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ArticleOptimal combinations of realised volatility estimators

Andrew J. Patton2009

Article2009

Optimal combinations of realised volatility estimators

Andrew J. Patton · Kevin Keith Sheppard

142 citations·0/0 files stored·International Journal of Forecasting

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Financial Risk and Volatility Modeling Financial Markets and Investment Strategies Stochastic processes and financial applications

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References

1994Comparing Predictive Accuracy 1995Comparing Predictive Accuracy 1987Expected stock returns and volatility 2003Modeling and Forecasting Realized Volatility 1998Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts 1969The Combination of Forecasts 1980On estimating the expected return on the market 2001The distribution of realized stock return volatility

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2010Data-based ranking of realised volatility estimatorsarticle · 31 shared references 2010Realized volatility forecasting and market microstructure noisearticle · 19 shared references 2009Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcementsarticle · 19 shared references 2010Parametric and Nonparametric Volatility Measurementchapter · 18 shared references 2009Comparison of Volatility Measures: a Risk Management Perspectivearticle · 18 shared references 2005Variation, Jumps, Market Frictions and High Frequency Data in Financial Econometricspreprint · 18 shared references

Details

Year2009

Typearticle

Published2009-03-02

VenueInternational Journal of Forecasting

ISSN-L0169-2070

Languageen

Citations142

Files0 public / 0

ShelfFinancial Risk and Volatility Modeling

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doi10.1016/j.ijforecast.2009.01.011

openalex_workW2069601141

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2012

A Comprehensive Look at Financial Volatility Prediction by Economic Variables

2021Stock market volatility forecasting: Do we need high-frequency data?