Abstract
The paper develops measures of home bias for 48 countries over the period 2001 to 2011 by employing various models: International Capital Asset Pricing Model (ICAPM), Mean-Variance, Minimum-Variance, Bayes-Stein, Bayesian and Multi-Prior. ICAPM country portfolio weights are computed relative to world market capitalization. Bayesian models allow for various degrees of mis-trust in the ICAPM model. Multi-Prior restricts the expected return for each asset to lie within specified confidence interval around its estimated value. Mean-Variance computes optimal weights by sample estimates of mean and covariance matrix of sample return. Bayes-Stein shrinks each asset’s historical mean return toward the return of the Minimum Variance Portfolio and improves precision associated with estimating the expected return of each asset. The paper finds that foreign listing, idiosyncratic risk, beta, inflation, natural resources rents, size, global financial crisis and institutional quality has significant impact on home bias. There are policy implications associated with home bias.
Highlights
There is a body of literature on equity home bias1 that focuses on the fact that investors are found to hold disproportionately large share of their wealth in domestic portfolios as compared to predictions of standard portfolio theory
International Capital Asset Pricing Model (ICAPM), benchmark is characterized by the weight of a country in the world market capitalization
This paper develops measures of home bias for a sample 46 countries5 by employing various models i.e. ICAPM, Mean-Variance, Minimum-Variance, Bayes Stein, Bayesian and Multi Prior
Summary
There is a body of literature on equity home bias that focuses on the fact that investors are found to hold disproportionately large share of their wealth in domestic portfolios as compared to predictions of standard portfolio theory. This paper develops measures of home bias for a sample 46 countries by employing various models i.e. ICAPM, Mean-Variance, Minimum-Variance, Bayes Stein, Bayesian and Multi Prior. They compare observed foreign asset holdings of 25 markets with optimal weights obtained from five benchmark models. They find that for many countries, home bias decreases sharply at the end of the 1990s, a development they link to time varying globalization and regional integration. Optimal portfolio weights are calculated by employing various methodologies including classical mean-variance, international capital asset pricing model, minimum variance portfolio, Bayes-Stein shrinkage portfolio model, Bayesian portfolio model and Multi-Prior portfolio model. The above home bias measure takes into account the case of overinvestment abroad (negative home bias)
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