A codifference-based predictor for linear fractional stable motion increments is developed and shown to improve volatility forecasts by separating kurtosis effects from serial dependence.
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Prediction of linear fractional stable motions using codifference, with application to non-Gaussian rough volatility
A codifference-based predictor for linear fractional stable motion increments is developed and shown to improve volatility forecasts by separating kurtosis effects from serial dependence.