Exchange Rate Volatility, Production Networks, and Stock Market Risk during the Sino-US Trade Friction
ZHOU Yinggang, XIAO Xiao
Center for Macroeconomic Research/School of Economics/ Wang Yanan Institute for Studies in Economics, Xiamen University; Wang Yanan Institute for Studies in Economics, Xiamen University
Summary:
Sino-US trade friction has created a major challenge to China's efforts to ensure high-level opening-up and maintain production and supply chains. Previous studies have discussed the impact of trade frictions on consumption, employment, economic welfare, and industrial output. This paper examines financial market risks in China and the U.S. against the backdrop of recent trade frictions, and in particular the impact of the expected exchange rate change on stock market returns. An empirical spatial auto-regressive (SAR) model is derived from a static general equilibrium model, in which the production network measured by the input-output table is used as the spatial weight matrix, which is then given economic meaning. Based on the SAR model, the direct impact of exchange rate changes on the stock market and the indirect impact through the production network are estimated. We further calculate the direct and indirect impacts on various industries and assess the average impacts on industries subject to tariff sanctions versus those not subject to sanctions. The main findings are summarized as follows. First, during the trade friction period, the expected exchange rate change has significant negative impact on stock market returns in China and the United States. Indicating that the correlation between the stock market and the foreign exchange market increased during the friction period. Second, expected bilateral exchange rate changes between China and the United States not only directly affect the stock returns of various industries but also affect a certain proportion of the network because of the production links between industries. The proportion of network effects is 50% in the Chinese sample and 37% in the American sample, which reflects the stronger links between Chinese industries. Third, on average, both industries affected by tariff sanctions and those not affected are influenced by exchange rate changes; the latter is more affected through the network. The fimdings have important policy implications. First, trade frictions have mutually damaging consequences. Therefore, we should strengthen communication and cooperation between countries. Second, the linkage between the foreign exchange market and the stock market is enhanced during the period of trade friction. Therefore, special attention should be paid to high-level risk and cross-market contagion in periods of economic and financial turmoil. Third, the connection between the entity units (such as a production connection) can transmit the impact of financial shock (such as the impact of exchange rates on the stock market). Therefore, we should take into account the factors at the entity level when attempting to prevent and resolve financial risks. Finally, as an economic power, China should accelerate the improvement of the industrial chain, promote economic internal circulation, and enhance its ability to resist external shocks. This study contributes to the literature in several ways.First, this paper examines the impact of Sino-US trade frictions on the financial market, studies the financial market risk magnified by inter-industry production linkages and gives some new insight from the perspective of industrial linkages. Second, most studies of trade frictions focus on their impact on entity-level activities. From the perspective of the production network, this paper compares and analyzes the changes in the financial markets of China and the United States in a period of trade frictions, focusing on the impact of exchange rate changes on stock market returns. Finally, this paper uses a new method to estimate and decompose the impact of exchange rate changes, and it obtains several new findings. Using the SAR model, with the production network as the spatial weight matrix, this paper quantitatively evaluates the direct impact of exchange rate changes on the stock market and the network impact caused by inter-industry production links.
Acemoglu, D., V. M. Carvalho, A. Ozdaglar, and A. Tahbaz-Salehi. 2012. “The Network Origins of Aggregate Fluctuations”, Econometrica, 80(5): 1977～2016.
[17]
Acemoglu, D., U. Akcigit, and W. Kerr. 2016. “Networks and the Macroeconomy: An Empirical Exploration”, NBER Macroeconomics Annual, 30(1): 273～353.
[18]
Acemoglu, D., and P. D. Azar. 2020. “Endogeous Production Networks”, Econometrica, 88(1): 33～82.
[19]
Atalay, E., Hortaçsu A., and C. Syverson. 2014. “Vertical Integration and Input Flows”, American Economic Review, 104(4): 1120～1148.
[20]
Atalay, E. 2017. “How Importan t are Sectoral Shocks”, American Economic Journal: Macroeconomics, 9(4): 254～80.
[21]
Ahern, K., and J. Harford. 2014. “The Importan ce of Industry Links in Merger Waves”, Journal of Finance, 69(2): 527～576.
[22]
Baqaee, D. R. 2018. “Cascading Failures in Production Networks”, Econometrica, 86(5): 1819～1838.
[23]
Baqaee, D. R., and E. Farhi. 2018. “Macroeconomics with Heterogeneous Agents and Input-Output Networks”, NBER Working Paper, No.24684.
