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On forecasting daily stock volatility: The role of intraday information and market conditions

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References

1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation20,684 cited
1994Comparing Predictive Accuracy5,495 cited
1995Comparing Predictive Accuracy5,214 cited
1987The Pricing of Options on Assets with Stochastic Volatilities3,864 cited
1998Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts3,380 cited
1988A Theory of Intraday Patterns: Volume and Price Variability3,120 cited
1973A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices2,781 cited
1984A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market2,748 cited
2001The distribution of realized stock return volatility2,264 cited
2002Econometric Analysis of Realized Volatility and its Use in Estimating Stochastic Volatility Models2,224 cited
2001The Distribution of Realized Exchange Rate Volatility2,108 cited
2004Power and Bipower Variation with Stochastic Volatility and Jumps2,033 cited
1980The Extreme Value Method for Estimating the Variance of the Rate of Return1,946 cited
1995Gaussian Semiparametric Estimation of Long Range Dependence1,476 cited
1983The Price Variability-Volume Relationship on Speculative Markets

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1,450 cited

2007Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility1,448 cited

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