China's Footprint on Global Financial Markets and Commodities

 The sources detail a rigorous, multi-stage empirical methodology aimed at accurately quantifying China's specific footprint in global financial markets while controlling for major confounding factors.

The central novelty of the methodology is the simultaneous identification of China-specific structural shocks alongside shocks emanating from the United States and shifts in global risk sentiment. This joint identification is crucial because global financial markets are driven by a multitude of interacting, high-frequency shocks, and failing to purge China spillover estimates of common factors could result in substantial bias or overestimation of China’s influence.

Stage 1: Identifying Structural Shocks via BVAR

The investigation employs a two-stage approach, beginning with the identification of shocks using a structural Bayesian VAR model (BVAR) on daily data.

Data and Model Specification:

• Data Frequency and Period: The model uses daily data spanning from January 2017 to March 2023.
• Endogenous Variables: The BVAR model includes six key financial variables, specified in first (log) differences: yields on 1-year (short-term) and 10-year (long-term) Chinese government bonds, China and US equity price indices (using the Shanghai ETF), the spread between Chinese and US 10-year government bond yields, and the bilateral exchange rate of the Renminbi against the US dollar.
• Timing: To ensure that the reaction of market participants to Chinese and US shocks can be reflected on the same day, the methodology relies on round-the-clock trading data, such as the Shanghai ETF and the offshore quotation for the Renminbi against the dollar.
• Estimation: The model is estimated using Bayesian techniques for identification of structural shocks, implemented through the BEAR toolbox.

Shock Identification Scheme: The identification scheme relies on three broad building blocks using sign restrictions and narrative identification:

1. Separating Domestic Shocks: For both China and the US, "monetary shocks" are separated from "macro risk shocks" based on the assumed reaction of domestic bond yields and equities (e.g., accommodative monetary shocks lower yields and boost equities, while favorable macro risk shocks raise yields and boost equities).
2. Separating US/China Shocks: The methodology assumes that US shocks spill over to China's bond markets, consistent with the global financial cycle literature. It also assumes that domestic financial markets react most strongly to domestic structural shocks, imposed via the China-US yield spread.
3. Identifying Global Risk: A "global risk shock" is identified by exploiting the safe-haven role of the US dollar.

• Refinements: Magnitude and narrative restrictions are used to strengthen the identification. For instance, the Wuhan lockdown on January 23, 2020, was assumed to represent the largest adverse China macro risk shock contribution to the drop in Chinese equity prices on that day.

Model Validation: The model's ability to capture China-specific dynamics is validated by examining the correspondence between the identified China macro risk and monetary policy shocks and key external events (such as PBoC announcements, RRR cuts, the US-China trade war, and COVID-19 related lockdowns). The historical decomposition confirms that Chinese equity prices are largely determined by changes in perceptions of the Chinese economic outlook, captured by macro risk shocks.

Stage 2: Quantifying Spillovers via Local Projections

Once the structural shocks are identified, the research uses the Local Projections method (à la Jordà, 2005) to map out the spillovers to global financial and commodity markets. This approach imposes fewer restrictions and is considered more robust to misspecification than a standard VAR framework.

• Panel Setup: The analysis uses a panel setup to estimate the impact of each structural shock across a sample of 30 advanced and emerging market economies.
• Estimation and Controls: The panel regression includes country-fixed effects, lags of the dependent variable, and control variables such as the CBOE Volatility Index (VIX) and the US and Global Citigroup Economic Surprise Index.
• Inference: To account for potential serial correlation and cross-sectional dependency in the panel context, Driscoll and Kraay (1998) corrected standard errors are used.
• Quantifying Importance: The importance of each shock is assessed by computing the cumulative forecast error variance decomposition at a 20-day horizon, following the R-Squared estimator proposed by Gorodnichenko and Lee (2020).
• Commodity Markets: For oil and metal prices, a similar methodology is applied, but in a time-series context.

