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Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures
16 references·2 citing·29 second hop
Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures (2011)Forecasting the volatility of S&P deposit…A forecast comparison of volatility models: does anything beat a GARCH(1,1)? (2005)A forecast comparison of vola…Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach (2000)Estimation of tail-related ri…Evaluating Interval Forecasts (1998)Evaluating Interval ForecastsAnswering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts (1998)Answering the Skeptics: Yes, …Fractionally integrated generalized autoregressive conditional heteroskedasticity (1996)Fractionally integrated gener…Techniques for Verifying the Accuracy of Risk Measurement Models (1995)Techniques for Verifying the …Testing the null hypothesis of stationarity against the alternative of a unit root (1992)Testing the null hypothesis o…Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances (1992)Quasi-maximum likelihood esti…Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root: How Sure Are We That Economic Time Series Have a Unit Root? (1991)Testing the Null Hypothesis o…Conditional Heteroskedasticity in Asset Returns: A New Approach (1991)Conditional Heteroskedasticit…Testing for a Unit Root in Time Series Regression (1988)Testing for a Unit Root in Ti…Modelling the persistence of conditional variances (1986)Modelling the persistence of …Generalized autoregressive conditional heteroskedasticity (1986)Generalized autoregressive co…Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Autoregressive Conditional He…The Extreme Value Method for Estimating the Variance of the Rate of Return (1980)The Extreme Value Method for …On the Estimation of Security Price Volatilities from Historical Data (1980)On the Estimation of Security…On the predictive power of ARJI volatility forecasts for Bitcoin (2019)On the predictive power of AR…Structural changes and out-of-sample prediction of realized range-based variance in the stock market (2017)Structural changes and out-of…Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Distribution of the Estimators for Autoregressive Time Series With a Unit Root (1979)A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test (1999)Unit root tests for panel data (2001)Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework (2014)Comparing Predictive Accuracy (1994)CAViaR (2004)CoVaR (2016)ARCH modeling in finance (1992)On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks (1993)Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles (2000)Time Series Analysis: Forecasting and Control (1977)Time Series Analysis: Forecasting and Control (1977)Multivariate Simultaneous Generalized ARCH (1995)The Behavior of Stock-Market Prices (1965)Modeling and Forecasting Realized Volatility (2003)The Cross‐Section of Volatility and Expected Returns (2006)Introductory Econometrics for Finance (2008)Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root (1981)Trends and random walks in macroeconmic time series (1982)Unit root tests in panel data: asymptotic and finite-sample properties (2002)A simple panel unit root test in the presence of cross‐section dependence (2007)Multivariate GARCH models: a surveyThe distribution of realized stock return volatility (2001)Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility (2007)Econometric Analysis of Cross Section and Panel Data (2001)Global financial crisis, extreme interdependences, and contagion effects: The role of economic structure? (2010)Systemic Risk Measurement: Multivariate GARCH Estimation of CoVaR (2012)Financial Network Systemic Risk Contributions (2014)
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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
  • 1988Testing for a Unit Root in Time Series Regression19,288 cited
  • 1992Testing the null hypothesis of stationarity against the alternative of a unit root12,634 cited
  • 1991Conditional Heteroskedasticity in Asset Returns: A New Approach10,268 cited
  • 1998Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts3,380 cited
  • 1992Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances3,220 cited
  • 1998Evaluating Interval Forecasts2,689 cited
  • 1995Techniques for Verifying the Accuracy of Risk Measurement Models2,447 cited
  • 1996Fractionally integrated generalized autoregressive conditional heteroskedasticity2,290 cited
  • 1986Modelling the persistence of conditional variances2,068 cited
  • 1980The Extreme Value Method for Estimating the Variance of the Rate of Return1,946 cited
  • 2005A forecast comparison of volatility models: does anything beat a GARCH(1,1)?1,783 cited
  • 2000Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach1,732 cited

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  • 1991Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root: How Sure Are We That Economic Time Series Have a Unit Root?1,589 cited
  • 1980On the Estimation of Security Price Volatilities from Historical Data1,538 cited
  • Cited by

    • 2019On the predictive power of ARJI volatility forecasts for Bitcoin25 cited
    • 2017Structural changes and out-of-sample prediction of realized range-based variance in the stock market20 cited