**2. Review of Studies**

Contagion has been the subject of extensive academic literature. There exist several definitions for this concept2. In general, contagion is usually used to refer to the spread of market turbulences from one country to the others. It needs to be distinguished from a common shock that affect many country simultaneously. Masson (1998) proposes the term "monsoonal effects" rather than contagion for a such phenomenon.

<sup>1</sup> The interdependence term here refers to a high level of market comovement in all periods.

<sup>2</sup> See Pericoli and Sbracia (2003) for a review of contagion definition.

Contagion can be divided into two categories3: The first category is "fundamental-based contagion", which refers to spillovers resulting from interdependence among markets. In this case, a shock to a market can be transmitted to the others through the linkages (trade, financial linkages) between these markets. Forbes and Rigobon (2001) show that trade linkages are important in transmitting a crisis internationally during the currency crisis in 1990s. Kaminsky and Reinhart (2000) find that beside trade links, financial links are powerful channels of fundamental-based contagion. However, there are several authors who do not consider cross-country propagation of shocks through fundamentals as contagion (See Masson (1998), Forbes and Rigobon (2002)) because it reflects normal interdependence but in crisis period. Hence, another category of contagion labeled "pure contagion" or "non-fundamental based contagion" is considered. This form of contagion cannot be explained by the fundamentals, but rather by the behaviors of investors. When a crisis occurs in one country, investors can withdraw their investments from many markets. "Pure contagion" is hence a panic movement which cannot be justified by economic linkages between markets (See Moser (2003)). In the paper of Kumar and Persaud (2001), the authors show that investors' appetite for risk can conduct to pure contagion.

Based on these two categories of contagion, theories on shock transmission are also divided into two groups: non crisis-contingent theories and crisis-contingent theories. On the one hand, the non crisis-contingent theories, which refer to the "fundamental-based contagion" category, assume that transmission mechanisms do not change after a shock. On the other hand, the crisis-contingent theories, which refer to the "pure contagion" category, propose that there is a significant difference in transmission mechanisms between stable and crisis periods and therefore cross-market linkages increase after a shock4.

In this paper, we follow the second group of theories mentioned above. More specifically, we use the definition of contagion established by Forbes and Rigobon (2001) and Forbes and Rigobon (2002). These authors label "shift-contagion" instead of simply "contagion". They define contagion as *"a significant increase in cross-market linkages after a shock to an individual country (or group of countries)"*. If two markets always show high correlations in all states of the world, this situation should be referred to "interdependence" and not "contagion". Although this definition is clearly narrow and restrictive, it exhibits two important advantages. First, it gives a simple empirical method to test for the existence of contagion. We can simply compare the linkages between two markets during stable periods with those during crisis periods. If there is a shift in linkages between markets during crisis period then we conclude that contagion occurs during the crisis under investigation. Second, by defining contagion as a significant increase in cross-market linkages, we can differentiate the mechanisms of transmission of shocks. The evidence of "shift-contagion" would support for crisis-contingent theories.

One of the statistics used to measure cross-market linkages is cross-market correlation coefficients. The cross-markets linkages can also be measured by probability of a speculative attack, transmission of shocks or volatility. In summary, there are four kinds of tests that are usually used: tests on the correlation coefficients, tests estimating the variance-covariance transmission mechanism across countries, tests for co-integration and tests measuring changes in the propagation by identifying a model with simple assumptions and exogenous events (Forbes and Rigobon (2002)). The tests for contagion based on this statistics will test for an increase in the correlation coefficients between two markets after a shock. Due to its simplicity, this methodology is used in many papers such as King and Wadhwani (1990), Calvo and Reinhart (1996). They all find a significant increase in cross-market correlation during crisis period which gives evidence for the existence of contagion. However, as demonstrated by Forbes and Rigobon (2002), the results of this kind of tests are biased in the presence of heteroskedasticity in market returns. They show that even though the linkages

<sup>3</sup> See Dornbusch et al. (2001).

<sup>4</sup> See Forbes and Rigobon (2001).

between two markets do not change after a shock, these tests do show an increase in cross-market correlation coefficients because of an increase in market volatility. Hence, these tests of contagion need to be corrected for this bias. In their paper, by using tests for contagion based on cross-market correlation coefficient corrected for heteroskedasticiy, Forbes and Rigobon (2002) found no contagion, only interdependence during the 1994 Mexican Peso Crisis, the 1997 Asian Crisis and the 1987 US crisis.

Nevertheless, Billio and Pelizzon (2003) raises an issue with the methodology proposed by Forbes and Rigobon (2002) mentioned above. They show that even if correlation coefficients are adjusted for heteroskedasticity, the traditional tests for contagion are highly affected by the source of crisis and the windows used. Moreover, splitting a sample according to realized or observed values (i.e., high and low volatility) may provide misleading results due to the selection bias (See Boyer et al. (1999)).

Since this test for contagion based on simple correlation coefficients presents obvious limitations, another econometric technique is developed in the literature to study financial contagion: They are dynamic conditional correlation models (DCC-GARCH models). This method has four advantages. First, these models capture the dynamics of correlation coefficients. Many studies prove that cross-market correlations are not constant but vary over time (See Longin (1995), Ramchand and Susmel (1998)). Second, the DCC-GARCH models estimate correlation coefficients of standardized residuals and thus account for heteroskedasticity directly5. Third, the DCC-GARCH models can be used to examine multiple asset returns without adding too many parameters. Finally, this method allows to examine all possible pair-wise correlations for all markets in only a single model (Chiang et al. (2007)).

Recently, this methodology has been usually used in examining financial contagion. Chiang et al. (2007) found evidence of contagion effects during the Asian financial crisis with heteroskedasticity-adjusted simple correlation analysis as well as dynamic correlation analysis. Cho and Parhizgari (2008) otherwise studied contagion during the Asian financial crisis in 1997 by using dynamic conditional correlation (DCC) means and medians difference tests. They considered two sources of contagion, Thailand and Hong Kong, and found the presence of contagion in equity markets across all markets studied: Korea, Malaysia, Philippines, Singapore, Taiwan, Indonesia. Naoui et al. (2010) investigated contagion during the 2007 US subprime crisis in using DCC-GARCH models and adjusting correlation coefficients to control for heteroscedasticity. They found contagion effects from US toward Argentina, Brazil, Korea, Hong Kong, Malaysia, Mexico and Singapore.
