I examine the sensitivity of scoring rules for distribution forecasts in two dimensions: sensitivity to linear rescaling of the data and the influence of measurement error on the forecast evaluation outcome. First, I show that all commonly used …
We study the relation between a comprehensive set of firm characteristics and the entire universe of individual equity option prices. We find that 42 out of 86 characteristics are priced in the option market, in the sense that they significantly …
The highfrequency package for the R programming language provides functionality for pre-processing financial high-frequency data, analyzing intraday stock returns, and forecasting stock market volatility. For academics and practitioners alike, it …
We propose a heterogeneous autoregressive (HAR) model with time-varying parameters in the form of a local linear random forest. In contrast to conventional random forests that approximate the volatility nonparametrically using local averaging, the …
Low-volatility investing is typically implemented by sorting stocks based on simple risk measures; for example, the empirical standard deviation of last year's daily returns. In contrast, we understand identifying next-month's ranking of volatilities …
We examine the properties and forecast performance of multiplicative volatility specifications that belong to the class of GARCH-MIDAS models suggested in Engle et al. (2013). In those models volatility is decomposed into a short-term GARCH component …