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Forecasting implied volatility risk indexes: International evidence using Hammerstein-ARX approach

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  • Tissaoui, Kais

Abstract

This paper investigates the predictive ability of the Unites States (US) volatility risk index toward the European and Asian volatility risk indexes, and vice versa. We use the Hammerstein-ARX approach to model dependency between the different volatility risk indexes. The unknown parameters of the non-linear Hammerstein-ARX model are estimated using particle swarm optimization (PSO), in order to minimize the error between the real output and the forecasted output. Our empirical findings provide that the US implied volatility risk index is particularly powerful in forecasting the European and Asian volatility risk indexes than in the opposite case. We also show that the US implied volatility risk index react to other international implied volatility risk indexes linearly and non-linearly, and vice versa. The simulation results confirm the fitness and performance of the proposed PSO identification tool.

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  • Tissaoui, Kais, 2019. "Forecasting implied volatility risk indexes: International evidence using Hammerstein-ARX approach," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 232-249.
    • Handle: RePEc:eee:finana:v:64:y:2019:i:c:p:232-249
    DOI: 10.1016/j.irfa.2019.06.001
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    Cited by:

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    3. Junyu Zhang & Xinfeng Ruan & Jin E. Zhang, 2023. "Risk‐neutral moments and return predictability: International evidence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1086-1111, August.
    4. Zied Ftiti & Kais Tissaoui & Sahbi Boubaker, 2022. "On the relationship between oil and gas markets: a new forecasting framework based on a machine learning approach," Annals of Operations Research, Springer, vol. 313(2), pages 915-943, June.
    5. Tissaoui, Kais & Zaghdoudi, Taha, 2021. "Dynamic connectedness between the U.S. financial market and Euro-Asian financial markets: Testing transmission of uncertainty through spatial regressions models," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 481-492.
    6. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Khalid Mehmood Cheema & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdullah Ahmed Azhari, 2022. "Nonlinear Hammerstein System Identification: A Novel Application of Marine Predator Optimization Using the Key Term Separation Technique," Mathematics, MDPI, vol. 10(22), pages 1-22, November.
    7. Kais Tissaoui & Taha Zaghdoudi & Abdelaziz Hakimi & Mariem Nsaibi, 2023. "Do Gas Price and Uncertainty Indices Forecast Crude Oil Prices? Fresh Evidence Through XGBoost Modeling," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 663-687, August.
    8. Kais Tissaoui & Taha Zaghdoudi & Abdelaziz Hakimi & Ousama Ben-Salha & Lamia Ben Amor, 2022. "Does Uncertainty Forecast Crude Oil Volatility before and during the COVID-19 Outbreak? Fresh Evidence Using Machine Learning Models," Energies, MDPI, vol. 15(15), pages 1-20, August.

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    More about this item

    Keywords

    Volatility risk indexes; Forecasting; Spreading risk; Particle swarm optimization; Hammerstein-ARX; Long-memory behavior;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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