In economics, analyzing variables’ fluctuations around the world is practiced by applying shocks in relevant economies. Most of the time, authors investigate shocks from the U.S. [1,2]. However, as China's economic influence on the world has increased over the decades, we begin to notice works investigating how Chinese shocks impact other regions [2]. This article is a contribution to this growing interest in understanding Chinese shocks and how they affect the world economy. We analyze Chinese financial shocks and verify how they spread throughout the system, particularly seeking to observe how emerging market economies (EMEs) and advanced economies (ADs) behave after these shocks. We also identify transmission channels, which clarify how financial shocks impact the real sector. We used the GVAR to produce econometric estimates; this model allows us to incorporate the international environment into the analysis, providing a proxy for the vulnerability of economies to external shocks. The GVAR has the advantage of liberating each region to be modeled according to its idiosyncratic factors. Moreover, this model uses bilateral trade to connect each region. As China’s trade pattern with other economies changed remarkably over time, we can compare Chinese shocks in different periods according to the trade evolution. In short, the GVAR is fit to incorporate spillover effects. The results show that a Chinese negative credit shock causes a world recession, with the exchange rate markets as the main transmission channel. This shock triggers generalized "flight to quality," in which capital flows to safer places, which is the U.S. economy in our model. This finding is interesting because the shock happened in China, but caused broad exchange rate devaluations around the world. The second Chinese financial shock was on the exchange rate, and the results closely followed those of the credit shock. The difference was related to the transmission channel, with the stock market connecting the financial market with the real sector. All shocks affected emerging and advanced economies variables, especially the GDP. We also found that the Chinese influence changed according to bilateral trade. When we compared shocks using bilateral trade of 1985, 1995, 2005, and 2014-16, the shocks from the new millennium had a greater influence on the world economy, a finding that held even when we analyzed commodities prices. However, while the difference of the shocks between millenniums is remarkable, when we assess shocks from the same millennium we no longer observe this difference between the shocks. The first contribution of this article is the focus on the Chinese financial shock. In general, studies analyzing how Chinese shocks impact the world economy concentrates in the real sector, specifically in GDP shocks [3] [4]. While we follow these works to verify how Chinese influence develops over time, we advance the study by looking at Chinese financial shocks. We seek to add to the literature how Chinese financial markets can affect other regions and their financial and real sectors. What motivated us to pursue this research was the recent nervousness with the bankruptcy (or default) of Evergrande and the possible effects this event could cause on the world. Evergrande's episode inspired us to analyze Chinese negative credit shocks to simulate this event. Zhang et al. [5] investigated the financial risk in China without treating the international economy. Hence, implicitly, they modeled China as a closed economy. Other econometric specifications, such as the VAR and TVP-VAR-SV, use Chinese financial variables with other relevant factors, but they fail to treat China as a country integrated into the international economy [5,6]. VAR models employ relevant variables to proxy the external environment without modeling the world economy explicitly. Shehzad et al [1] incorporated our comments when analyzing the relationship between the U.S. and China using VARX. However, their model included only two countries and few financial variables. We complement these studies by using a model with 33 countries, explicitly modeling the world economy through domestic variables of each region and transmission channels to understand how Chinese financial shocks spread throughout the system. In short, our estimation strategy treats China as a region integrated into the international economy. Some works relate the financial cycle of China with relevant economies such as the U.S., the U.K., Japan, and the Eurozone [7,8] Jiang et al. [9] showed spillovers from China’s real estate over its economy, and Min et al. (2021) displayed how Chinese bank credit affects the country. Both of these papers restrict China’s influence to a few places, while we explore the spillover effects of the Chinese financial system on the whole world, testing credit and exchange rate shocks in particular. Adarov [10] studied a sample with a size similar to ours. He concluded that global financial cycles follow the movements of the U.S. T-bill and the VIX index, and BenSaida and Litini [11] focused on the Eurozone. These works motivated us to pursue ours because it raised the question: What about China? Given the remarkable growth of China's importance in the world economy, we speculated that the Chinese financial system could provoke pronounced international fluctuations. Thus, we consider our paper a continuation of the research evaluating the Chinese financial influence on the world economy. Furthermore, our method allows us to study the Chinese financial influence according to bilateral trade, which addresses the soaring economic integration of China into the international economy. Many articles used the GVAR to explore international connections among countries. Bettendorf [12] and Alzuabi et al.[13] employed this model to analyze financial shocks in the system, mainly focusing on the Eurozone and the U.S., respectively. Using the same econometric model, we now focus on China, investigating how Chinese financial shocks impact the prices of commodities. Wen et al. [14] indicated that the Chinese monetary policy affects the oil price. Here we broaden this investigation by adding three commodities prices: oil prices, agricultural materials prices, and metal prices. Finally, the last contribution is to analyze the responses of EMEs and ADs to Chinese financial shocks. Most articles typically analyze only one of these groups - most times the EMEs - but not both at the same time, as Chudik and Fratzscher 2011 did for the U.S. case. We use the GVAR to create three regions: Asia, Latin America, and the Eurozone, which addresses this issue. The article is structured as follows: Section 2 briefly reviews the literature; Section 3 describes the GVAR; Section 4 presents the data and econometric strategies; Section 5 displays the results; and finally, Section 6 closes the article with a few comments.

