Modeling dynamic volatility under uncertain environment with fuzziness and randomness

TL;DR

This paper introduces a generalized BN-S model incorporating fuzziness and randomness to better predict dynamic volatility in uncertain financial environments, addressing delays and long-range dependence issues.

Contribution

A novel generalized BN-S model that accounts for fuzziness, randomness, and delay phenomena, improving volatility prediction accuracy in uncertain markets.

Findings

The new model outperforms classical models in Dow Jones futures prediction.

It effectively captures environmental uncertainty and delay effects.

Enhanced long-range dependence modeling in volatility prediction.

Abstract

The problem related to predicting dynamic volatility in financial market plays a crucial role in many contexts. We build a new generalized Barndorff-Nielsen and Shephard (BN-S) model suitable for uncertain environment with fuzziness and randomness. This new model considers the delay phenomenon between price fluctuation and volatility changes, solves the problem of the lack of long-range dependence of classic models. Through the experiment of Dow Jones futures price, we find that compared with the classical model, this method effectively combines the uncertain environmental characteristics, which makes the prediction of dynamic volatility has more ideal performance.