Impact of International Monetary Policy Adjustments on the Liquidity of China’s Domestic Financial Market: A Case Study of the US Dollar
ZhenHang Chen1,a*,JiaYu Sun2,b
1Adam Smith Business School, University of Glasgow, Glasgow, Scotland, United Kingdom
2 Adam Smith Business School, University of Glasgow, Glasgow, Scotland, United Kingdom
aEmail: 15355396181@163.com
bEmail: 18822728720@163.com
*Corresponding Author
Abstract: Frequent adjustments in monetary policies of major economies, especially the Federal Reserve’s interest rate hikes and balance sheet cuts, have had a profound impact on global financial markets. To enhance the stability of China’s domestic financial market and improve the risk resistance of China’s domestic stock market, this study adopts three open economy macroeconomic models and constructs three vector auto regression models with specific variables. The purpose is to analyze the channels through which changes in US monetary policy are transmitted to the Chinese stock market. The results demonstrated that the transmission channels of US monetary policy on the liquidity of China’s stock market mainly included capital flow channels, interest rate channels, and money supply channels. The changes in the federal funds rate during the international financial crisis were opposite to the changes in China’s interbank lending rate. During the US dollar interest rate hike policy, the contribution of the federal funds rate remained relatively stable at around 10%. The lending rate of Bank of China would rise to around 8% in the long term. The transmission mechanism of US monetary policy on liquidity in the Chinese stock market exhibits heterogeneity. The adjustment of international monetary policy will have a linkage with China’s domestic financial market, and analyzing the adjustment of international monetary policy can enhance the stability of China’s domestic financial market.
Keywords: US dollar; Monetary policy; Financial markets; Stock
Introduction
With the deepening of global economic integration, the adjustment of monetary policies in various countries has had a profound impact on international capital flows and financial market dynamics. Especially in the United States, as the world’s largest economy, changes in its monetary policy directly affect the stability of global financial markets [1-2]. In recent years, the Federal Reserve has frequently adjusted its monetary policy, especially the implementation of Interest Rate Hikes (IRH) and quantitative easing policies, which not only affects the domestic economy of the US but also triggers capital flows and market turbulence worldwide [3-4]. Therefore, it is crucial to clarify the Transmission Mechanism (TM) of International Monetary Policy (IMP) on China’s Domestic Financial Market (CDFM), especially the stock market. The current research is facing the problem of the complexity of IMP, which leads to diversified impact paths, and there is still a lack of clear theoretical framework on what mechanisms affect the Chinese market. This study aims to explore IMP in depth by constructing an appropriate economic theoretical framework and combining empirical analysis. The focus of the research will be to identify and analyze the TM of IMP to CDFM, and clarify the interactive relationship between different financial variables. This study will use three Open Economy Macroeconomics (OEME) models and combine them with Vector Auto Regressive (VAR) models for empirical analysis, analyzing the factors that affect CDFM liquidity from multiple perspectives. The innovation lies in systematically integrating existing theoretical frameworks and empirical data to comprehensively assess the effect of IMP changes on CDFM liquidity. By introducing dynamic Impulse Response Analysis (IRA) and variance decomposition, a deeper understanding of the interrelationships between major financial variables has been provided.
- Related works
The liquidity of financial markets is influenced by various factors. Many scholars have analyzed the liquidity issues in financial markets. Marozova G et al. investigated the effects of bond and stock market liquidity on foreign portfolio investments. They used three different liquidity indicators for panel data analysis. Foreign investors entering the Domestic Financial Market (DFM) of the host country would increase the risk sharing between local and foreign investors [5]. The advantage of this method lies in the use of multiple liquidity indicators, which enhances the robustness and credibility of the research. The disadvantage lies in the lack of in-depth analysis of the specific mechanisms by which changes in liquidity affect market behavior, and the failure to consider other factors that may affect investor behavior, such as macroeconomic conditions or policy changes. Regarding the impact of stock market liquidity, Alaoui Mdaghri A et al. conducted regression analysis on stock market data using panel data. In the regression results of the overall sample, the liquidity related to depth measurement was positively correlated with the increase of confirmed cases, deaths, and strictness index [6]. The limitation of this method is that the time span of the sample may be limited to the epidemic period, making it difficult to generalize the results to liquidity characteristics in other economic environments. Saliya CA proposed an autoregressive distribution lag testing framework to evaluate the impact of structural determinants on Fiji’s financial development, and used descriptive and narrative analysis to explain the status of the stock exchange. The proposed method could effectively analyze the influencing factors of financial development [7]. This research method can effectively capture short-term and long-term relationships, and combine narrative analysis to explain the results. It constrains that the study mainly focuses on Fiji and may lack universality, without considering changes in external economic connections in the region. Wang X et al. developed a competitive strategy measure for stock liquidity at the enterprise level using Machine Learning (ML)-based natural language processing methods. It was found that companies that emphasize differentiation strategies exhibit higher stock liquidity than those that adopt cost leadership strategies [8]. This method utilizes advanced technology to analyze complex market behavior, opening up new directions for traditional financial analysis methods. However, the interpretability of ML models is relatively weak, which may cause a decrease in the transparency of the results. Xu Y et al. used the Copula generalized autoregressive conditional heteroskedasticity model to study the liquidity dependence between futures markets in response to the adjustment of short-term liquidity problem in the futures market of China. This method could effectively predict the liquidity of the futures market [9]. This method can effectively capture complex liquidity dynamics and conduct in-depth analysis of liquidity relationships between different futures markets. However, the construction and parameter estimation of the model are relatively complex, and the sensitivity of the assumptions may affect the stability of the prediction.