The Increasing Influence of China on the International Economy

Figure 1 suggests the growing influence China has on the world economy. Each line depicts the percentage of China’s GDP to an aggregate. The participation of China in these aggregates has changed considerably since 1960. The turning point seems to have been around the 1990s, when all lines increase remarkably. However, we can detect the Chinese economy climbing in the 1980s in the low and middle-income economies.

Figure 1: China GDP Over the Years (%).

In the EMEs economies (low and middle-income group), China's GDP was proportional at 9.6% of the GDP of this group in 1990. Thirty years later, this value increased to 47.5%. This increase of around five times has been investigated by the business fluctuation literature. Cesa-Bianchi et al. [15] asserted that Chinese influence on Latin America has increased since the 1990s, with trade being the main channel of connection. In particular, the authors showed that Chinese GDP shocks have been more influential on Latin American economies, while the U.S. shock has lost prevalence, a difference that is attributed to the growing trade between China and Latin America. Çakir and Kabundi [17] had similar conclusions to the Cesa-Bianchi et al [16] study. While the latter focused on Latin American economies, Çakir and Kabundi [17] analyzed the Chinese influence on the BRIS group (Brazil, Russia, India, and South Africa). Their estimates reinforce the increasing Chinese influence on EMEs economies, with trade linking the regions. As Cesa-Bianchi et al. (2012) asserted that U.S. influence on Latin American economies is being substituted by the Chinese, likewise Wall and Eyden [17] investigated a similar change in South Africa. Their conclusion aligns with what we have been discussing: based on the trade between South Africa and China, they found that Chinese influence has increased over the years. Furthermore, the authors found that U.S. influence on South Africa had decreased. They used these findings to explain why South Africa remained relatively unaffected following the 2008 financial crisis. Lastly, they recommend that policymakers pay attention to the changes of foreign economies, given that the epicenter of external influence on South Africa changed from the U.S. to China. Jenkins et al. [18] discussed the consequences of China’s pervasiveness on Latin American economies, mainly increasing trade and foreign direct investment. This study shows that there are beneficial and detrimental effects of this structural change, varying among economies and sectors. We can connect this information with Wall and Eyden [19], whose study advanced the hypothesis that South Africa has become more sensitive to the domestic policies of China. Chinese influence is apparent not only on EMEs, but also on advanced economies. Coming back to Figure 1, we can see that in 1992 China's GDP participation in high-income countries was 2%. In 2020, this value had increased to 27.6%. Bloom et al. (2016) asserted that Chinese import competition has relevant effects on European countries, affecting innovation and employment. In the world economy, China's GDP has increased from 1.6% in 1988 to 17.4% in 2020. Although this increase is not as remarkable as that of the low and middle-income group, it is notable how China grew over time. According to Eickmeier and Kuhnlenz (2018), China presents a relevant influence on world prices.

