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Model specification for volatility forecasting benchmark

Author

Listed:
  • Zhang, Yaojie
  • He, Mengxi
  • Wang, Yudong
  • Wen, Danyan

Abstract

The ideal model specification for asset price volatility forecasting is still an open question. From a variable transformation perspective, existing studies arbitrarily choose between the raw volatility measure, its square root form, or its natural logarithmic form. In this paper, both the in- and out-of-sample forecasting results support the effectiveness of variable transformation compared to the raw volatility variable. Notably, the logarithmic transformation shows overwhelming advantages. Our results hold across thirty global stock indices, five cryptocurrencies, a crude oil market, as well as a wide range of extensions and robustness checks. In statistics, we find the predictability sources that the logarithmic transformation can lead to more efficient regression estimators by mitigating the heteroscedasticity and serial correlation issues. Consequently, let's make a deal: the benchmark model of volatility forecasting should be based on the natural logarithmic form of the original volatility measure.

Suggested Citation

  • Zhang, Yaojie & He, Mengxi & Wang, Yudong & Wen, Danyan, 2025. "Model specification for volatility forecasting benchmark," International Review of Financial Analysis, Elsevier, vol. 97(C).
  • Handle: RePEc:eee:finana:v:97:y:2025:i:c:s1057521924007828
    DOI: 10.1016/j.irfa.2024.103850
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    More about this item

    Keywords

    Volatility forecasting; Benchmark model; Model specification; Variable transformation; Natural logarithm;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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