Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Quantitative Finance on 24/04/2019, available online: https://www.tandfonline.com/doi/full/10.1080/14697688.2019.1600713
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Forecasting Realised Volatility Using ARFIMA and HAR Models
AU - Izzeldin, Marwan
AU - Hassan, M. Kabir
AU - Pappas, Vasileios
AU - Tsionas, Mike
PY - 2019/10/1
Y1 - 2019/10/1
N2 - 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.
AB - 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.
KW - High-frequency data
KW - Market conditions
KW - Market sectors
KW - Realised variance
KW - HAR
KW - ARFIMA
U2 - 10.1080/14697688.2019.1600713
DO - 10.1080/14697688.2019.1600713
M3 - Journal article
VL - 19
SP - 1627
EP - 1638
JO - Quantitative Finance
JF - Quantitative Finance
SN - 1469-7688
IS - 10
ER -