Abstract
Volatility estimation and prediction are important topics in financial research and practices. There were extensive volatility studies based on low-frequency data in the past three decades, and in the past several years intensive research activities were on the volatility estimation based on high-frequency financial data. One recent trend in the volatility research is to combine the strengths of both low-frequency and high-frequency methods. An empirical approach is to directly estimate volatilities based on high-frequency data and then fit the estimated volatilities to a low-frequency heterogeneous autoregressive volatility model. This chapter is to provide some theoretical justifications for the empirical approach by showing that realized volatility estimators approximately obey a heterogeneous autoregressive model for some appropriate underlying price and volatility processes.
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Acknowledgment
Yazhen Wang’s research was partially supported by the NSF grant DMS-105635.
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Wang, Y., Zhang, X. (2013). Heterogenous Autoregressive Realized Volatility Model. In: Zeng, Y., Wu, S. (eds) State-Space Models. Statistics and Econometrics for Finance, vol 1. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7789-1_14
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DOI: https://doi.org/10.1007/978-1-4614-7789-1_14
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