A Simple Approximate Long-Memory Model of Realized Volatility
A Simple Approximate Long-Memory Model of Realized Volatility
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DOI：
10.1093/jjfinec/nbp001
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发表时间：
2009-03-01
影响因子：
2.5
通讯作者：
Corsi, Fulvio
中科院分区：
文献类型：
--
作者：
Corsi, Fulvio
本文提出了一个由不同时间段的波动分量组成的加性级联模型。这种波动级联导致一个简单的ar型已实现波动模型，该模型考虑了在不同时间范围内实现的不同波动分量，因此称为已实现波动率的异构自回归模型（HAR-RV）。尽管HAR-RV模型结构简单，缺乏真正的长记忆特性，但仿真结果表明，HAR-RV模型以一种非常易于处理和简约的方式成功地再现了金融回报的主要经验特征（长记忆、肥尾和自相似性）。实证结果表明，该方法具有较好的预测效果。
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.