Do high-frequency measures of volatility improve forecasts of return distributions?John M. Maheu, Thomas H. McCurdy2010-03-09articleJournal of Econometrics147 citationsgreenNo readable copy stored yet — this record is metadata only.Financial Risk and Volatility ModelingFinancial Markets and Investment StrategiesStochastic processes and financial applicationsAbstractNo abstract was published for this record.Citations in this libraryStored only when both papers are in the corpus — 147 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 Volatility2001The distribution of realized stock return volatility2002Econometric Analysis of Realized Volatility and its Use in Estimating Stochastic Volatility Models2001The Distribution of Realized Exchange Rate Volatility2004Power and Bipower Variation with Stochastic Volatility and Jumps2007Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return VolatilityCited by2011Impact of macroeconomic news on metal futures2015Does data frequency matter for the impact of forward premium on spot exchange rate?2014Do Momentum‐Based Trading Strategies Work in the Commodity Futures Markets?2020Impact of macroeconomic news, regulation and hacking exchange markets on the volatility of bitcoin2012Realizing smiles: Options pricing with realized volatility2015Intraday volatility interaction between the crude oil and equity markets2013Financial Risk Measurement for Financial Risk Management2012Modeling Realized Covariances and Returns