The present paper analyzes the forecastability and tradability of volatility on the large S&P500 index and the liquid SPY ETF, VIX index and VXX ETN. Even though there is already a huge array of literature on forecasting high frequency volatility, most publications only evaluate the forecast in...

The predictability of a high-dimensional time series model in forecasting with large information sets depends not only on the stability of parameters but also depends heavily on the active covariates in the model. Since the true empirical environment can change as time goes by, the variables...

This paper develops alternative text-based indexes assessing human sentiment and economic uncertainty in the oil market. The text analysis includes the titles and full articles of 138,797 oil related news items which featured in The Financial Times, Thompson-Reuters and The Independent from...

We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...

We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...

We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...

This paper introduces a general class of combined neural network-GARCH models suitable to financial time series analysis. We put special emphasis on designing a full model-building cycle for this class of models that includes all stages of econometric modelling (specification, estimation and...

Four model selection methods are applied to the problem of predicting business cycle turning points: equally-weighted forecasts, Bayesian model averaged forecasts, and two models produced by the machine learning algorithm boosting. The model selection algorithms condition on different economic...