The continuous-time limit of quasi score-driven volatility models

TL;DR

This paper investigates the continuous-time limit of Quasi Score-Driven models for volatility, revealing conditions for correlation and demonstrating their use in approximating stochastic volatility models with effective estimation techniques.

Contribution

It establishes the weak convergence of QSD models to correlated stochastic volatility models and identifies conditions for non-degenerate correlation.

Findings

QSD models converge to continuous-time stochastic volatility models with correlated Brownian motions.

Asymmetric innovations are necessary for non-zero correlation in the limit.

Simulation confirms the effectiveness of quasi maximum likelihood estimation for correlation.

Abstract

This paper explores the continuous-time limit of a class of Quasi Score-Driven (QSD) models that characterize volatility. As the sampling frequency increases and the time interval tends to zero, the model weakly converges to a continuous-time stochastic volatility model where the two Brownian motions are correlated, thereby capturing the leverage effect in the market. Subsequently, we identify that a necessary condition for non-degenerate correlation is that the distribution of driving innovations differs from that of computing score, and at least one being asymmetric. We then illustrate this with two typical examples. As an application, the QSD model is used as an approximation for correlated stochastic volatility diffusions and quasi maximum likelihood estimation is performed. Simulation results confirm the method's effectiveness, particularly in estimating the correlation coefficient.