This paper revisits the relative importance of global versus country-specific factors underlying stock returns. It constructs a new firm level data set covering emerging and developed markets and estimates a simple factor model, which breaks down stock returns into a global business cycle...

A stochastic volatility model where volatility was driven solely by a latent variable called news was estimated for three stock indices. A Markov chain Monte Carlo algorithm was used for estimating Bayesian parameters and filtering volatilities. Volatility persistence being close to one was...

We explore the link between international stock market comovement and the degree to which firms operate globally. Using stock returns and balance sheet data for companies in 20 countries, we estimate a factor model that decomposes stock returns into global, country-specific and industry-specific...

We investigate the relative importance of country and industry effects in international stock returns, with the innovation that we decompose country effects into region and within-region country effects. We divide the global stock market into the Americas, Asia, and Europe and find that most of...

We estimate a latent factor model that decomposes international stock returns into global, country-, and industry-specific shocks and allows for stock-specific exposures to these shocks. We find that across stocks there is substantial dispersion in these exposures, which is partly explained by...

While SWF investment objectives to some extent reflect inherent characteristics, notable differences in strategic asset allocation (SAA) exist even amongst SWFs of similar types. Even so, this paper shows that the global crisis may have changed SWF’s asset allocations in ways that may not be...

In most macroeconomic models, the substitutability between domestic and foreign goods is calibrated using aggregated data. This imposes homogeneous elasticities across goods, and the calibration is only valid under this assumption. If elasticities are heterogeneous, the aggregate...