Factor Exposure Analysis 115: Measuring International Exposures
Should correlated independent variables be avoided?
May 2025. Reading Time: 10 Minutes. Author: Nicolas Rabener.
SUMMARY
- Variables used in a factor exposure analysis should be uncorrelated
- However, adding correlated equity indices can increase the explanatory power
- Also, provides clearer insights on the performance & risk contributors
INTRODUCTION
India’s economy stands out as a strong performer, supported by a pro-business government, a manageable public debt-to-GDP ratio of 80%, and a youthful population with an average age of 30. In contrast, China faces challenges with a less business-friendly environment, soaring public debt exceeding 300% of GDP, and an aging population with an average age of 39.
Investors looking to gain exposure to India’s stock market can do so through ETFs like the iShares MSCI India ETF (INDA) listed on U.S. exchanges. However, since Indian and U.S. trading hours do not overlap, questions arise about how these Indian funds perform when the Indian stock market is closed (read Diversifying via Time Zones).
When evaluating the risk and returns of Indian funds trading in the U.S. market, should investors rely solely on U.S. equity indices or also consider international benchmarks? In theory, regression analysis variables should be independent to minimize multicollinearity.
In this research article, we will examine the impact of using correlated equity indices in a factor exposure analysis.
U.S. VS GLOBAL EX U.S. EQUITY INDICES
With the globalization of the economy and advancements in technology making trading more accessible, global stock markets have become increasingly correlated. For instance, the correlation between the U.S. market (represented by the S&P 500) and international markets (measured by the MSCI ACWI ex U.S.) was 0.89 from 2008 to 2025.
