Abstract
In this chapter, we estimate, model, and forecast Realized Range Volatility, a realized measure and estimator of the quadratic variation of financial prices. This quantity was early introduced in the literature and it is based on the high-low range observed at high frequency during the day. We consider the impact of the microstructure noise in high frequency data and correct our estimations, following a known procedure. Then, we model the Realized Range accounting for the well-known stylized effects present in financial data. We consider an HAR model with asymmetric effects with respect to the volatility and the return, and GARCH and GJR-GARCH specifications for the variance equation. Moreover, we consider a non-Gaussian distribution for the innovations. The analysis of the forecast performance during the different periods suggests that the introduction of asymmetric effects with respect to the returns and the volatility in the HAR model results in a significant improvement in the point forecasting accuracy.
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- 1.
In this version, we only present the results based on the MSE loss function. Similar results are obtained with the other loss function.
- 2.
Results for the test between model I and II are not presented. As expected, HAR models perform significantly better than AR(1) model.
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Acknowledgements
The authors wish to thank the participants to the Italian Statistical Society XLV Conference held in Padova in June 2010 for their helpful comments and suggestions.
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Caporin, M., Velo, G.G. (2013). Modeling and Forecasting Realized Range Volatility. In: Torelli, N., Pesarin, F., Bar-Hen, A. (eds) Advances in Theoretical and Applied Statistics. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35588-2_42
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DOI: https://doi.org/10.1007/978-3-642-35588-2_42
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