A moving average heterogeneous autoregressive model for forecasting the realized volatility of the US stock market: Evidence from over a century of data

Afees Salisu, Rangan Gupta and Ahamuefula Ogbonna

International Journal of Finance & Economics, 2022, vol. 27, issue 1, 384-400

Abstract: This study forecasts the monthly realized volatility of the US stock market covering the period of February 1885 to September 2019 using a recently developed novel approach – a moving average heterogeneous autoregressive (MAT‐HAR) model, which treats threshold as a moving average generated time‐varying parameter rather than as a fixed or unknown parameter. The significance of asymmetric information in realized volatility of stock market forecasting is also considered by examining the case of good and bad realized volatility. The Clark and West, Journal of Econometrics, 2007, 138, 291–311 forecast evaluation approach is employed to evaluate the forecast performance of the proposed predictive model vis‐à‐vis the conventional HAR and threshold HAR (T‐HAR) models. We find evidence in favour of the MAT‐HAR model relative to the HAR and T‐HAR models. Also observed is the significant role of asymmetry in modelling the realized volatility as good realized volatility and bad realized volatility yield dissimilar predictability results. Our results are not sensitive to the choice of sample periods and realized volatility measures.

Date: 2022
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Citations: View citations in EconPapers (6)

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https://doi.org/10.1002/ijfe.2158

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Working Paper: A Moving Average Heterogeneous Autoregressive Model for Forecasting the Realized Volatility of the US Stock Market: Evidence from Over a Century of Data (2019)
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