Based on the above relevant research, although various studies have provided useful perspectives in analyzing liquidity and international capital flows, most studies have limitations in sample selection, method applicability, and result generalizability. In addition, many studies have failed to systematically consider the interaction of liquidity between different markets, especially in the face of complex economic situations. Therefore, this study proposes to explore the impact of IMP adjustment on CDFM liquidity. Through the application of multi-channel TMs and VAR models, it is possible to systematically analyze the effects of various factors on liquidity and make up for the shortcomings in existing research.
- Empirical analysis of international monetary policy adjustment based on VAR model on domestic stock market liquidity
2.1 Impact analysis tools for different economies
It is crucial to use an effective economic theoretical framework when analyzing the effect of IMP adjustments on domestic stock market liquidity. The current analysis of the impact of major economies includes: the Mundell-Fleming Theoretical Model (M-FTM) used to analyze the effects of monetary and fiscal policies on domestic and foreign economies under fixed and floating exchange rate systems; Hamada model for analyzing the effects of economic policies under different market structures and restricted capital flows; The new OEME model emphasizing micro foundations and dynamic optimization, focusing on the interaction between monetary policy, fiscal policy, and exchange rate policy in an open economy [10-13]. M-FTM mainly focuses on the correlation between exchange rates, Gross Domestic Product (GDP), Interest Rates (IRs), and capital flows. Its core includes the investment savings (IS) curve, liquidity preference money supply (LM) curve, and balance of payments (BP) curve. The expression of the IS curve is shown in formula (1).
（1）
In formula (1), is the total output GDP. and are the consumption and investment functions. means taxation. refers to the IR. corresponds to government expenditure. is net export. means the actual exchange rate. The IS curve describes the relationship between output and total demand at a given IR level. The expression of the LM curve is shown in formula (2).
（2）
In formula (2), is the nominal money supply. is the price level. is the liquidity preference function. The LM curve represents the equilibrium conditions of the money market at a certain level of money supply and price. The calculation of the BP curve is shown in formula (3).
（3）
In formula (3), represents capital flow. The BP curve describes the relationship between net exports and capital flows under the condition of balance of payments. Figure 1 shows M-FTM.
Figure 1 M-FTM schematic diagram
The IS curve in Figure 1 reflects the impact of exchange rate fluctuations, capital flow changes, and liquidity. Under the expansionary fiscal policy, the IS curve moved to the upper-right and intersects with the LM curve at the upper intersection point, without increasing national income. In the case of expanding monetary policy, the LM curve shifts to the right, the local currency depreciates, the exchange rate decreases, stimulating an increase in net exports and a rise in national income Y, indicating the effectiveness of monetary policy. The mathematical formula of the Hamada model is shown in formula (4).
（4）
In formula (4), is the exchange rate of the local currency to the foreign currency. is the IR of an external country. Formula (4) can quantitatively evaluate how IMP adjustments affect capital flows, investment, and net exports, thereby affecting CDFM liquidity [14]. Figure 2 shows the Hamada mathematical model.
Figure 2 Schematic diagram of Hamada model
Figure 2 can reflect the interactive relationship between the international market and the domestic market. This study takes the United States and China as examples. When the United States and other countries implement monetary tightening policies, external IRs rise. This will attract capital outflows from China, leading to a decrease in domestic capital flows and thus affecting China’s liquidity [15]. The expression of the new OEME model is shown in formula (5).
