Should Americans invest internationally? Mean-variance portfolios optimization and stochastic dominance approaches

Fathi Abid, Mourad Mroua, Wing Keung WONG*

*Corresponding author for this work

    Research output: Contribution to journalJournal articlepeer-review

    9 Citations (Scopus)

    Abstract

    Using both mean-variance portfolio optimization (MVPO) and stochastic dominance (SD) approaches, this paper investigates whether international diversification and home bias inertia are substitutes or complements for Americans. More specifically, we compare daily closing prices of 30 US stocks and the stock indices from American, Latin American, and Asian financial markets, including both emerging and major markets. Results from the MVPO show that a domestic diversification strategy performs better for any risk level up to 0.5%, whereas international diversification performs better for any risk level higher than 0.5%. Some results from the SD test support international diversification, some promote home bias, and still others conclude that there is no difference between investing domestically and internationally. However, our findings show that one could not find any single internationally diversified portfolio that dominates all domestically diversified portfolios and, similarly, one could not find any single domestically diversified portfolio that dominates all internationally diversified portfolios. At last, our SD findings also recommends that the US investors have a 'home bias' if they prefer less risk and to be 'internationally diversified' if they prefer higher risk.

    Original languageEnglish
    Pages (from-to)89-102
    Number of pages14
    JournalRisk and Decision Analysis
    Volume4
    Issue number2
    DOIs
    Publication statusPublished - 2013

    Scopus Subject Areas

    • Statistics and Probability
    • Finance
    • Economics and Econometrics
    • Statistics, Probability and Uncertainty

    User-Defined Keywords

    • Home bias
    • international diversification
    • mean-variance portfolio optimization
    • Monte Carlo and bootstrap p-values
    • stochastic dominance

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