Robustness and Non-Linearity Checks

The methodology also includes advanced checks to ensure the reliability and comprehensive understanding of the spillover channels:

1. Filtering Validation: The sources specifically benchmark the findings against simpler BVAR models that do not separately identify US and global risk shocks. This exercise demonstrates that failing to properly filter for global factors could lead to a substantial overestimation of China's footprint in global markets.
2. Non-Linearity Analysis: The local projection framework is extended to investigate whether the transmission of China shocks is state-dependent. This involves partitioning the data based on two conditions:
   • Shock Magnitude: Examining spillovers when China macro risk shocks are particularly large (in the top or bottom deciles of the shock distribution).
   • Global Volatility: Examining spillovers during periods when the VIX is above or below its historical average.
3. Alternative Specifications: Robustness checks included testing an alternative BVAR identifying only a single China-specific shock (to ensure macro risk and monetary policy shocks are indeed separable) and running local projections country-by-country (to check for averaging effects in the panel setup).
4. Time Variation: Spillovers were also estimated over non-overlapping six-month periods (excluding 2020) to track whether China's influence was changing over the sample period.

This comprehensive methodology, integrating BVAR shock identification with Local Projections spillover analysis and targeted robustness checks, is designed to provide a "solid framework for policy makers to monitor the evolving importance of Chinese structural shocks for global financial and commodities markets".

The key findings of the sources concerning spillovers emphasize that while China's structural shocks do leave a significant footprint on global financial markets, their influence is highly conditional on the asset class and the current global volatility environment.

The research's central contribution is isolating the impact of China-specific shocks (macro risk and monetary policy) from the dominant drivers of global markets: shocks emanating from the United States and shifts in global risk sentiment. This careful filtering process is essential, as the results suggest that failing to properly account for global factors could lead to a substantial overestimation of China's footprint in global markets.

Overall Footprint and Relative Importance

The empirical evidence suggests that shocks emanating from China have an effect on global financial markets, but this impact is generally smaller than that of US or global risk shocks. US shocks and shifts in global risk sentiment are identified as the preeminent factors shaping global financial markets.

However, China’s influence is material and specific, particularly in certain asset classes:

• China shocks have a material impact on global equity markets.
• Shocks in China are associated with a much more modest effect on global bond markets.
• China shocks account for a significant proportion of variation in global commodity prices, sometimes on a par with those of the United States.

Spillovers to Global Financial Markets

The primary mechanism for financial spillovers is the China macro risk shock (changes in perceptions of the Chinese economic outlook), while Chinese monetary policy shocks have little measured impact.

1. Global Equity Markets:

   • Global equity prices respond significantly to Chinese macro risk shocks. This suggests that market re-assessment of China’s growth outlook reverberates to equity prices globally.
   • The impact of shocks stemming from the US or global risk shocks can be up to three times as large as the impact from Chinese macro risk shocks. Other sources suggest the impact of US or global risk shocks can be three to four times as large.
   • China-specific shocks account for only a small proportion of cross-country equity variation.
   • Global equity prices are found to not move when the People’s Bank of China (PBoC) unexpectedly changes its policy stance (China monetary policy shocks).

2. Global Bond Markets and Currencies:

   • Spillovers to global bond markets are generally more modest compared to equities.
   • Following unfavorable macro risk shocks in China, bond yields of other countries increase, and their exchange rates depreciate modestly in effective terms. This is likely because a dire macro outlook in China weighs on the growth outlook of other countries.
   • A smaller proportion of cross-country yield and exchange rate variation reflects shocks originating in China.
   • Global yields and exchange rates are much more strongly affected by US shocks (e.g., US monetary tightening lifts global yields) and global risk shocks (due to the safe-haven status of the US dollar).

Spillovers to Global Commodity Markets

China's footprint is significantly more important in global commodity markets, often rivaling or exceeding the US influence:

• China shocks play a significantly more important role in shaping developments in global commodity markets.
• For some commodities, especially metals, the impact of China macro risk shocks is larger than that of macro risk shocks originating in the US.
• This strong effect is consistent with China’s role as a major consumer, utilizing a significantly higher share of global non-energy commodities (like metals) than the US, while consuming a similar amount of energy goods.
• Adverse China macro risk shocks cause a significant decline in both oil and metal prices.
• China shocks account for a relatively large proportion of the variation in oil and metals prices.
• Commodity prices are identified as an important channel through which macro risk shocks in China are transmitted globally.