[24]
Baqaee, D. R., and E. Farhi. 2020. “Productivity and Misallocation in General Equilibrium”, Quarterly Journal of Economics, 135(1): 105～163.
[25]
Bernard, A. B., A. Moxnes, and Y. U. Saito. 2019. “Production Networks, Geography, and Firm Performance”, Journal of Political Economy, 127(2): 639～688.
[26]
Branson, W. 1983. “Macroeconomic Determinants of Real Exchange Rate Risk”, in R. J. Herring, eds: Managing Foreign Exchange Rate Risk, Cambridge: Cambridge University Press.
[27]
Carvalho, V. M. 2014. “From Micro to Macro via Production Networks”, Journal of Economic Perspectives, 28(4): 23～47.
[28]
Carvalho, V. M., and A. Tahbaz-Salehi. 2019. “Production Networks: A Primer”, Annual Review of Economics, 11(8): 635～663.
[29]
Conley, T. G., and B. Dupor. 2003. “A Spatial Analysis of Sectoral Complementarity”, Journal of Political Economy, 111(2): 311～352.
[30]
Cooley, T. F., and G. D. Hansen. 1989. “The Inflation Tax in a Real Busin ess Cycle Model”, American Economic Review, 79(4): 733～748.
[31]
Dornbusch, R., and S. Fischer. 1980. “Exchange Rates and the Current Account”, American Economic Review, 70(5): 960～971.
[32]
Dupor, B. 1999. “Aggregation and Irrelevance in Multi-sec tor Models”, Journal of Monetary Economics, 43(2): 391～409.
[33]
Elhorst, J. P. 2014. Spatial Econometrics: from Cross-Sectional Data to Spatial Panels, Berlin: Springer.
[34]
Frankel, J. A. 1992. “Monetary and Portfolio Balance Models of Exchange Rate Determination”, International Economic Policies ＆ Their Theoretical Foundations, pp.793～832.
[35]
Gavin, M. 1992. “The Stock Market and Exchange Rate Dynamics”, Journal of International Money ＆ Finance, 8(2): 181～200.
[36]
Gorodnichenko, Y., and M. Weber. 2016. “Are Sticky Prices Costly？ Evidence from the Stock Market”, American Economic Review, 106(1): 165～199.
[37]
Hau, H., and H. Rey. 2006. “Exchange Rates, Equity Prices, and Capital Flows”, Review of Financial Studies, 19(1): 273～317.
[38]
Horvath, M. 1998. “Cyclicality and Sectoral Linkages: Aggregate Fluctuations from Sectoral Shocks”, Review of Economic Dynamic, 1(4): 781～808.
[39]
Horvath, M. 2000. “Sectoral Shocks and Aggregate Fluctuations”, Journal of Monetary Economics, 45(1): 69～106.
[40]
Kuersteiner, G. M., and I. R. Prucha. “Dynamic Spatial Panel Models: Networks, Co mmon Shocks, and Sequential Exogeneity”, Econometrica, 88(5): 2109～2146.
[41]
Leontief, W. 1941. The Structure of American Economy, 1919-1929: An Empirical Application of Equilibrium Analysis, Cambridge: Harvard Univ. Press.
[42]
LeSage, J. P., and R. K. Pace. 2009. Introduction to Spatial Econometrics, Boca Raton: CRC Press Taylor ＆ Francis Group.
[43]
Liu, E. 2019. “Industrial Policies in Production Networks”, Quarterly Journal of Economics, 134(4): 1883～1948.
[44]
Long, J. B., and C. I. Plosser. 1983. “Real Busin ess Cycles”, Journal of Political Economy, 91(1): 39～69.
[45]
Lucas, R. E. 1977. “Understan ding Busin ess Cycles”, in Carnegie-Rochester Conference Series on Public Policy, Vol.5: 7～29.
[46]
Oberfield, E. 2018. “A Theory of Input-Output Architecture”, Econometrica, 86(2): 559～589.
[47]
Ozdagli, A., and M. Weber. 2017. “Monetary Policy through Production Networks: Evidence from the Stock Market”, NBER Working Paper, No.23424.
[48]
Pasten, E., R. Schoenle, and M. Weber. 2020. “The Propagation of Monetary Policy Shocks in a Heterogeneous Production Economy”, Journal of Monetary Economics, 116: 1～22.
[49]
Sercu, P., and C. Vanhulle. 1992. “Exchange Rate Volatility, International Trade, and the Value of Exporting Firms”, Journal of Banking and Finance, 16(1): 155～182.