（5）
In formula (5), is the marginal utility at time . is the consumption of . is the time preference factor, with a value range of (0,1). is the expected operation of future random variables. is the potential output. means the nominal IR. is the inflation rate at the price level. denotes the target inflation. is the policy response coefficient.
2.2 Transmission mechanism of international monetary policy adjustment on DFM liquidity
After exploring the impact mechanism of IMP adjustment through different economic theoretical frameworks, it is required to carry out in-depth research on how these policy changes specifically affect the liquidity of the DFM. Therefore, the TM of IMP adjustment on DFM liquidity has become a research and analysis focus, mainly including three channels: capital flow, IR, and money supply. The capital flow channel emphasizes the flow of international funds between different markets, reflecting the impact of central bank policies on investor behavior [16-17]. The specific impact path is shown in formula (6).
（6）
In formula (6), is the liquidity of the Chinese Stock Market (CSM). The IR channel refers to the adjustment of IMP. Taking the US dollar as an example, when the US monetary policy changes, it affects the level of IRs and thus affects the process of domestic investment and consumption decisions. When the US central bank implements monetary tightening policies, global IRs often rise, which will directly affect China’s IR levels. The calculation of the impact path is shown in formula (7).
（7）
Formula (7) reflects that as market IRs rise, the cost of borrowing also increases. Companies may delay or reduce investment decisions in order to reduce debt, which can cause a decrease in overall market activity. The money supply channel refers to the process of influencing market liquidity by changing the money supply, as shown in formula (8).
（8）
The path of formula (8) indicates that if the United States implements expansionary policies, global liquidity will be abundant and help reduce financing costs, stimulate corporate investment and consumption, which usually stimulates liquidity in the Chinese market. Given the above analysis, the TM of IMP adjustment on DFM liquidity is shown in Figure 3.
Figure 3 TM of IMP adjustment on DFM liquidity
Figure 3 shows the monetary policy changes in the US as an example, which affect the liquidity of CDFM through a series of TMs. The Federal Reserve’s adjustment of the Federal Funds Rate (FFR) will directly affect market IRs, thereby increasing borrowing costs for businesses through the IR channel and suppressing investment and consumption. At the same time, changes in IRs will also guide capital flows, attract capital to flow to the United States, lead to capital outflows from the Chinese market, and further reduce domestic capital liquidity. In addition, changes in the money supply can also affect market liquidity. If the US implements austerity policies, it may lead to a decrease in global liquidity and suppress market activity. Overall, these three channels work together and profoundly affect the funding supply, investment confidence, and financing environment of the CSM.
2.3 Construction of empirical analysis model
After a thorough analysis of the TM of IMP adjustment on DFM liquidity, to further quantify the role of these mechanisms and confirm the specific impact of different channels on liquidity in the CSM, a corresponding VAR model will be constructed [18]. Through these models, this study will be able to systematically analyze the dynamic relationship between changes in the US FFR, Short-Term Capital Flows (STCF), domestic IRs, money supply, and Stock Market Turnover Rates (SMTR), thereby revealing how these factors collectively affect the liquidity of the CSM. The VAR is a multivariate time series model utilized to capture the dynamic relationships between multiple time series variables. The basic idea is that each dependent variable is not only influenced by its past values, but also by that of other explanatory variables [19-20]. The basic operation of the VAR is shown in formula (9).
（9）
In formula (9), is a constant vector. is the observed value of endogenous variables, such as STCFs, IRs, money supply, etc. in China. is a coefficient matrix used to express the impact on . is the error term of the model, which assumes independent and equally distributed random variables with a mean of 0 and a constant covariance matrix. The specific matrix construction of the VAR model is shown in Figure 4.
Figure 4 Specific matrix construction of VAR model
In Figure 4, the specific matrix construction process of the VAR model begins with selecting multiple related variables. Next, the lagged variable vector is constructed by determining the lag period , and a parameter matrix is constructed for each lag period to represent the dynamic relationship between different variables. The error term matrix captures random disturbances. Finally, the standardized expression of the VAR model integrates these matrices to form a set of multivariate equations that can quantify and analyze the mutual influence between variables. The specific VAR model is shown in formula (10).
