Annex 1. Stress Test
Annex 2. Diebold-Yilmaz Connectedness Index
Annex 3. Event Study
Annex 4. Vector Autoregression Model
The impact of Chinese financial market developments and economic news on global and EM markets is analyzed in a vector autoregression (VAR) framework. The baseline VAR includes three groups of endogenous variables: Chinese (equities, bilateral exchange rate against the U.S. dollar), global (VIX, S&P 500 stock index, U.S. 10-year yield, oil price and metals price index) and EM25 (equities and bilateral exchange rate against the U.S. dollar). VIX and the U.S. 10-year yield enter the model as simple daily differences, whereas all the remaining variables are included as natural logarithm differences. The model also includes two exogenous variables: the surprise in Chinese industrial production (IP) data (calculated as the difference between the released value and the consensus forecast of the value at the time of release and zero on other days) and the Citi economic surprise index for the U.S. economy (which is an amalgamation of similar surprises in a variety of economic variables including GDP, IP, PMI, etc.).
The baseline model is run on daily data from January 1, 2005, through April 22, 2016, with five lags on both endogenous and exogenous variables. Variables are ordered chronologically for the Cholesky decomposition: Chinese variables followed by global variables and EM variables.26
All the results in the main text survive various robustness checks. Altering the number of lags in the model or replacing Chinese IP data surprises with the Citi economic surprise index does not materially affect the outcome. The baseline model specification includes the average daily returns on EM equities and exchange rates. The results do not differ substantially if the EM variable responses are instead calculated as averages of responses of individual EM country asset prices estimated in separate VAR models. Excluding Asian EM countries that do not follow the baseline chronological ordering of variables (since they are in the same or a similar time zone as China) from the EM average likewise produces similar results. Finally, placing the global variables ahead of the Chinese variables in the Cholesky decomposition ordering also qualitatively preserves the results regarding impact of China on EM asset prices.27
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We are grateful to Gillian Adu, Wang Ruosi, and Tessy Vasquez Baos for excellent assistance. The note benefited from useful discussions with Patrick Balgrave, Vikram Haksar, Petya Koeva Brooks, and Esteban Vesperoni; we also thank the IMF Spillover Taskforce for their insightful comments.
Errors and omissions were relatively small until recently, when they reached about −1¾ of GDP, suggesting even larger outflows.
Data used in this section may provide only a partial view of Chinese financial flows because these flows may be channeled through Hong Kong SAR and other financial centers.
Based on Treasury International Capital data. Figure includes U.S. Treasury holding by China and Belgium, where most of the holdings are believed to reflect those of China.
This result holds for an alternative identification scheme using Cholesky decomposition (Annex 1).
Arslanalp and others (2016) find that financial spillovers from China to Asian countries have increased since the global financial crisis and are higher for economies with stronger trade links with China.
Refers to level of fundamentals below which there is increased likelihood of a sudden stop in capital flows.
This is consistent with the findings of Kolerus, N’Diaye, and Saborowski (forthcoming).
These are obtained by replacing each of the three Chinese variables in the VAR model by two separate variables. The negative and positive variables take the values of their underlying variables when those are negative and positive, respectively, and take zero otherwise. The separate estimates of the impact of the negative and positive Chinese variables then allow differentiating between the effect of negative and positive developments in the Chinese markets and economy. See Mork (1989) and Hamilton (2003) for a similar econometric approach.
Figure 15 depicts the results of an alternative specification that, in addition to the baseline version of the model, includes an additional variable for each of the three Chinese variables that equals the underlying variable when the latter is more than two standard deviations below its mean and zero otherwise. The figure stacks the impact of any change in Chinese equities and the “large falls” in Chinese equities and compares them to the baseline estimates of the effect of Chinese equities on global and EM markets.
Figure 16 shows the results of the baseline model combined with a model where all of the global variables are treated as exogenous. It attributes the impact of the Chinese variables in the latter model to direct effects, whereas the difference between the baseline model (where global variables are endogenous) and the exogenous model is attributed to indirect effects via the global variables.
While it is ideal to use the common equity Tier 1 capital ratio (CET1 ratio), it is hard to obtain the cross-country aggregate data. Therefore, we use Tier 1 capital data at country levels as reported in the IMF’s Financial Soundness Indicators database.
Using EMBI yields for EMs is due to the small country coverage of data on long-term interest rates among EMs.
Argentina, Brazil, Chile, Colombia, Hungary, India, Indonesia, Malaysia, Mexico, Peru, Philippines, Poland, Russia, South Africa, Thailand, and Turkey.
On any given day, Chinese markets generally close before U.S./ most commodity/most EM markets open.
To preserve chronological ordering in this alternative setup, global variables are lagged one day when placed ahead of the Chinese variables. The resulting impact of the latter on the former is understandably different from that obtained in the baseline specification. Yet the impact of the Chinese variables on EM asset prices remains qualitatively similar.