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Hedging performance of multiscale hedge ratios

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  • Jahangir Sultan
  • Antonios K. Alexandridis
  • Mohammad Hasan
  • Xuxi Guo

Abstract

In this study, the wavelet multiscale model is applied to selected assets to hedge time‐dependent exposure of an agent with a preference for a certain hedging horizon. Based on the in‐sample and out‐of‐sample portfolio variances, the wavelet‐based generalized autoregressive conditional heteroskedasticity (GARCH) model produces the lowest variances. From a utility standpoint, wavelet networks combined with GARCH have the highest utility. Finally, the wavelet‐GARCH model has the lowest minimum capital risk requirements. Overall, the wavelet GARCH and wavelet networks offer improvements over traditional hedging models.

Suggested Citation

  • Jahangir Sultan & Antonios K. Alexandridis & Mohammad Hasan & Xuxi Guo, 2019. "Hedging performance of multiscale hedge ratios," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1613-1632, December.
    • Handle: RePEc:wly:jfutmk:v:39:y:2019:i:12:p:1613-1632
    DOI: 10.1002/fut.22047
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    References listed on IDEAS

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    Cited by:

    1. Xingyu Dai & Dongna Zhang & Chi Keung Marco Lau & Qunwei Wang, 2023. "Multiobjective portfolio optimization: Forecasting and evaluation under investment horizon heterogeneity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2167-2196, December.
    2. Hsiu-Chuan Lee & Donald Lien & Her-Jiun Sheu, 2023. "Hedging performance of volatility index futures: a partial cointegration approach," Review of Quantitative Finance and Accounting, Springer, vol. 61(1), pages 265-294, July.
    3. Wang Haoyu & Junpeng Di & Qing Han, 2023. "Adaptive hedging horizon and hedging performance estimation," Papers 2302.00251, arXiv.org.

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