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High-low range in GARCH models of stock return volatility
16 references·13 citing·30 second hop
High-low range in GARCH models of stock return volatility (2016)High-low range in GARCH models of stock r…Dynamic spillovers between oil and stock markets in the Gulf Cooperation Council Countries (2012)Dynamic spillovers between oi…Properties of range-based volatility estimators (2011)Properties of range-based vol…Volatility forecast comparison using imperfect volatility proxies (2010)Volatility forecast compariso…Realising the future: forecasting with high‐frequency‐based volatility (HEAVY) models (2010)Realising the future: forecas…Volatility Forecasting With Range-Based EGARCH Models (2006)Volatility Forecasting With R…Journal of Applied Econometrics (2006)Journal of Applied Econometri…Chapter 15 Volatility and Correlation Forecasting (2006)Chapter 15 Volatility and Cor…Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model (2005)Forecasting Financial Volatil…Consistent ranking of volatility models (2005)Consistent ranking of volatil…Forecasting Volatility in Financial Markets: A Review (2003)Forecasting Volatility in Fin…Range‐Based Estimation of Stochastic Volatility Models (2002)Range‐Based Estimation of Sto…Comparing Predictive Accuracy (1995)Comparing Predictive AccuracyOn the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks (1993)On the Relation between the E…Conditional Heteroskedasticity in Asset Returns: A New Approach (1991)Conditional Heteroskedasticit…Generalized autoregressive conditional heteroskedasticity (1986)Generalized autoregressive co…Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Autoregressive Conditional He…Robust estimation of the range-based GARCH model: Forecasting volatility, value at risk and expected shortfall of cryptocurrencies (2024)Robust estimation of the rang…Modeling and forecasting dynamic conditional correlations with opening, high, low, and closing prices (2022)Modeling and forecasting dyna…Large dynamic covariance matrices: Enhancements based on intraday data (2022)Large dynamic covariance matr…Forecasting volatility during the outbreak of Russian invasion of Ukraine: Application to commodities, stock indices, currencies, and cryptocurrencies (2022)Forecasting volatility during…Stock market volatility forecasting: Do we need high-frequency data? 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References

  • 1986Generalized autoregressive conditional heteroskedasticity22,329 cited
  • 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation20,684 cited
  • 1991Conditional Heteroskedasticity in Asset Returns: A New Approach10,268 cited
  • 1993On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks9,041 cited
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  • 2010Volatility forecast comparison using imperfect volatility proxies1,281 cited
  • 2002Range‐Based Estimation of Stochastic Volatility Models1,215 cited
  • 2006Journal of Applied Econometrics979 cited
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  • 2010Realising the future: forecasting with high‐frequency‐based volatility (HEAVY) models436 cited
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  • 2005Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model366 cited
  • 2012Dynamic spillovers between oil and stock markets in the Gulf Cooperation Council Countries347 cited
  • 2006Chapter 15 Volatility and Correlation Forecasting330 cited
  • 2006Volatility Forecasting With Range-Based EGARCH Models

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252 cited
  • 2011Properties of range-based volatility estimators136 cited
  • Cited by

    • 2020Understanding risk of bubbles in cryptocurrencies129 cited
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    • 2019Range-Based DCC Models for Covariance and Value-at-Risk Forecasting59 cited
    • 2016Forecasting risk via realized GARCH, incorporating the realized range49 cited
    • 2020Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators40 cited
    • 2022Large dynamic covariance matrices: Enhancements based on intraday data39 cited
    • 2020Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression36 cited
    • 2022Forecasting volatility during the outbreak of Russian invasion of Ukraine: Application to commodities, stock indices, currencies, and cryptocurrencies35 cited
    • 2019Improving forecasts with the co-range dynamic conditional correlation model26 cited
    • 2024Robust estimation of the range-based GARCH model: Forecasting volatility, value at risk and expected shortfall of cryptocurrencies24 cited
    • 2022Modeling and forecasting dynamic conditional correlations with opening, high, low, and closing prices21 cited
    • 2021Covariance matrix forecasting using support vector regression20 cited