https://doi.org/10.3390/su10051530
Suidarma, I. M., Indrawati, Y., Diatmika, I. G. N. D., & Anggaradana, I. N. (2017). Financial system vulnerability indicators in Indonesia. International Journal of Economics and Financial Issues, 7(5), 299–306.
Thakor, A. (2015). Lending booms, smart bankers, and financial crises. American Economic Review: Papers and Proceedings 2015, 105(5), 305–309. https://doi.org/10.1257/aer.p20151090
Tng, B. H., & Kwek, K. T. (2015). Financial stress, economic activity and monetary policy in the ASEAN-5 economies. Applied Economics, 47(48), 5169–5185. https://doi.org/10.1080/00036846.2015.1044646
van Roye, B. (2014). Financial stress and economic activity in Germany. Empirica, 41(1), 101–126. https://doi.org/10.1007/s10663-013-9224-0
Yu, Y. D. (2010). The impact of the Global Financial Crisis on the Chinese economy and China’s policy responses. Third World Network, Penang, Malaysia.