In this section, we describe the GVAR model. All the model constructions are mainly based on Pesaran et al. (2004), which presented a GVAR model with one lag to both domestic and foreign variables. The first step to building the GVAR is to create individual models with foreign and domestic variables,  (Equation 1). The term  denotes the domestic variables of region i in time t. Regions vary from 0 (reference region, the US) to N, and the subscript t varies from 1 to T. The terms  and  represent the constant and the trend.

We use bilateral trade between regions i and j, , to produce the foreign variables,  (Equation 2). We multiply the domestic variables of region j with bilateral trade, giving rise to the foreign variables. The idea behind this procedure is to simulate the international environment, i.e., how vulnerable a region is to the world economy. According to Equation 2, we display the region's exposure to international events through bilateral trade.

Returning to Equation 1, we can verify that foreign variables are incorporated on the right side of the . Besides these variables, we have the idiosyncratic shock . We suppose this term is serially uncorrelated. Continuing the GVAR description, we create a new vector,  (Equation 3), which has domestic and foreign variables. This step is useful to the solution of the GVAR.

Now we can rewrite Equation 1, giving us Equation 4:

Another useful step to solve the GVAR is to create the global vector, . This vector possesses all domestic variables of the model. To see that, note that the term  represents all domestic variables in region 0,  the domestic variables of region 1. This reasoning follows until the last term of the global vector, . We can combine the global vector with the link matrix, . The link matrix gathers the shares of bilateral trade, allowing us to write the identity (Equation 5):

Using Equation 5 in Equation 4, we have:

Finally, we obtain the GVAR by multiplying G for its inverse (Equation 8). In general, G is a nonsingular matrix:

When we have unit-root in some series, we use the GVAR in the error-correcting form (Equation 9). Pesaran et al. (2004) provide details about this procedure. One of the necessary conditions is to verify if the series are cointegrated; in other words, if they present long-term relationships.

In the next part, we present the data, aggregations, and our estimation strategy, which considers the Chinese evolution in international trade. The GVAR has valuable features, like allowing us to assess regions’ influence over time through bilateral trade. It also gives us the liberty to model each region individually. We explore these characteristics in the following section.

The database is composed of six domestic variables: real GDP, real interest rate, real exchange rate, real stock exchange, consumer prices index, and private credit/GDP. We obtained the first five variables from Mohaddes and Raissi [20], where we also took the weighing matrix (shares of bilateral trade). Because we aim to investigate how Chinese financial shocks spread to the world real sector, we opted for a credit definition related to the private sector. From BIS, we used credit to the private non-financial sector from banks. We believe this credit properly incorporates financial volatility happening abroad and its impact on domestic economies.  During the econometric exercises, we tested three global variables working as proxies for commodities prices. All of them come from Mohaddes and Raissi [20] and include oil prices, agricultural materials prices, and metal prices. We follow Dees et al. (2007), putting the oil price as a domestic variable in the U.S. while using the other variables as domestic variables in China’s model. Moreover, we employ two uncertainty (financial and macroeconomic) indexes related to the U.S., both from Ludvigson et al. [21] We specify when we use these variables in the results presented in the next section. Given the importance of the U.S. economy - mainly considering its capacity to influence the whole world - we have to use caution when building its model, which is a recommendable step in GVAR models (Dees et al. 2007). Hence, in the main setup in the U.S., we include only the GDP and exchange rate as foreign variables (Equation 10). The exchange rate serves as a domestic variable in all regions except the U.S., because this variable is the domestic currency compared to the U.S. dollar. On the other hand, we incorporate the exchange rate as a foreign variable only in the U.S. Another point, which we have already discussed, are the global variables. They serve as domestic variables only in the U.S. and China. In the other regions, we treat them as foreign variables. Equation (10) helps visualize these steps:

The terms ?gdp?_it,i_it,e_it,?cred?_it,?oil?_it are, respectively, GDP, interest rate, exchange rate, credit, and oil price. We do not put the domestic price, the stock exchange, and other global variables because our intention in Equation (10) is to illustrate the construction of the model. However, we change this setup during the econometric results. Another common practice in GVAR works is to build the Eurozone as an aggregate of countries. We followed the same procedure, building the Eurozone by the average GDP at PPP over the years (2014-2016). Hence, eight economies (Austria, Belgium, Finland, France, Germany, Italy, the Netherlands, and Spain) become one region. We also created two other regions with only emerging market economies: Asia and Latin America. We aggregated India, Indonesia, Malaysia, Philippines, Singapore, and Thailand to make up Asia, and we used Argentina, Brazil, Chile, Mexico, and Peru for Latin America. Besides the advantage of separating economies by the development level, this procedure reduces the number of regions in the GVAR which helps stabilize the model. Consequently, our model has 17 regions, or 33 countries if we do not consider the aggregations. In the weighing matrix, the matrix responsible for building foreign variables, we used two different models. In the first model, we define a specific period, employing fixed weights; we then based foreign variables and the solution of the GVAR on these weights. In practical terms, this means that bilateral trade was roughly the same over the years. However, when we analyze countries with changing trade patterns, such as China, we can utilize time-varying weights, in which the construction of the foreign variables occurs according to the bilateral trade of each period. But, with this option, we still have to choose the period in which to solve the GVAR. Analyzing the influence of China on the world economy, Cesa-Bianchi et al. [22] opted for the second method. We opted to utilize both because the results do not change significantly, and we interpret this procedure as additional proof of the robustness of the results. Using alternative weighing periods allows us to verify how Chinese influence changes over time according to China’s participation in bilateral trade. Again, our strategy closely mirrors that of Cesa-Bianchi et al. (2012). We used weights relative to 1985, 1995, 2005, and 2014-16. We also intercalated these periods with their averages: 1985-87, 1995-97, 2005-07, and 2014-16, which smoothes possible changes in the trade pattern. Finally, due to data limitations, we employed the period 1985Q4-2016Q4, with all variables in the logarithmic form.

Credit Shock

Analyze the responses of emerging economies and relevant world regions. The credit shock is our strategy to study how the collapse of a company like Evergrande can spread throughout the system.  The next five figures portray a negative credit shock on China and the responses of regions’ variables, with all values in percentages. The dashed lines represent bootstrap confidence intervals at 90%. In this first setup, the model is composed of GDP, exchange rate, private credit, interest rate, and agricultural raw materials index, our proxy for commodities price - during the article we employ other proxies to illustrate the volatility of commodities from Chinese shocks. The weights employed in the weighing matrix are the average of bilateral trade in the years 2014-2016. AsZwe advance the analysis, we will change this setup to test the robustness of the results. Figure 2 shows that the Chinese credit market has a significant influence on the world economy. Starting with the emerging market economies, both regions’ GDP falls right after the credit shock. Asia exhibits an accumulated fall of GDP over 0.2% while Latin America displays a reduction of 0.6%, but Asia estimates are insignificant. These results converge with the ones presented by Cesa-Bianchi et al. [23], Bloom et al. (2016), and Çakir and Kabundi [23], with the distinguishing characteristic that the Chinese influence occurred initially in the financial sector.  

Figure 2: GIRF of China Negative Credit Shock and GDP Responses.\

Figure 3: GIRF of China Negative Credit Shock and Credit Responses.

Figure 4: GIRF of China Negative Credit Shock and Interest Rate Responses.

Figure 5: GIRF of China Negative Credit Shock and Exchange Rate Responses.

