Stylized Facts and Simulating Long Range Financial Data

TL;DR

This paper introduces an R-based simulation method that accurately reproduces stylized facts and long-range dependencies in daily stock-price data, outperforming traditional parametric models like GARCH.

Contribution

The paper presents a novel simulation approach that better captures stylized facts and variance dynamics in financial data than existing models.

Findings

Simulated data matches empirical stylized facts more closely.

The method accurately reflects changes in unconditional variance.

Outperforms GARCH-family models in reproducing long-range dependencies.

Abstract

We propose a new method (implemented in an R-program) to simulate long-range daily stock-price data. The program reproduces various stylized facts much better than various parametric models from the extended GARCH-family. In particular, the empirically observed changes in unconditional variance are truthfully mirrored in the simulated data.