1973; instead, we look at the 20 biggest stock markets in 2006,<sup>7</sup> a year in the middle of our period of analysis. The original basket includes mostly developed economies,<sup>8</sup> while the new basket includes several developing economies, which are playing an increasingly important role in the global economy, and thus should enter in any meaningful index of globalization. Indeed, in 2012, the original basket accounted for 77 percent, while our basket for 94 percent of global stock market capitalization.<sup>9</sup> Hence, the global factor that we estimate does not reflect the dynamics of developed economies only.

The second difference is in the number of GFs used to estimate the R-squared: while Pukthuanthong and Roll (2009) consider the first ten GFs, we only retain the first one. The reason is that, since we deal with a heterogeneous set of countries, we expect some clustering in the GFs. It is thus likely that some of the GFs with lower eigenvalues capture only the dynamics of emerging or frontier markets. Since the focus of this paper is on global (rather than regional or within asset types) co-movements we decided to use the first GF only. Nonetheless, we also replicate our main results using ten GFs for comparability.

Third, and more important, we correct Pukthuanthong and Roll’s index acknowledging that changes in the R-squared of regressions (1) may either reflect changes in the interlinkages between economies or changes in the volatility of the GF(s). This argument is based on Forbes and Rigobon (2002), which look at the Pearson correlation coefficients between stock market indices, and establishes that they are conditional on measures of volatility. More precisely, they show that during financial crises the increase in the correlation between different countries’ stock market indices may just reflect the increase in volatility. In the paper, they propose a correction for volatility, which we adapt to our framework to remove from the R-squared the variation driven by changes in the volatility of the GF. Denoting by UFGI the uncorrected index computed with one GF (and UFGI10, the one computed with ten), the corrected index (FGI) is equal to with where �<sup>�</sup> (��)� is the variance of the global factor in year �, and �<sup>�</sup> (��)� is the base variance of the global factor. As a measure of base variance, Forbes and Rigobon (2002) choose the variance in tranquil times that precede periods of financial turbulence. In this paper, we choose the variance of global factor daily observations over the entire sample period. Hence, in any given year, �, �� can be positive or negative, and the FGI can be lower or higher than the UFGI.

We refer the reader to Appendix B for the derivation of equation (2) and additional technical details. Here, it is important to underscore that the FGI is not affected by changes in the variance of the global

> 7 This set includes Australia, Belgium, Brazil, Canada, France, Germany, Hong Kong SAR, China, India, Italy, Japan, Mexico, China, Singapore, South Africa, the Republic of Korea, Spain, Switzerland, the Netherlands, the United Kingdom, and the United States.

> 8 The only non-developed market included is South Africa.

> 9 These estimates are based on the stock market capitalization of domestically listed companies from WDI.

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