Production also decreased in the advanced regions. Eurozone GDP had a trajectory similar to that of Latin America, with a fall of 0.6%. In the other three regions, Japan, the U.K., and the U.S., the GDP responses were significant only in the first quarters. The negative credit shock seemed to affect these economies in the short-term, losing power over the periods. Once the shock was absorbed, Japan, the U.K., and the U.S. no longer had statistically significant responses. Comparing results between EMEs and advanced economies, the estimates suggest that a credit shock from China is more prominent in the former regions, except for the Eurozone.

According to the model setup, we have three possible transmission channels to explain how a financial shock affects the real sector (GDP): credit, interest rate, and exchange rate. In Figure 3, which displays the credit market, we see that the estimates do not support that credit connects the financial and real sectors. The Eurozone and the U.K. had significant credit falls in the first period, but they became nonsignificant in both regions after that. We see the same issue with these three regions as well as with the rest in the study: the estimates failed to be significant enough to establish the transmission channel through which the financial shock impacted the real sector. Therefore, we must explore transmission channels in other segments of their economies. Searching for another possible transmission channel, Figure 4 reports interest rate responses. Contrary to Figure 3, all regions had significant estimates, with Japan being the only exception. Asia, the Eurozone, the U.K., and the U.S. exhibited expansionary monetary policies, but the value of the interest rate change is negligible. Asia had the highest percentage change with a fall of 0.1%. Although interest rates reacted to the Chinese shock, we determined the movements were irrelevant. The exception is Latin America, where we see the interest rate rise by 1% at the end of the period. As illustrated in the next figure, the response in Latin America can be explained by noting that the region suffers one of the strongest capital outflows after the shock.  Figure 5 presents the exchange rate, our last possible transmission channel. The results suggest that the Chinese credit shock spread to the other regions in this economic segment in particular. Asia, Latin America, the Eurozone, and the U.K. had increases of 1%, 2%, 1.5%, and 3%, respectively. We detect the famous process of "flight to quality," in which capital flows to safer places, which is the U.S. Eickmeier and Ng [24] realized a similar study about credit but with the shock originating from the U.S. rather than China. As Figure 5 shows, the authors also identified capital outflows after the U.S. credit shock. Here we portray that Chinese shock triggers domestic devaluations both in EMEs and in advanced economies. We deduce that a negative credit shock from China can cause profound volatility in international financial markets, with effects reaching domestic real sectors of economies in different continents. However, as in the other tests, Japan’s estimates were insignificant once again. With this information, now we can build a coherent picture. The Chinese negative credit shock affects emerging and advanced economies through the exchange rate channel, triggering prominent capital outflows. In most regions, the central bank reacts by reducing interest rates, while in Latin America they tighten monetary policy, increasing interest rates. One possible explanation is the considerable outflow of capital we see in the region; however, this hypothesis fails to justify the case of the U.K., which had a 3% increase in its currency (depreciation) and applied an expansionary monetary policy. Besides the exchange rate, in Asia the credit also worked as a transmission channel of the shock, decreasing following the shock. The final result is the negative influence of the Chinese shock on production and generalized falls of the GDP. Therefore, shocks in the Chinese financial market have the potential to provoke an international recession.

The Chinese shock affected other regions through another channel as well: the commodity price. Figure 6 shows that the negative credit shock caused a strong depression effect on commodities. In the first year of the shock, the price fell by around 4%, ending the series at 5%. In general, emerging economies are dependent on primary exports, particularly commodities. Thus, the fall in Figure 6 demonstrates an additional channel through which the Chinese shock can impact economies. Although the GVAR does not provide shock identification, when we focus it on analyzing the transmission of shocks, we can extrapolate the results and reach the conclusion that the credit shock triggers deep capital outflows. This flight to quality, in turn, impacts domestic production. We can also assert that the fall of commodities prices also reinforces the depression effect from the Chinese credit market. Furthermore, the credit shock exemplifies a known characteristic of China portrayed in works like Eickmeier and Kuhnlenz [12] the significant influence of this country on world inflation, a role which is confirmed by the estimates in Figure 6.

