3. An Investigation into Financial Development and Contagion
3.7 Methodology
3.8.1 Index-trend model
/ , ,
,
,t ijt iit jjt
ij q q q
(7)
As noted above, contagion has to be distinguished from a peak in correlations following a rare event. For that purpose, the dynamic correlations obtained by the procedure above are further regressed using time dummies. More specifically, the persistence of the contagion effect is assessed using an AR(1) model:
t ij t t
ij t
ij, 0 1 ,12D e ,
, (8)
Whereet t1~N(0,Mt),M is the conditional variance of e determined by a GARCH(1,1) process and D is the time dummies for crisis periods. The effect of contagion can then be deemed to be persistent if the estimates for
2 are significantly different than zero.3.8 Results
3.8.1 Index-trend model
The results for the index-trend model are provided in Table 3.12 and Table 3.14. The correlations consider three distinct periods. The pre-crisis period runs from 1 January 2007 until the bankruptcy procedures of Lehman Brothers, 15 September 2008. The post-crisis period starts the day following the Lehman event until the ousting of Mubarak on 10 January 2011. The post-Mubarak period runs until the end of the sampling period on 5 November 2012.
The correlation coefficients between the region’s stock markets and commodity indices highlight several interesting findings. First, oil and grain prices are strongly and positively correlated with the stock markets of all countries except Lebanon, Morocco, Palestine and Syria. For Tunisia, grain prices have a positive impact on Tunisia’s stock market returns, although the impact is relatively small. The commodity prices heavily influence the market returns in the relatively advanced countries, such as Israel, Turkey, the US and Europe.
Table 3.12 Correlations with commodity indices
Notes: Figures depict the sensitivity of the stock market returns to commodity prices, as defined in equation (2). ***,**,* stand for significance at 1%, 5% and 10%.
Turning to how correlations evolved throughout the sampling period, the results show that the correlations clearly increased in Israel, Turkey, the US, and Europe after the crisis (up until the ousting of Mubarak). The same is also true only for grains in Egypt, especially after the onset of the Arab spring. The correlation with grains also increased in the post-crisis period, up until January 2011, for Palestine. The correlation of the market returns with Brent oil increased in Jordan, but only weakly so.
The correlation of the market returns with the US and EU markets, as depicted in Table 3.13, highlight a close relationship between these countries. Clearly, the correlations with the US and EU are extremely strong for Egypt, Israel and Turkey. The results show that the relationships have not increased after the crisis for most cases, except for Egypt (with the US only), Tunisia (only weakly) and Jordan (relatively strongly so). Even though Israel and Turkey’s stock markets display the closest interdependence with both the EU and the US, the relationship has not become more sensitive after the crisis or the ousting of Mubarak.
Table 3.13 Correlations with the US and Europe
Notes: Figures depict the sensitivity of the stock market returns to European and US market returns, as defined in equation (2). ***,**,* stand for significance at 1%, 5% and 10%
The ousting of Mubarak has little effect on the interdependence of the region’s markets with the EU or the US. Paradoxically, Egypt’s interdependence with the US has slightly strengthened and that with the EU weakened after the toppling of Mubarak, although the differences are too small to gauge significance. Morocco’s stock market became more sensitive to developments both in the EU and US during the Arab spring.
So did Israel’s stock market, but only with the US market. Tunisia’s stock markets, on the other hand, became less related with the US and the EU.
While these results are relatively vague, Turkey’s emergence as an alternative for global investors in the region contributed to its decoupling from the US and the EU markets.
These results have to be interpreted carefully, since they simply show an increasing sensitivity, which may not be lasting and may not be significant. Moreover, inherent volatilities in the stock market returns are not clearly taken into account, which the next section turns to.
3.8.2 Return volatility model
The coefficient estimates for the GARCH-DCC model in equation (2) are summarised in the annex at the end of this chapter. The results show that the stock market returns are highly elastic to the developments in the US as
well as the ARCH/GARCH terms in all of the countries, justifying the use of the empirical analysis that is robust to the heteroscedasticity of error terms.
The dynamic correlations with the US DJCI market returns are depicted in Figure 3.3. A visual examination reveals that the Lehman event has had a clear and persistent upward impact on market correlations for Europe and Turkey but not in other countries. A short-lived upward impact is also present in Egypt, Morocco, Palestine and Tunisia. In turn, Israel and Jordan’s correlation coefficients appear to be relatively random.
The magnitude of correlation appears to be the highest in Europe, Israel and Turkey, which is most likely reflecting the closer linkages between these markets and the US.
