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Evaluating Realized Volatility Models with Higher Order Cumulants: HAR-RV Versus ARIMA-RV

  • Sanja DudukovicEmail author
Conference paper
Part of the Eurasian Studies in Business and Economics book series (EBES, volume 10/2)

Abstract

The objective of this paper is to introduce a new Realized Volatility (RV) Model. The model solves the problems of capturing long memory and heavy tales, which persist in current Heterogeneous Auto Regressive Realized Volatility Models (HAR-RV). First, an extensive empirical analysis of the classical RV model is provided by coupling Digital Signal Processing (DSP), Non Gaussian Time Series Analyses (NG-TSA) and volatility forecasting concepts. All models are built and tested on 30 min quotations of closing spot prices: USD/JPY, CHF/USD, JPY/EUR USD/GBP and GBP/EUR for the period from May 14, 2013 to July 31, 2015, taken from Bloomberg. The independence of the model’s innovations is tested by using the second, third and fourth cumulants, known as Higher Order Cumulants (HOC).Two tests are used, the Box-Ljung (B-Lj) test and Hinich test. The model is compared with the more natural Autoregressive Moving Average model (ARMA-RV). The empirical analysis shows that neither classic HAR-RV nor ARMA-RV models produce independent residuals. In addition, DSP recent findings are used to build a new HOC-ARMA-RV model. It was shown that only HOC-ARMA model fully captures fat tails and the long memory of FX returns.

Keywords

Realized volatility HAR-RV model HOC-ARMA model Extended Box-Jenkins method Model testing Volatility forecasting 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.International Management DepartmentFranklin University SwitzerlandSorengoSwitzerland

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