Figure 6: GIRF of China Negative Credit Shock and Commodity Price Response.

While Eickmeier and Kuhnlenz [18] analyzed the impact of real shocks from China on world inflation, here we applied a financial shock, which had a remarkable influence on the commodity price. Later in this section, we test this result by changing the proxy of commodity price, and the conclusion holds. The following subsection will test China’s financial influence over decades, a common exercise applied in articles about China using the GVAR. Until now, we have used fixed bilateral trade during 2014-2016 in the weighing matrix. We changed the values to perform this test, and we will inspect how the results behave.

Credit Shock with Alternative Weights

When investigating Chinese shocks, Cesa-Bianchi et al. [24] used time-varying weights in their weighing matrix. Varying the weights changes how we build foreign variables. By implementing time-varying weights, the process to calculate these variables considers the bilateral trade over the periods. In other words, the values of foreign variables in 1985 would use the average bilateral trade from 1985-1987, and in 1986 the values would be 1986-1988, and we would apply the same strategy until the last period - 2016 in our model. The explanation for this method is that the Chinese pattern of trade with many regions changed significantly over time.

In Subsection 5.3 we incorporate this strategy to analyze another Chinese financial shock. Here we closely followed Cesa-Bianchi et al [24] but we made some important changes. We also assessed the Chinese shock in the periods 1985, 1995, and 2005. However, because we have a larger period, we extended the investigation to 2016. Furthermore, we considered the averages of these periods instead of considering only the specific year. Hence, we evaluated how the Chinese shock behaved in 1985-1987, 1995-1997, 2005-2007, and 2014-2016. We believe this proceeding smooths changes in bilateral trade over time. Finally, while Cesa-Bianchi et al. [24] employed time-varying weights, we opted for fixed weights. Again, we made our decision considering that utilizing alternative setups shows the robustness of results (we use time-varying weights later). The objective of this exercise is to observe if China’s influence increased over time, correlatively with its growing participation in world GDP and international trade. Figure 7 presents estimates of the GDP for the four periods. We hypothesize that Chinese financial influence enhanced pari passu with its integration into the world economy. As we have four lines in each graphic of Figure 7, we do not display bootstrap intervals.

The first part of Figure 7 confirms what we’ve previously discussed: the financial influence of China on the world economy has strengthened over time. This finding is clearer in Latin America and Eurozone cases, while in Asia the shock impact of 2005-2007 is similar to 2014-2016. We also want to underline the distinction between the periods of 1985-1987 and 1995-1997 and the more recent periods. The first two periods show a small Chinese influence on the world economy, whereas this picture changes remarkably in the new millennium. In Japan, the U.K., and the U.S., we can also see the growing power of China, although it is not as apparent as the results portrayed in the upper part of Figure 7. We should keep in mind that, as displayed in Figure 2, the estimates for these regions are significant only in the initial quarters; after that, they fail to have significant values, which helps understand the positive responses of the GDP in later periods. Figure 8 reinforces the previous conclusions. The negative credit shock depressed commodity prices, and the force of this process gained traction over the decades, with a noticeable division between old and new millennium. Comparing Figures 7 and 8, we can assert that although Chinese financial influence increased in more recent periods, this gap has stopped showing relevant differences if we look at the periods 2005-07 and 2014-16.

Figure 7: GIRF of China Negative Credit Shock and GDP Responses with Alternative Weights.

Figure 8: GIRF of China Negative Credit Shock and Commodity Price Responses with Alternative Weights.

We finish this subsection with the conclusion that Chinese financial influence has increased over the decades. Cesa-Bianchi et al. (2012) stated that China augmented its influence in the real sector, mainly regarding GDP shocks. Now we contribute evidence that this is also true in the financial markets. The following part of this article complements this analysis by investigating another financial shock and implementing time-varying weights. 

