Exploiting the errors: A simple approach for improved volatility forecastingTim Bollerslev, Andrew J. Patton, Rogier Quaedvlieg2015-11-10articleJournal of Econometrics448 citationsgreenNo readable copy stored yet — this record is metadata only.Financial Risk and Volatility ModelingStochastic processes and financial applicationsForecasting Techniques and ApplicationsAbstractNo abstract was published for this record.Citations in this libraryStored only when both papers are in the corpus — 448 citations exist upstream.References2003Modeling and Forecasting Realized Volatility1998Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts2008A Simple Approximate Long-Memory Model of Realized Volatility2002Econometric Analysis of Realized Volatility and its Use in Estimating Stochastic Volatility Models2004Power and Bipower Variation with Stochastic Volatility and Jumps2007Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility2003A Tale of Two Time Scales: Determining Integrated Volatility with Noisy High Frequency Data2005Econometrics of Testing for Jumps in Financial Economics Using Bipower VariationCited by2023The impact of the Russian-Ukrainian war on global financial markets2019The impact of sentiment and attention measures on stock market volatility2021The effects of a “black swan” event (COVID-19) on herding behavior in cryptocurrency markets2018The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market2017Forecasting the realized volatility of the oil futures market: A regime switching approach2022A Machine Learning Approach to Volatility Forecasting2017Structural breaks and volatility forecasting in the copper futures market2014REALIZED BETA GARCH: A MULTIVARIATE GARCH MODEL WITH REALIZED MEASURES OF VOLATILITY