Non-Linear Transmission and Amplification

Crucially, the average impact findings mask significant non-linearities. Spillovers from China can be significantly amplified when they occur during periods of heightened global risk or when the shocks themselves are large:

• Heightened Global Volatility: When global volatility is elevated (e.g., when the VIX is above its historical average), the transmission of China shocks is magnified.
  • The response of global equities can be twice as large.
  • Spillovers to nominal effective exchange rates can be three times as large.
  • The impact on oil prices can be close to five times as strong.
• Large Shocks: Out-sized China macro risk shocks (those in the top or bottom deciles of the distribution) have larger spillover effects.
  • The response of global equity prices to large downside shocks in China is estimated to be about four times as large as the response to average shocks.

Ultimately, the sources conclude that if China "catches a cold," global equities and commodity prices "would sneeze as well," but policy makers should "only start worrying when symptoms of the flu appear" (referring to large or high-volatility shocks).

This dynamic relationship means that China's influence acts less as the daily anchor of global financial volatility (a role still held by the US and global risk factors) and more as an amplifier, dramatically boosting risk transmission during already fragile or turbulent periods.

The sources emphasize that the transmission of shocks originating in China to global financial markets is significantly characterized by non-linearities, meaning the magnitude of spillovers is highly dependent on the state of global volatility or the size of the Chinese shock itself. While the average impact highly dependent on the state of global volatility or the size of the Chinese shock itself. While the average impact of China's shocks is generally modest compared to US or global risk shocks, these non-linearities show that China's footprint is strongly reinforced (amplified) under specific conditions.

The analysis investigates non-linearities by focusing on China macro risk shocks—changes in perceptions of the Chinese economic outlook—which are the primary source of global financial market spillovers from China.

Amplification by Shock Magnitude

The sources find evidence that out-sized China macro risk shocks—those that are particularly large in size—have exaggerated or "out-sized" spillover effects on global markets.

• Global Equities: The most notable non-linearity is observed in global equity markets. The response of global equities to a large downside shock in China (falling into the bottom decile of the shock distribution) is estimated to be about four times as large as the response to an average shock.
• Commodity Markets: The reaction of commodity markets is also notably larger for out-sized China shocks.
  • Oil prices react more strongly to large negative China macro risk shocks.
  • Metals prices are found to be more reactive to larger shocks, specifically negative ones. This greater sensitivity in commodity markets aligns with China's crucial role in steering global growth and its heavy reliance on metals to fuel that growth.

In other financial markets (bond yields and exchange rates), the sources note that there is less evidence of significant non-linearities tied specifically to shock magnitude.

Amplification by Global Volatility

Spillovers are also significantly amplified when they occur during periods of heightened global volatility. The non-linearity analysis partitioned the data based on whether the CBOE Volatility Index (VIX) was above or below its historical average.

When global volatility is elevated (VIX above average), the transmission of China shocks to risky assets and currencies is magnified:

• Global Equities: The response of global equities can be twice as large.
• Currencies: Spillovers to nominal effective exchange rates can be three times as large.
• Commodity Markets: Commodity markets are particularly susceptible to amplification in high-volatility environments. The impact of China macro risk shocks on both oil and metals prices is significantly larger. The impact on oil prices responds close to five times as strong.

Contextualizing Non-Linearity

These findings contribute to the existing literature that examines the interdependence of financial markets during periods of stress.

• The sources highlight that sharp variations in markets might affect shock transmission because larger shocks may prompt investors to sell risky assets more aggressively to rebalance portfolios, thereby increasing spillovers.
• Similarly, periods of high volatility (fragile risk sentiment) may inherently generate larger price adjustments when shocks occur.

The ultimate conclusion regarding China's footprint is expressed metaphorically: while China's shocks cause global equities and commodity prices to "sneeze" normally, policymakers should "only start worrying when symptoms of the flu appear". This stresses that China's most disruptive global influence is exerted not through its average daily shocks, but through its ability to powerfully amplify risk transmission during already unstable market conditions or when experiencing severe domestic downturns (large negative macro risk shocks).