Exchange Rate Shock

In this subsection, we made a few changes to the model setup used in past subsections. First is the incorporation of the stock exchange variable as a domestic variable in the place of the commodity price.  The second is our treatment of the weighting matrix: here, we employed time-varying weights to build the foreign variables. To solve the model, we used four periods: 1985, 1995, 2005, and 2014-16. Finally, we investigated a Chinese negative exchange rate shock. As the exchange rate variable is based on the given country’s domestic currency to the U.S. dollar, this shock represents the valuation of the Chinese currency. Figures 9, 10, 11, 12, and 13 display the results.

Figure 9 tells that Chinese influence grew in Latin America, the Eurozone, and the U.K. over time. This picture is evident in Latin America, where the exchange rate increased by 1% in 2014-16, which represents a domestic valuation. In Asia and Japan, the exchange rate shock lost effect as time passed. Chinese spillover is noticeable in Figure 10; the credit response becomes increasingly stronger over time in Latin America, the Eurozone, and Japan; in Latin countries the credit had an accumulated expansion of around 0.5%. The shock effects on the U.K. and the U.S. presented modifications according to the pervasiveness of China in the global economy. While shocks dated in 1985 harmed the U.K., this pattern changes when we consider shocks in the new millennium, which caused positive credit responses. The U.S. shows an inverse behavior: 1985 shocks were favorable and became prejudicial to its economy. Asia remained indifferent to Chinese exchange rate shocks.  The Chinese shock did not cause changes in the domestic monetary policies (Figure 11), a conclusion similar to that of Subsection 5.1. Latin America was the only region in which the presence of China was worth mentioning once the interest rate increased by 1%. The rest of the economies had values too small to be significant, which suggests these movements are irrelevant. Figure 12 substantiates that the stock market is the main transmission channel of this shock. The domestic markets in all regions reacted positively to the exchange rate shock, varying from 1% to 2%, which shows how sensitive this segment is to international shocks. Furthermore, the stock market response increased according to Chinese presence in the world economy, which we can observe by the lines depicting the weight of bilateral trade.

Figure 9: GIRF of China Negative Exchange Rate Shock and Exchange Rate Responses.

Figure 10: GIRF of China Negative Exchange Rate Shock and Credit Responses.

Figure 11: GIRF of China Negative Exchange Rate Shock and Interest Rate Responses.

Figure 12: GIRF of China Negative Exchange Rate Shock and Stock Exchange Responses.

Figure 13: GIRF of China Negative Exchange Rate Shock and GDP Responses.

he last figure of this subsection (Figure 13) strengthens our conclusion that China's financial system can cause international volatility in the real sector, and that this shock has become more prominent in the new millennium. We see this conclusion in all advanced economies, whereas the emerging economies failed to present this feature. The GDP of Latin America and Asia did not display a pattern of changing behavior over the decades. As the Chinese negative exchange rate shock caused booms in the wealthy regions, our first transmission channel candidate was the stock market, which increased in value following the shock. Additionally, in the Eurozone, Japan, and the U.K., the private credit segment also reinforced the exchange rate shock’s positive effect on domestic production. In Latin America, the shock provoked the expansion of private credit and the stock market, but these favorable forces can have been nulled by the domestic currency valuation with depressing effects on the export sector. The results were inconclusive in Asia because the estimates of this region did not allow us to build a reasoning about the shock transmission. When we compare the exchange rate shock with the credit shock, we realize these shocks spread via different channels. The stock market stood out in the first case, while the exchange rate had a role in the last scenario. As a common ground, both financial shocks affected the world economy, causing significant macroeconomic volatility and impacting the domestic production of emerging and advanced economies. 

