Journal
JOURNAL OF FINANCIAL ECONOMETRICS
Volume 7, Issue 2, Pages 174-196Publisher
OXFORD UNIV PRESS
DOI: 10.1093/jjfinec/nbp001
Keywords
C13; C22; C51; C53; high-frequency data; long-memory models; realized volatility; volatility forecast
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The paper proposes an additive cascade model of volatility components defined over different time periods. This volatility cascade leads to a simple AR-type model in the realized volatility with the feature of considering different volatility components realized over different time horizons and thus termed Heterogeneous Autoregressive model of Realized Volatility (HAR-RV). In spite of the simplicity of its structure and the absence of true long-memory properties, simulation results show that the HAR-RV model successfully achieves the purpose of reproducing the main empirical features of financial returns (long memory, fat tails, and self-similarity) in a very tractable and parsimonious way. Moreover, empirical results show remarkably good forecasting performance.
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