（10）
In formula (10), denotes the US federal funds rate. is the STCF in China. is the IR in the Chinese market. means the money supply in China. By estimating the model, parameter estimation is usually carried out using the least squares method or maximum likelihood method to obtain the specific values of each parameter matrix. Through these values, further IR) and variance decomposition can be performed. IRA is used to evaluate the changes over time in the impact of a unit shock on a variable in a VAR model, such as adding one unit, on other variables. IRA first selects a variable, and then uses the structure of the VAR model to calculate the response of other variables after the impact based on the equation description of the model. Finally, the impulse response results are visualized to show the path of time series fluctuations, to visually analyze the response trends of different variables. Through IRA, it is possible to clearly assess the short-term and long-term impacts of IMP, such as the Federal Reserve’s IRH, on variables including IRs and capital flows in the Chinese market. This is vital in understanding capital flows, changes in IRs, and the effectiveness of economic policies.
Variance decomposition is used to evaluate how much of the volatility of each variable in a model at a certain point in time is caused by the impact of other variables. That is, it reveals which factors contribute to the fluctuations of each variable, thereby indicating the relative importance between variables. In the VAR model, variance decomposition first calculates the prediction error variance of each variable within a specific time range, and gradually decomposes the proportion of influence from other variables in the variance of each variable by accumulating the impact of different variables. Finally, the results of variance decomposition are presented in graphical form, providing proportional information on the sources of each variable. Variance decomposition can help investigate which factors have the greatest impact on CDFM liquidity volatility.
- Verification of IMP adjustments on domestic stock market liquidity
3.1 Empirical analysis of the impact of unconventional monetary policy on stock liquidity
The empirical analysis data came from currency related data between 2008 and 2023. During this period, the Federal Reserve made multiple Unconventional Monetary Policy (UMP) adjustments, including large-scale quantitative easing and multiple changes in IRs, which can better reflect the impact of these policies on the Chinese market. The data came from the databases of the PBOC, the Federal Reserve and the International Monetary Fund (IMF), including FFR, quantitative easing related indicators (such as balance sheet size), IR information of the Chinese market, stock market turnover, capital flows, investment and consumption data, etc. The stationarity test results of the model under unconventional monetary policies are shown in Figure 5.
Figure 5 Stability test of VAR model under UMP
In Figure 5, after first-order differencing, the optimal lag order for the variable is 0. This indicates that there is no significant delay relationship between the current and past values, and the VAR model can be directly constructed using the original data. The unit roots are all located within the unit circle, verifying the stability of the model. In an UMP environment, the IRA comparison of the VAR model are displayed in Figure 6.
Figure 6 Pulse response of FFR with different variables
Figure 6 (a) shows the impact between FFR and SMTR. When there is an upward shock of two standard deviations in FFR, the turnover rate of the stock market will rapidly decrease. Over time, its weakening rate gradually slows down and almost disappears after about 40 trading cycles. Figure 6 (b) shows the influence between FFR and IR. When FFR experiences an upward shock of two standard deviation amplitudes, IR will rapidly decrease and reach its lowest point within the fourth period. After the fourth period, this influence begins to weaken and stabilize by the 50th period. Figure 6 (c) shows the influence between FFR and M. When there is an upward shock of two standard deviation amplitudes in FFR, M will rapidly decrease. In the second period, this impact changes from positive to negative, indicating that external IR changes have had adverse effects on domestic monetary policy. Until the 50th issue, FFR continued to have a negative impact on M. Figure 6 (d) shows the influence between FFR and SCF. When there is an upward shock of two standard deviation amplitudes in FFR, SCF will rapidly decrease. In the first period, this impact changes from negative to positive and reaches its peak in the second period, then gradually slows down and almost disappears after the 25th cycle. This study further analyzes the impact of variables on SMTR in UMP environments, as shown in Figure 7.
Figure 7 Impulse response of variables and SMTR
Figure 7 (a) shows the impact between IR and SMTR. When there is an upward shock of two standard deviations in IR, the SMTR will rapidly decrease and reach its lowest point in the second period. Although the turnover rate gradually rebounds, the growth rate slows down, and around the 40th period, this negative impact almost disappears. Figure 7 (b) shows the impact between M and SMTR. After experiencing a positive impact of two standard deviation amplitudes, although the SMTR slowly increases in the initial stage, this positive effect immediately weakens. Figure 7 (c) shows the impact between SCF and SMTR. When SCF is positively interfered by two standard deviation amplitudes, the SMTR immediately drops significantly and reaches its peak in the 2nd period. Subsequently, the indicator quickly recovers and begins to approach 0 around the 5th period. The variance decomposition diagram of the VAR are exhibited in Figure 8.
