# **2.** **<u>Data and Methodology</u>**

Our data set is an unbalanced panel of 84 countries: 21 advanced, 20 emerging, 36 frontier, and 7 that transitioned<sup>2</sup> between groups during the 1991-2016 period, which we consider.<sup>3</sup> Our main variables of interest are country stock market index returns. Country classification follows the MSCI baskets for emerging and developed markets. In the frontier markets category, instead, because of substantial heterogeneity among the MSCI and other indices, we include all countries that have been classified for at least one year as frontier by either MSCI, or by no less than two of the other indices providers.<sup>4</sup> For all indices we ensure that they have adequate liquidity in local currency; hence, we only keep those country-year pairs with at least 150 non-return zeros. We refer the reader to tables DA1-DA5 in the Data Appendix for the list of countries, available observations, DataStream tickers, and descriptive statistics for daily stock market returns. Regarding the other data we use in our analysis, table DA6 provides information about the different sources.

To measure financial globalization, we start from the methodology developed by Pukthuanthong and Roll (2009) and apply the adjustment proposed by Forbes and Rigobon (2002) to remove the variation caused by changes in the volatility of the global factor. Pukthuanthong and Roll (2009) perform their analysis in two stages. In the first stage, they run a principal component analysis of the daily stock market index returns,<sup>5</sup> ��,� , where � denotes countries and � days. They retain the first ten principal components, global factors (GFs), hereinafter. The analysis is performed year by year. One important detail is that the weights used to estimate the GFs in a given year � are computed out of sample from the previous year’s correlation matrix of the returns.<sup>6</sup> In the second stage, the daily returns of each country’s stock market index are regressed on the global factors estimated in the first stage. Thus, for each year � and country �, they estimate: The R-squared of such regressions provides the country-year measures of financial globalization. We deviate from Pukthuanthong and Roll’s (2009) methodology in three ways. First, to estimate the GFs they use 17 countries for which stock market data are available on a daily basis starting from

> 2Portugal (1997), and Israel (2010) transitioned from emerging to frontier countries; Sri Lanka (2000), Jordan (2007), Pakistan (2008), Argentina (2009), and Morocco (2013) from emerging to frontier.

> 3 The choice of the time period is driven by the fact that the index for China, which we need to estimate the first principal component, is only available on Datastream starting from 1991.

> 4 Other providers are FTSE, S&P, and Russell.

> 5 Daily index returns are obtained from DataStream. When available, we use total returns price indices, including reinvestment of dividends, otherwise, we use simple price indices. All returns are measured in USD. DataStream provides an exchange rate conversion option to download the data in the desired currency. However, special care is needed when using it to avoid an artificial increase in the frequency of observations due to currency fluctuations. To deal with such a spurious source of variation, in our data set, returns are estimated only when the value of the index, expressed in local currency, differs from the previous business day. Using such a rule, we also omit holidays and stale values. We also exclude daily returns above 20 and below -20 percent.

> 6 The out of sample computation of the weights, as proposed by Pukthuanthong et Roll (2009), is motivated by the fact that, in the second stage, we estimate the FGIs for the very same countries that have been used to estimate the GFs. This may introduce a positive bias in the FGI estimates. While such a methodology does not rule out completely the bias, which could be still present because of stock market volatility auto-correlation, it certainly attenuates it.

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