Figure 3.3 Dynamic correlations with US Dow Jones Composite Index (DJCI)
(a) Europe (Euronext-100) (b) Egypt (EGX-30)
(c) Israel (TA-100) (d) Jordan (ASE Free float index)
.6.7.8.9rho_eur
0 100 200 300 400
week
.2.3.4.5.6rho_egy
0 100 200 300 400
week
.3.4.5.6.7.8rho_isr
0 100 200 300 400
week
0.1.2.3.4rho_jor
0 100 200 300 400
week
(e) Morocco (MSCI, Large & Mid cap) (f) Palestine (Al-Quds)
(g) Tunisia (TUNINDEX) (h) Turkey (ISE-100)
The dynamic correlation coefficients obtained from the GARCH-DCC were regressed using the AR(1) specification depicted in equation (8) to determine whether any changes that occurred in the two key events, i.e. the Lehman event on 15 September 2008 and the fall of Egypt’s General Mubarak on 10 January 2011, were permanent (Table 3.14). More specifically, the regression regresses the time series depicted in Figure 3.3 using an AR(1) specification.
The empirical tests lead to the following key findings.
First, the correlation coefficients have strong persistent and dynamic factors. More specifically, all stock markets except Morocco exhibit a significant constant correlation term (0). For Israel, the constant correlation is surprisingly high at 0.48. This implies that almost half of the variance in the (dollar-denominated) returns of Israel’s Tel Aviv stock market index is persistently determined by the returns in US DJCI. Jordan’s stock market also has a relatively high stand-alone correlation with the US DJCI,
-.4-.20.2.4rho_mar
0 100 200 300 400
week
.05.06.07.08rho_pal
0 100 200 300 400
week
-.20.2.4.6.8rho_tun
0 100 200 300 400
week
.3.4.5.6.7rho_tur
0 100 200 300 400
week
although the magnitude is much smaller. For other countries, the persistent term is smaller than 0.1.
Table 3.14 AR(1) regression for dynamic correlation coefficients for US DJCI
Europe Egypt Israel Jordan Morocco Palestine Tunisia Turkey
0 0.0803*** 0.0668*** 0.482*** 0.141*** 0.00442 0.0130*** 0.0290*** 0.0377***
(0.0291) (0.0117) (0.0323) (0.0108) (0.00524) (0.00165) (0.00823) (0.0127)
1 0.892*** 0.785*** 0.0863 0.0537 0.881*** 0.777*** 0.756*** 0.923***
(0.0396) (0.0368) (0.0585) (0.0585) (0.0248) (0.0283) (0.0330) (0.0248) Lehman 0.0123** 0.0074* 0.0218** 0.0183*** 0.0078 0.0002 0.0136 0.0081*
(0.00534) (0.00385) (0.0102) (0.00689) (0.00756) (0.000236) (0.0110) (0.00445) Mubarak -0.00321* -0.00529 -0.0134 -0.00467 -0.00203 -0.000275 -0.0177 -0.0108***
(0.00187) (0.00437) (0.00996) (0.00566) (0.00824) (0.000237) (0.0120) (0.00389)
Obs. 301 296 305 305 305 305 301 301
R² 0.957 0.655 0.026 0.035 0.767 0.611 0.595 0.915
Second, the dynamic correlation coefficients of most countries exhibit a strong autocorrelation term. Indeed, the AR(1) coefficients (1) of all countries except Jordan and Israel are in the vicinity of 0.8-0.9. Thus, while the correlations are persistently high in Israel and to a lesser degree in Jordan, they follow a positive reinforcing pattern in the other countries. In other words, a higher correlation in one period implies a higher correlation in the next one as well. Thus, correlations appear to be self-supporting, possibly highlighting the strong two-way trade and investment linkages.10
Third, and perhaps most importantly for our purposes, the Lehman event has increased correlations in several countries. This is especially the case for Europe, Israel and Jordan, where the event has led to a general increase of over 1% of correlation. The correlation has also increased in Egypt and Turkey, although only by 0.7-0.8%. It should not be surprising
10 There is some indication that many countries in the region have two-way flows with the US. The US has been running a trade surplus and an investment deficit (i.e. more flows from the region to the US than the other way around) with most countries in the region. This two-way dependency may explain the self-supporting nature of capital market co-dependencies. In turn, the US has run a large and consistent trade deficit with Israel since the late 1990s while capital inflows from the US into Israel have also been very strong. Jordan’s trade and investment linkages with the US remain relatively small.
that the correlations have increased significantly for these traditional US allies in the region.
Fourth, the departure of Mubarak and the following political uncertainty have reduced the correlations in the entire sample, although the level of significance differs from one country to another. In particular, the Arab spring events appear to have reduced the Europe and US-Turkey correlations significantly. US-Turkey has decoupled from the US as its stock exchange increasingly drew more investors searching for yield among emerging markets due to the country’s emergence as a stable alternative to the rest of the region. In turn, the (much-smaller) drop in correlation with Europe may be explained either by the concurrent home-grown issues in the EU (i.e. the eurozone sovereign debt crisis in 2011).
More generally, the lower correlation coefficient may also mean that the troubles faced by the EU and the Arab spring might have led to a decoupling of the European economy.11