Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Capital flow volatility is a concern for macroeconomic and financial stability. Nonetheless, literature is scarce in this topic. Our paper sheds light on this issue along two dimensions. First, using quarterly data for 33 emerging markets and developing economies, we introduce new estimates of volatility for total multilateral gross capital in- and outflows and key categories, based on the residuals of ARIMA models. We find that a combination of our proposed approach and the commonly used standard deviation best identifies sharp rises during episodes of heightened global risk aversion, thus underscoring the need for a multi-faceted approach to gauge capital flow volatility. Second, we perform panel regressions to understand the determinants of volatility using both ARIMA and standard deviation measures of volatility. While there are variations across different categories of capital flows, generally speaking we identify three main drivers: the US interest rates, global risk aversion, and domestic real GDP. Overall, our findings call for a richer set of volatility estimates, beyond standard deviation, and also show that the determinants of capital flow volatility could vary depending on the measurement approach and the category of flow under analysis.