The sources provide substantial context regarding the evolution of China's role in global finance, emphasizing a fundamental shift in its policy paradigm and financial market structure, which necessitates a systematic re-evaluation of its global footprint.

Historical Context and Rationale for Limited Coverage

Historically, spillovers from China to global markets received "much less coverage" compared to the dominant role played by the United States. This historical neglect was "understandable" prior to the global financial crisis (before 2008) due to several structural features of the Chinese economy:

• Underdeveloped financial markets: China's markets were relatively immature.
• Closed capital account regime: The capital account was largely closed.
• Tightly managed exchange rate: The Renminbi (RMB) exchange rate was tightly managed.

These factors suggested there was "limited scope for meaningful financial market spillovers from China".

Evolution of China's Policy Paradigm and Market Structure

Since the global financial crisis, the Chinese economy has "evolved rapidly", leading to structural changes that increased the potential for global financial spillovers:

1. Shift to a Market-Based System

The overall policy paradigm has shifted from a "tightly controlled system" toward one where market forces are intended to play a "decisive role" in resource allocation.

• Monetary Policy: Monetary policy has moved away from heavy reliance on quantitative targets toward using market-interest rates to steer the economy. The PBoC signals policy changes principally through short-term interest rates, such as the 7-day repo rate. Governor Yi Gang noted that a "complete system of market-based interest rates has been formed" and the yield curve has "come close to a mature pattern".
• Interest Rate Markets: China's government bond market has significantly deepened and is now the second largest in the world, with increased foreign investor participation.
• Financial Liberalization: Years of financial liberalization mean China's financial markets are now deemed "sufficiently reflective of economic conditions" to extract information and identify underlying shocks.

2. Exchange Rate Flexibility

The Renminbi has become "considerably more flexible" and subject to market forces.

• Shift in Mechanism: A major shift occurred in August 2015 when the PBoC altered the RMB/USD central parity quoting mechanism to enhance the role of market forces.
• Increased Variation: The standard deviation of daily fluctuations of the RMB/USD rate since 2015 was three times larger than in the preceding period.
• De Facto Flexibility: Measures of de facto exchange rate flexibility confirm a substantial increase, placing China's exchange rate flexibility in the middle range for emerging market economies, with the degree of exchange rate management "notably lower" since 2015 compared to the preceding 15 years.

3. Increased Global Systemic Importance

Beyond internal financial reforms, China's economic integration has grown significantly:

• Economic Growth: China accounted for one-third of global GDP growth in the last decade.
• Trade and Commodities: China has assumed a "systemic position in global trade networks and commodity markets," which suggests shocks originating there "could entail spillovers to global asset markets". China consumes a "significantly higher share of global non-energy commodities" than the US.

Need for Systematic Measurement and Future Trajectory

The sources argue that despite anecdotal evidence of spillovers (such as the Evergrande crisis in 2021 or policy shifts in 2015/2016 leading to global jitters), a "systematic understanding" of China's importance to global financial markets was missing.

The necessity of the sources' empirical framework stems directly from this evolution:

• Global financial markets are driven by multiple, interacting shocks (US policy, global risk sentiment).
• To accurately measure China's unique contribution, it is crucial to "purge market developments in China of key global shocks" and control for common factors, a step often overlooked in previous correlation or event-study analyses.

Looking ahead, the ongoing shift in China's policy paradigm toward a more market-based mechanism suggests that "global financial markets are likely to continue to catch up with China’s role in the global economy".

The comprehensive framework developed in the sources is designed to provide a "solid framework for policy makers to monitor the evolving importance of Chinese structural shocks" for global markets going forward. Robustness checks, while showing that changes in China's footprint remained contained over the 2017-2022 sample period (excluding 2020), indicated a tendency for spillovers to increase between 2017 and 2019, consistent with China's policy ambitions.

In essence, China's journey from a closed, centrally managed financial system to one embracing market mechanisms and increasing global influence has transformed its potential for spillovers from limited to material, particularly during periods of high volatility or large domestic shocks ("symptoms of the flu").