A Simple Approximate Long-Memory Model of Realized Volatility
Posted: 23 Mar 2009
Date Written: Spring 2009
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.
Keywords: C13, C22, C51, C53, high-frequency data, long-memory models, realized volatility, volatility forecast
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