In the piece, Ronald and co-authors explore how to predict stock market volatility based on estimates obtained from investor surveys.

One of the most common models for forecasting in conditions of instability (volatility) is GARCH (Generalized Autoregressive Conditional Heteroscedastic model). It assumes that the current changes in variance are affected by both previous values of indicators and preliminary estimates of variance. 

Ronald Huisman and colleagues in the article prove that with his method you can get predictions comparable to GARCH in quality. They calculate expected volatility using aggregated investor estimates of where the market will go. According to the authors, their method can be useful in cases where market data are not available, so it is impossible to predict volatility using models like GARCH.

Congratulations to Ronald Huisman on the publication!

And all interested are invited to read it here.