GARCH option valuation with long-run and short-run volatility components: A novel framework ensuring positive variance

TL;DR

This paper introduces a new GARCH option valuation model that combines long-run and short-run volatility components, guaranteeing positive variance and outperforming previous models in options data fitting.

Contribution

The paper proposes a novel GARCH framework ensuring positive variance, improving upon prior models by CJOW and Oh and Park, with better options data performance.

Findings

Ensures positive variance in GARCH models.

Outperforms previous models on S&P500 options data.

Maintains comparable in-sample performance on returns data.

Abstract

Christoffersen, Jacobs, Ornthanalai, and Wang (2008) (CJOW) proposed an improved Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model for valuing European options, where the return volatility is comprised of two distinct components. Empirical studies indicate that the model developed by CJOW outperforms widely-used single-component GARCH models and provides a superior fit to options data than models that combine conditional heteroskedasticity with Poisson-normal jumps. However, a significant limitation of this model is that it allows the variance process to become negative. Oh and Park [2023] partially addressed this issue by developing a related model, yet the positivity of the volatility components is not guaranteed, both theoretically and empirically. In this paper we introduce a new GARCH model that improves upon the models by CJOW and Oh and Park [2023], ensuring the positivity of the return volatility. In comparison to the two earlier GARCH approaches, our novel methodology shows comparable in-sample performance on returns data and superior performance on S&P500 options data.