Figure 8 Variable variance decomposition of VAR model under UMP
Figures 8 (a), (b), and (c) show the variance decomposition results of all variables, SHS, as well as IR, FFR, M, and SCF. From a long-term perspective, the most essential factor affecting the turnover rate of the stock market is the inter-bank lending rate. Although FFR also has a certain impact, its influence decreases over time. By the 7th period, the contribution of FFR remained relatively stable at around 10%.
3.2 Empirical analysis of the impact of US dollar IRHs on the liquidity of Chinese stocks
In the environment of US dollar IRHs, global capital flows and investment costs will be significantly affected, which may lead to capital flowing back from emerging markets to the United States, thereby increasing pressure on DFMs. Therefore, this study further analyzes the impact of the US dollar IRH on the liquidity of the CSM. The stability test results of the model under the US dollar IRH policy are shown in Figure 9.
Figure 9 Stability test under the US dollar IRH policy
In Figure 9, under the US dollar IRH policy, the variables become stable after first-order difference processing, and the unit roots are all located within the unit circle. This indicates that the model is stable and convenient for subsequent impulse response testing and variance decomposition testing. Figure 10 shows the subsequent impulse response test of variables under the US dollar IRH policy.
Figure 10 Pulse response of FFR with different variables
Figure 10 (a) shows the impact between FFR and SMTR. FFR has a short-term impact on SMTR. When FFR experiences a positive impact of two standard deviations, the turnover rate immediately increases and reaches its peak in the second phase. Subsequently, the turnover rate rapidly declines, showing a negative trend in the third period, and then reverses to positive in the fourth period. The fluctuation process repeats repeatedly and tends to stabilize after the 7th cycle. Figures 10 (b)～(d) show the effects of FFR on IR, FFR on M, and FFR on SCF. When there is a positive change in the amplitude of two standard deviations, IR, M, and SCF also exhibit a positive and negative alternation phenomenon, and stabilize in the 7th period. In the context of the US dollar IRH policy, the effect of variables on SMTR is shown in Figure 11.
Figure 11 Pulse response of variables and SMTR
Figure 11 (a) shows the impact between IR and SMTR. Under the positive impact of two standard deviations, the SMTR exhibits significant fluctuations in a short period. For a long run, the turnover rate curve of the stock market remains relatively stable. Figure 11 (b) shows the effect of M on SMTR, where M shows consistency with IR changes and tends to zero around the 7th period. Figure 11 (c) shows the impact between SCF and SMTR. The trend of SCF changes is almost consistent with that of IR, both reaching their peak in the second period and tending towards zero in the eighth period. Figure 12 is the variance decomposition diagram of the VAR model’s variables.
Figure 12 Variance decomposition of VAR model variables under the US dollar IRH policy
Figures 12 (a) and (b) show the decomposition data of all variables and SHS variables. Figure 12 (c) shows the decomposition results of IR, FFR, M, and SCF variables. Overall, the crucial factor affecting the liquidity of the domestic stock market is the domestic interbank lending rate, followed by FFR, but the latter has a smaller impact with a contribution of less than 5%. The remaining variables did not have an obvious effect on stock market liquidity.
- Conclusion
This study analyzed the impact of IMP adjustment, especially the US dollar IRH, on CDFM liquidity, using M-FTM, Hamada, and new OEME models, and constructed a VAR model for empirical analysis. It was found that the Federal Reserve’s IRH policy significantly suppressed the liquidity of the CSM, specifically reflected in the rise of FFR leading to a rapid decrease of about 30% in turnover rate. Under unconventional monetary policies, the contribution of inter-bank lending rates to turnover rates was the highest, consistently around 40%. The experiment showed that the model had good stability and effectiveness in UMP environments, and could capture the complex dynamic relationships between variables such as FFR, capital flows, market IRs, and money supply. In addition, IRA showed that an increase in FFR had a significant short-term negative impact on liquidity, while variance decomposition indicated that inter-bank lending rates contributed the most significantly to market turnover rates. Although this study provides valuable empirical support, there is still room for improvement due to limitations in data scope, linear assumptions in the model, and the lack of consideration for external variables. Future research can expand the scope of data by using nonlinear models for cross-border comparisons and high-frequency data analysis, aiming to obtain more comprehensive insights and response strategies.
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