This study is about the importance of considering a full set of countries’ covariances for bilateral asset portfolio decision making rather than just analyzing bilateral market correlations. The authors apply methods of spatial econometrics to account for the interdependence of cross-country holdings.
How Is This Research Useful to Practitioners?
In today’s global economy, understanding the international stock market’s correlation matrix has become essential for investors who want to diversify their portfolios. The authors attempt to explain one of the most important diversification puzzles in international macro-finance. Investors tend to diversify little abroad; when they do invest abroad, they prefer a country they know well, resulting in fewer diversification benefits.
The authors argue that understanding the correlation puzzle’s empirical findings requires a multi-country perspective. Their research model shows that the effect of stock return correlations on bilateral asset holdings also depends on the stock return correlations of other countries. It also shows that the overall level of equity home bias depends on the heterogeneous stock return correlations among all countries. Interpreting the correlation of equity returns as a measure of contiguity, the authors merge the gravity approach of international capital allocation with spatial econometric techniques. The empirical result, after controlling for multilateral stock return correlations with other countries, confirms that a higher stock return correlation lowers bilateral equity asset holdings, as theory predicts. Thus, perceiving lowly correlated stock markets as close neighbors in the context of the spatial framework is useful in visualizing and understanding patterns in international portfolio diversification. So, this paper is excellent reading material for portfolio managers.
How Did the Authors Conduct This Research?
The authors cull data on international bilateral equity holdings from an International Monetary Fund (IMF) survey covering the four years preceding the recent financial crisis—that is, 2001–2006. They construct an N-country model with heterogeneous stock return correlations and use a two-country pair model that builds up to N countries. The model represents a consumer’s dynamic optimization that uses covariance structures on income and follows the approach of Devereux and Sutherland (Journal of the European Economic Association 2011). Because covariance is too complex for an analytical experiment to show the desired results, the authors also conduct a numerical experiment. A log of stock market capitalization data from world development indicators provides a capitalization variable. Bilateral monthly return data are extracted from Datastream to form annual equity return variances/covariances for each country pair. The authors also propose a simulation of a three-country model. To deal with the endogeneity of stock return correlations, they instrument current correlations with past correlations. They conduct a series of robustness tests and note that the empirical results remain unchanged.
The authors analyze the impact of third-country effects on the direction and strength of bilateral equity flows. Using aggregate data on bilateral cross-border equity holdings, they investigate whether investors correctly hedge their overexposure to domestic risk, the well-known equity home bias, by investing in foreign stock markets that have low correlations with their home stock market. However, whether a potential international market can generate better returns or reduce portfolio risk will depend not only on its bilateral correlations but also on the full set of covariances of many countries. Overall, the authors provide an astute analysis that demystifies the correlation conundrum.