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A model-free approach to do long-term volatility forecasting and its variants.

Kejin Wu1, Sayar Karmakar2

  • 1Department of Mathematics, University of California San Diego, La Jolla, USA.

Financial Innovation
|March 6, 2023
PubMed
Summary

The new Normalizing and Variance Stabilizing (NoVaS) method offers superior long-term volatility forecasting, especially for challenging short and volatile financial datasets. A refined NoVaS variant further enhances prediction accuracy over standard GARCH models.

Keywords:
ARCH-GARCHAggregated forecastingModel free

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Area of Science:

  • Financial econometrics
  • Time series analysis
  • Statistical modeling

Background:

  • Volatility forecasting is crucial in financial econometrics, primarily relying on Generalized Autoregressive Conditional Heteroskedasticity (GARCH)-type models.
  • Traditional GARCH models exhibit limitations, including instability with highly volatile or short datasets and difficulty in uniform cross-dataset application.
  • Existing methods struggle with the complexities of financial market volatility, necessitating more robust prediction techniques.

Purpose of the Study:

  • To evaluate the effectiveness of the Normalizing and Variance Stabilizing (NoVaS) method for long-term volatility forecasting compared to standard GARCH models.
  • To investigate the performance of NoVaS, particularly on short and highly volatile financial datasets.
  • To introduce and assess a novel variant of the NoVaS method designed for improved forecasting accuracy.

Main Methods:

  • Extensive empirical and simulation analyses were conducted to compare forecasting performance.
  • The study utilized the model-free NoVaS method, originally derived from an ARCH model inverse transformation.
  • A new, more complete variant of the NoVaS method was developed and tested.

Main Results:

  • The NoVaS method demonstrated higher-quality long-term volatility forecasting than standard GARCH models.
  • This advantage was particularly pronounced for datasets characterized by high volatility and short time series.
  • The proposed NoVaS variant generally outperformed the existing state-of-the-art NoVaS method.

Conclusions:

  • NoVaS-type methods exhibit uniformly superior performance, supporting their widespread adoption in volatility forecasting.
  • The flexibility of the NoVaS approach allows for further development and application in addressing specific prediction challenges.
  • The study validates NoVaS as a robust and accurate alternative for financial volatility prediction, especially in difficult data conditions.