assets. Building on this idea, we propose the use of a highly flexible and tractable model to forecast the volatility of an …

volatility models results in superior out-of-sample risk forecasts, compared to forecasts from existing models and more …

Standard realized volatility (RV) measures estimate the latent volatility of an asset price using high frequency data … estimate of volatility to the application in which it will be used. For example, if the volatility measure will be used in a … volatility. We use methods from machine learning to estimate optimal “bespoke” RVs for heterogeneous autoregressive (HAR) and …

. Our results indicate that over the period 1900:1-2014:2 the time-varying VAR model with stochastic volatility outranks all …

Option-implied moments, like implied volatility, contain useful information about an underlying asset's return …

't forecast these hazards. Consequently, we witness a growing interest in coherent risk measures, sensitive to high moments and …

significantly higher utility for volatility managed portfolios. Superior forecast performance is especially pronounced for firms … forest. In contrast to conventional random forests that approximate the volatility nonparametrically using local averaging … forecasting performance of the HAR forest across multiple forecast horizons and across 186 S&P 500 constituents. This leads to …

This study investigates the predictability of a fixed uncertainty index (UI) for realized variances (volatility) in the …