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A generalized heterogeneous autoregressive model using market information

Author

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  • Rodrigo Hizmeri
  • Marwan Izzeldin
  • Ingmar Nolte
  • Vasileios Pappas

Abstract

This paper introduces a novel class of volatility forecasting models that incorporate market realized (co)variances and semi(co)variances within the framework of a heterogeneous autoregressive (HAR) model. Our empirical analysis shows statistically and economically significant forecasting gains. For our most parsimonious market-HAR specification, stock volatility forecasting is improved by 9.80% points. Using a mixed sampling frequency market-HAR variant with low (high) sampling frequency for the stock (market) improves forecasting by a further 6.90% points. Our paper also develops noise-robust estimators to facilitate the use of realized semi(co)variances at high sampling frequencies.

Suggested Citation

  • Rodrigo Hizmeri & Marwan Izzeldin & Ingmar Nolte & Vasileios Pappas, 2022. "A generalized heterogeneous autoregressive model using market information," Quantitative Finance, Taylor & Francis Journals, vol. 22(8), pages 1513-1534, August.
    • Handle: RePEc:taf:quantf:v:22:y:2022:i:8:p:1513-1534
    DOI: 10.1080/14697688.2022.2076606
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    Cited by:

    1. Li, Zhao-Chen & Xie, Chi & Zeng, Zhi-Jian & Wang, Gang-Jin & Zhang, Ting, 2023. "Forecasting global stock market volatilities in an uncertain world," International Review of Financial Analysis, Elsevier, vol. 85(C).

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