Session I-b: Financial Development I Presenter: Richard Burdekin (Claremont McKenna College) 1st International Workshop on the Chinese Development Model

1. QUANTIFYING CHINA’S FINANCIAL
REACH UP THROUGH THE PANDEMIC:
THE AFRICAN EXPERIENCE
Richard C. K. Burdekin, Dawson Reckers and Ran Tao
Claremont McKenna College
EY-Parthenon, San Francisco, California
Lake Forest College and University of Wisconsin, Whitewater

2. 龙在等待

3. Background and Motivation
• In 2009, China replaced the United States as the largest trading
partner to African nations.
• China and major African countries such as Nigeria in 2018 formally
agreed on a currency-swap deal worth billions of dollars
• It is critical to understand spillover effects and differences of
transmission mechanisms between various markets and countries
• Prior work suggests that the rapid growth of the Australia–China trade
partnership helped fuel significant liquidity and inflation transmission
(Burdekin and Tao, 2020).

4. Prior Research
• Guo and Ibhagui (2019) find evidence of cointegration among China
and Africa’s major stock markets. The relationship peaked during the
2008 financial crisis but weakened after the crisis
• Ahmed and Huo (2018) observe spillover effects in terms of both price
movement and volatility behavior, consistent with African stock
markets integrating with the Chinese stock market
• Osabuohien-Irabor (2021) suggests a unidirectional causality both in
mean and variance from the U.S. and Chinese markets to African
capital markets

5. Summary of Main Findings
• We allow for variation across high volatility and low volatility regimes
• We offer some initial evidence on how the pandemic impacted Chinese-
African stock market correlations
• Our overall findings suggest that rising trade connections have been
accompanied by financial connections between the Shanghai Stock
Exchange and eight leading African stock markets

6. Stock Market Properties
Country Exchange Market Cap
(in billions of USD)
Number of
listings
China Shanghai Stock Exchange (SSE) $7,620 2057
South Africa* Johannesburg Stock Exchange $1,282 442
Morocco* Casablanca Stock Exchange $71.1 81
Nigeria* Nigerian Stock Exchange $67.8 328
Egypt* Egyptian Exchange $44.2 176
Kenya* Nairobi Securities Exchange $19.8 65
Tanzania Dar es Salaam Stock Exchange $6.97 27
Uganda* Uganda Securities Exchange $6.8 18
Zambia Lusaka Stock Exchange $3.7 22
Table 1: Basic Properties of the Stock Markets Included in Our Sample (2021 values).

7. FDI Flows from China, 2010-2020
Country Mean Std. dev. Min Max
Zambia 261.63 128.12 75.05 523.73
South Africa* 210.01 417.22 -814.91 843.22
Kenya* 213.73 178.17 10.37 630
Nigeria* 186.30 78.99 50.58 333.05
Tanzania 124.56 53.83 25.72 226.32
Egypt* 103.63 64.20 10.96 221.97
Uganda* 94.54 70.22 9.79 225.80
Morocco* 13.56 43.33 -95.16 90.78
Table 2: FDI Flows from China, Millions of US dollars (2010-2020)

8. Data and Sample Period
• Weekly data from 2010 to 2020 for market indices of the largest eight
African stock exchanges
• The Shanghai Composite index and Industrial index capture Chinese
stock market effects
• China’s annual FDI flow to each country is included in the analysis
along with the US S&P 500 index

9. Shanghai and US Stock Market Trends, 2010-2020

10. Stock Market Index vs. FDI Flow

11. Stock Market Index vs. FDI Flow (contd.)

12. Econometric Approach
•Panel regression
•Granger causality
•Markov switching regressions

13. Panel Regression Results
Table 3: Results of panel fixed-effect model, 2010-2020. 4,560 observations.
𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1𝑞𝑞 �
𝑞𝑞=0
2
𝑆𝑆𝑆𝑆𝑆𝑆𝑡𝑡−𝑞𝑞 + 𝛽𝛽2𝑞𝑞 �
𝑞𝑞=0
2
𝑆𝑆𝑆𝑆𝑆𝑆𝑡𝑡−𝑞𝑞 + 𝐹𝐹𝐹𝐹𝐹𝐹𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖

14. Granger-Causality Results (2010-2020)
Country Direction of
Causality
Coefficient χ2 p-value Lag Length
Egypt SSEEGX 0.251 21.84 0.000 1
Kenya SSENSE 0.133 15.34 0.000 1
Morocco SSEMOSE 0.094 7.81 0.020 2
Nigeria SSENGSE 0.183 16.83 0.000 1
South Africa SSEJSE 0.001 11.98 0.001 1
Uganda SSEUGSE 0.340 34.75 0.000 2
Table 4: Results of Granger causality tests. 572 observations.

15. Granger-Causality Results (2010-2019)
Country Direction of
Causality
Coefficient χ2 p-value Lag
Length
Egypt SSEEGX 0.267 17.36 0.000 1
Kenya SSENSE 0.113 9.50 0.002 1
Nigeria SSENGSE 0.185 13.80 0.000 1
South Africa SSEJSE 0.001 6.75 0.009 1
Uganda SSEUGSE 0.304 20.36 0.000 2
Table 5: Results of Granger causality tests. 520 observations.

16. Markov-Switching Dynamic Regression
𝑦𝑦𝑡𝑡 = 𝜇𝜇𝑠𝑠𝑡𝑡
+ 𝑥𝑥𝑡𝑡𝛼𝛼 + 𝑧𝑧𝑡𝑡𝛽𝛽𝑠𝑠𝑡𝑡
+ 𝜀𝜀𝑠𝑠𝑡𝑡,𝑡𝑡
• 𝜇𝜇𝑠𝑠𝑡𝑡
is state-dependent intercept
• 𝑥𝑥𝑡𝑡 is a vector of exogenous variables with state-invariant coefficients 𝛼𝛼
• 𝑧𝑧𝑡𝑡 is a vector of exogenous variables with state-dependent coefficients 𝛽𝛽𝑠𝑠𝑡𝑡
• 𝜀𝜀𝑠𝑠𝑡𝑡,𝑡𝑡 ∼ N (0, 𝜎𝜎𝑠𝑠𝑡𝑡
2
) Its variance 𝜎𝜎𝑠𝑠𝑡𝑡
2
changes under different regimes of 𝑦𝑦𝑡𝑡
• The time of transition between regimes and the duration in a particular regime are
both random

17. Markov Switching Regression Results-Egypt

18. Markov Switching Regression Results-Kenya

19. Markov Switching Regression Results-
Morocco

20. Markov Switching Regression Results-Nigeria

21. Markov Switching Regression Results-South
Africa

22. Markov Switching Regression Results-
Tanzania

23. Markov Switching Regression Results-Uganda

24. Markov Switching Regression Results-Zambia

25. Conclusions
• Growing trade and investment ties between China and Africa have
been accompanied by significant financial market integration
• Significant effects of the Shanghai Industrial Index persist throughout
• Markov-Switching analysis shows increased intensity of Chinese effects
during periods of higher market volatility
• Evidence of heightened connections following the onset of the
coronavirus pandemic

26. More General Implications
“Quand la Chine s’eveillera, le monde
tremblera …”
(Napoleon Bonaparte)
Quote attributed by Alain Peyrefitte, The Chinese: Portrait of a People, translated from the French by Graham
Webb (Indianapolis/New York: Bobbs-Merrill, 1977).