Prediction of linear fractional stable motions using codifference, with application to non-Gaussian rough volatility

TL;DR

This paper introduces a novel forecasting method for linear fractional stable motions (LFSM) using codifference, effectively handling non-Gaussian rough volatility data and outperforming traditional models in real-world applications.

Contribution

It develops a new prediction approach based on codifference for LFSM, extending forecasting techniques beyond Gaussian assumptions and demonstrating practical effectiveness.

Findings

The method accurately forecasts LFSM increments in simulations.

Application to real volatility data shows improved prediction over existing models.

Analysis reveals a new serial dependence regime in fractional processes.

Abstract

The linear fractional stable motion (LFSM) extends the fractional Brownian motion (fBm) by considering $\alpha$-stable increments. We propose a method to forecast future increments of the LFSM from past discrete-time observations, using the conditional expectation when $\alpha>1$ or a semimetric projection otherwise. It relies on the codifference, which describes the serial dependence of the process, instead of the covariance. Indeed, covariance is commonly used for predicting an fBm but it is infinite when $\alpha<2$. Some theoretical properties of the method and of its accuracy are studied and both a simulation study and an application to real volatility data, with a comparison to the fBm and to the heterogeneous auto-regressive model, confirm the relevance of the approach. The LFSM-based method shows a promising performance in the forecast of time series of volatilities, decomposing properly, in the fractal dynamic of rough volatilities, the contribution of the kurtosis of the increments and the contribution of their serial dependence. Moreover, the analysis of hit ratios suggests that, beside independence, persistence, and antipersistence, a fourth regime of serial dependence exists for fractional processes, characterized by a selective memory controlled by a few large increments.