Using High Frequency Data to Calculate, Model and Forecast Realized Volatility
AbstractThe objective of this paper is to calculate, model, and forecast realized volatility using high-frequency stock-market index data. The approach differs from existing ones in several ways. First, it is shown that the decay of the serial dependence of high-frequency returns on the sampling frequency is consistent with an ARMA process under temporal aggregation. This is important in modelling high-frequency returns and chosing the optimal sampling frequency when calculating realized volatility. Second, as a result of several test statistics for long memory in realized volatility, it is found that the realized volatility series can be modelled as an ARFIMA process. The ARFIMA's forecasting performance is assessed in a simulation study, and, although it outperforms representative GARCH models, it does so with greater complexity and data intensiveness that may not be worthwhile relative to GARCH's simplicity and flexibility.
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2001 with number 75.
Date of creation: 01 Apr 2001
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Web page: http://www.econometricsociety.org/conference/SCE2001/SCE2001.html
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High Frequency Data; Long Memory; GARCH; Realized Volatility;
Find related papers by JEL classification:
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
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