The impacts of the coronavirus on the global economy: Part VIII: The stock markets

Some stock exchanges are recovering faster than others from the financial crisis caused by the outbreak of the coronavirus disease. On April 28, the NASDAQ composite index and the SIX Swiss exchange mid-cap index were respectively only 5.1 % and 7.3 % below their levels of January 6. On the other hand, the year-to-date return of the Brazil stock exchange index was -30.43 %. Those of the London Stock Exchange FTSE All Share, the Euronext N150, the Bombay Stock Exchange sensitive index, and the Australia Securities Exchange index were about -22 %.

Between January 6 and April 28, capital loss on the Hong Kong Stock Exchange went as low as -25.3 % (this value is the percentage change between the lowest and the highest values of the benchmark index). On the NASDAQ, the range of the capital los was 30.1 %, but this exchange is recovering faster than the Hong Kong Stock Exchange. Why capital loss has been more important on some stock exchanges than the others and why some exchanges have recovered faster than the others? There are two possible explanations. The first one is the sensitivity of the exchange to factors affecting the global economy (the systematic risk) and the second one is the structure or the composition of the exchange.

In the table below, it appears that the systematic risks on the New York Stock Exchange (NYSE) and the Brazil Stock Exchange (Bovespa) are very high during turbulent periods (actually, they are greater than 1). This means that these two exchanges are more exposed to global risk than the other major exchanges. This explains why the year-to-date returns of their benchmark indices are very low. The systematic risk on the Hong Kong Stock Exchange is only .55 and the year-to-date decrease in its benchmark index is less than those on the NYSE composite and the Bovespa index.

Table: Year-to-Date Returns on Apr 28, 2020 and Systematic Risk during Turbulent Periods of some Stock Exchanges.

Stock Exchange	Year-to-Date Return	Systematic Risk
NYSE	-18.81 %	1.11
NASDAQ	-5.11 %	1.11
Tokyo Stock Exchange	-14.80 %	.55
London Stock Exchange	-21.99 %	.83
Hong Kong Stock Exchange	-12.93 %	.51
Euronext	-21.79 %	.88
Toronto Stock Exchange	-13.49 %	.87
Bombay Stock Exchange	-21.05 %	.54
Frankfurt Stock Exchange	-17.76 %	.98
Australian Securities Exchange	-21.12 %	.59
SIX Swiss exchange	-7.28 %	.72
Brazil Stock Exchange BOVESPA,	-30.43 %	1.06

The systematic risk on the NYSE is the same as on the NASDAQ, but the latter exchange is recovering faster than the former. This means that the systematic risk is not the only factor explaining returns on the exchanges. The activity sector and the performance of the main companies in the benchmark indices also explain their year-to-date returns. Half of the companies in the NASDAQ composite operate in the technology sector and 11 % in the healthcare sector. As I show in my previous post [here], these are the two sectors that are performing better during this crisis. Likewise, more than half of the components of the Swiss exchange mid-cap index operate in the healthcare, the technology or the telecommunication sector.

On April 24 the global financial turbulence score rose from 4.38 to 4.62 (a 5.3 % rise). After keeping falling since March 20, The VIX (the implied volatility index) rose by 3.5 % to 37.19, on April 24.

Figure : Global Financial Turbulence Scores and VIX, Jan 8, 2000 - Apr 24, 2020

Dataset and Code

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The Latest Global Financial Turbulence Scores.

Date	Score
Feb 14, 2020	1.36
Feb 21, 2020	6.23
Feb 28, 2020	3.68
Mar 6, 2020	9.74
Mar 13, 2020	12.89
Mar 20, 2020	9.37
Mar 27, 2020	8.03
Apr 3, 2020	7.70
Apr 10, 2020	4.10
Apr 17, 2020	4.38
Apr 24, 2020	4.62

The components indices of the global financial turbulence score

(1) NYA: the New York Stock Exchange composite index, (2) IXIC: the NASDAQ composite, (3) N225, the Tokyo Stock Exchange average index, (4) FTAS, the London Stock Exchange FTSE all share, (5) HSI, the Hong Kong Stock Exchange index, (6) N150, the Euronext Next 150 index, (7) GSPTSE, the Toronto Stock Exchange composite index, (8) BSESN, the Bombay Stock Exchange sensitive index, (9) GDAXI, the Frankfurt Stock Exchange performance index, (10) AXJO, the Australian Securities Exchange S&P 200, (11) SSMI, the SIX Swiss exchange mid-cap index, and (12) IBOVESPA, the Brazil Stock Exchange index.

Formula

$$d_t² = (r_t - μ ) Σ ^-1 (r_t - μ )',

where $d$ denotes the turbulence score, the vector $r$_t lists the current growth rates of the benchmark indices, the vector $$ μ their historical averages, and $$ Σ designates their variance-covariance matrix. For further details, see Mark Kritzman and Li Yuanzhen (2010).