Uncertainty and Commodity Prices

We tested the Chinese financial influence using two uncertainty measures: financial uncertainty, an index gathering a multitude of financial series, and macroeconomic uncertainty. We collected both indexes from Ludvigson et al. [14]. These authors suggest that these uncertainty measures are equivalent to those used in uncertainty studies, such as the VIX. As most of the series that encompass these indexes are related to the U.S. - and considering the U.S.’s role in the world economy - we treat these variables as global variables, putting them as domestic variables only in the U.S. model. We also tested the impact of China on other commodities prices using the base metals price index and the oil price, both extracted from Mohaddes and Raissi [25]. We denoted both as global variables in the GVAR. We incorporated the first variable as a domestic variable in the Chinese model, while in the U.S. model we followed Dees et al. [14] and used the oil price Figure 14 shows the same shock we investigated in Section 5.1, a Chinese negative credit shock. The results concerning uncertainty were unexpected; we thought that because this shock caused a generalized fall in GDP in all regions, the uncertainty level in the U.S. would increase, but the estimates reject this prognostic. The financial uncertainty was nonsignificant in all quarters, although it decreased after the shock. On the other hand, macroeconomic uncertainty became significant from the fifth period onwards. We can better understand these results by looking at the U.S. responses to the shock. Due to space we do not display GIRFs of other regions, but they suggest the following about the uncertainty drop: the domestic currencies devalued in most regions after the shock, meaning the U.S. dollar had a valuation, which could have contributed to reducing the uncertainty in this economy.

Figure 14: GIRF of China Negative Credit Shock and Uncertainty and Commodities Responses.

While the uncertainty results may not have been as conclusive as we hoped, we can draw conclusions when analyzing commodities prices results. Both indexes suffered considerable falls: around 3% in oil and an impressive 10% in metals. These estimates confirm the relevant role that China has in the commodities market, demonstrating its ability to change world inflation through the financial market.

Variance Decomposition of Chinese Shocks

Our final econometric exercise is the variance decomposition analysis (Generalized forecast error variance decomposition, GFEVD). Table 1 shows how financial shocks influence specific variables. Column Model 1 depicts the model of Section 5.1 which verifies how the credit shock affected the domestic credit of aggregates. We proceed the same way in column Model 2, but using the model from Section 5.3 In this case, we want to verify how the exchange rate shock from China affected the domestic exchange rates. We built two groups, the first consisting of emerging economies (Asia and Latin America), and the second of advanced economies (the Eurozone, Japan, the U.K., and the U.S.), which are titled EME and AD, respectively. We separated the analysis to observe how the Chinese shocks account for domestic variables over 1985 and 2014-16.

Table 1: GFEVD of China.

Beginning with the emerging economies (EMEs), the Chinese credit shock increased its explanatory power in the 2014-16 period, more than doubling its influence. Hence, we can assert that China’s credit shock has been increasingly influencing the domestic credit of EMEs. The same is not true for the advanced economies, in which the Chinese credit has lost ground, although in the last period (20), it surpassed the influence of the base year of comparison (1985). China has a growing influence on ADs when we look at the exchange rate. Although the values are lower than those of EMEs, we still detected Chinese influence on the domestic exchange rates of advanced economies. The explanatory power tripled in the last period. Finally, the Chinese exchange rate shock had little to no impact on the EME exchange.

This article explored the Chinese financial influence on emerging and advanced economies. The estimates suggest private credit and exchange rate shocks from China can cause persistent volatility in the international economy, affecting domestic financial markets as well as the real sector. This conclusion raises questions about the consequences of events such as the looming bankruptcy of Evergrande. If our results point in the right direction, this potential shock would affect many economies, causing swings in the financial system, GDP, and prices. We follow the spirit of Wall and Eyden's [15], who point out how South Africa’s vulnerability to external forces has shifted from the U.S. to China. While our article did not focus on comparing the U.S. and Chinese influences as that of the aforementioned authors, we still assert that policymakers should be watchful of shocks coming from China due to its growing influence, which the econometric estimates show to be increasing over the years.

Concerning future research, we recognize that future researchers may be able to find a stronger proxy than bilateral trade as a variable connecting China to the other regions. We consider this an area in which future studies could focus and improve. Moreover, we hope other authors analyze the points investigated here and compare estimates to refine the conclusions.

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