Abstract

Unconditional and conditional correlations have played a central role in portfolio analysis, optimization, and performance measurement. However, recent studies show these two correlation measures are inappropriate for measuring both financial integration and, therefore, diversification benefits. We use an alternative correlation measure that we refer to by factor model-implied correlation estimated from the systematic (predictable) portion of returns of a multi-factor model with several global risk factors. Estimated implied correlations, covariances, variances, and in-sample (predicted) mean returns are used to calculate optimal US and Asian equities’ asset allocation weights in alternative Global equity portfolios varying by Asian equity market combined with US equities, as well as by whether: (i) implied or unconditional statistics are used; and (ii) portfolios are optimized by Sharpe’s ratio-maximization or variance-minimization. Risk-adjusted returns of alternative actively-managed Global equity portfolios are compared to US equities’ risk-adjusted returns. We find Global equity portfolios with asset allocation weights calculated using factor model-implied statistics uniformly yield higher risk-adjusted returns than US equities and Global equity portfolios with asset allocation weights calculated using unconditional portfolio statistics. In actively-managed Global equity portfolios with asset allocation weights calculated using implied statistics, India and Taiwan consistently rank as top contributors, while South Korea, Singapore, and Hong Kong consistently rank as bottom contributors to enhancing US equities’ risk-adjusted returns. While our analyses are dynamic, they use implied portfolio statistics estimated from historical returns’ distributions. Future studies can extend this research using conditional(out-of-sample) ex-ante estimates of systematic returns, covariances, variances, and correlations in examining emerging markets’ contributions to developed markets’ equities.

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