Forecasting realised volatility using ARFIMA and HAR models

Marwan Izzeldin, M. Kabir Hassan, Vasileios Pappas and Mike Tsionas

Quantitative Finance, 2019, vol. 19, issue 10, 1627-1638

Abstract: Recent literature provides mixed empirical evidence with respect to the forecasting performance of ARFIMA and HAR models. This paper compares the forecasting performance of both models using high frequency data of 100 stocks representing 10 business sectors for the period 2000-2010. We allow for different sectors, changing market conditions, variation in the sampling frequency and forecasting horizons. For the overall sample and using the 300 sec sampling frequency, the forecasting performance of both models is indistinguishable. However, differences arise under different market regimes, forecasting horizons and sampling frequencies. ARFIMA models are superior for the crisis and pre-crisis sub-samples. HAR forecasts are less sensitive to regime change and to longer forecasting horizons. Variations in forecasting performance could also be explained using differences in the levels of persistence underlying each model.

Date: 2019
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Citations: View citations in EconPapers (7)

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DOI: 10.1080/14697688.2019.1600713

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