Analysing Models for Volatility Clustering with Subordinated Processes: VGSA and Beyond

TL;DR

This paper develops a unified probabilistic framework for subordinated stochastic processes like VGSA and CGMY, incorporating stochastic arrival mechanisms to better model market features such as jump clustering and volatility persistence.

Contribution

It extends classical jump models by integrating stochastic arrival processes, proving asymptotic normality, and providing simulation validation for these advanced models.

Findings

Strong consistency results for VG under Gamma subordination.

Asymptotic normality of VGSA and VG processes with CIR or CKLS arrivals.

Monte Carlo simulations confirm approximate Gaussian behavior in heavy-tailed jump models.

Abstract

This paper explores a comprehensive class of time-changed stochastic processes constructed by subordinating Brownian motion with Levy processes, where the subordination is further governed by stochastic arrival mechanisms such as the Cox Ingersoll Ross (CIR) and Chan Karolyi Longstaff Sanders (CKLS) processes. These models extend classical jump frameworks like the Variance Gamma (VG) and CGMY processes, allowing for more flexible modeling of market features such as jump clustering, heavy tails, and volatility persistence. We first revisit the theory of Levy subordinators and establish strong consistency results for the VG process under Gamma subordination. Building on this, we prove asymptotic normality for both the VG and VGSA (VG with stochastic arrival) processes when the arrival process follows CIR or CKLS dynamics. The analysis is then extended to the more general CGMY process under stochastic arrival, for which we derive analogous consistency and limit theorems under positivity and regularity conditions on the arrival process. A simulation study accompanies the theoretical work, confirming our results through Monte Carlo experiments, with visualizations and normality testing (via Shapiro-Wilk statistics) that show approximate Gaussian behavior even for processes driven by heavy-tailed jumps. This work provides a rigorous and unified probabilistic framework for analyzing subordinated models with stochastic time changes, with applications to financial modeling and inference under uncertainty.