Switching to non-affine stochastic volatility: A closed-form expansion for the Inverse Gamma model

TL;DR

This paper introduces the Inverse Gamma stochastic volatility model with time-dependent parameters, providing a closed-form expansion for option pricing that is more realistic, faster to calibrate, and easier to implement than affine models like Heston.

Contribution

It develops a closed-form volatility-of-volatility expansion for the non-affine Inverse Gamma model, enabling fast and accurate option pricing and calibration.

Findings

The expansion provides accurate option prices compared to Monte Carlo simulations.

The Inverse Gamma model offers more realistic volatility dynamics than affine models.

Calibration tests show the method's effectiveness on real market data.

Abstract

This paper introduces the Inverse Gamma (IGa) stochastic volatility model with time-dependent parameters, defined by the volatility dynamics $dV_{t}=\kappa_{t}\left(\theta_{t}-V_{t}\right)dt+\lambda_{t}V_{t}dB_{t}$. This non-affine model is much more realistic than classical affine models like the Heston stochastic volatility model, even though both are as parsimonious (only four stochastic parameters). Indeed, it provides more realistic volatility distribution and volatility paths, which translate in practice into more robust calibration and better hedging accuracy, explaining its popularity among practitioners. In order to price vanilla options with IGa volatility, we propose a closed-form volatility-of-volatility expansion. Specifically, the price of a European put option with IGa volatility is approximated by a Black-Scholes price plus a weighted combination of Black-Scholes greeks, where the weights depend only on the four time-dependent parameters of the model. This closed-form pricing method allows for very fast pricing and calibration to market data. The overall quality of the approximation is very good, as shown by several calibration tests on real-world market data where expansion prices are compared favorably with Monte Carlo simulation results. This paper shows that the IGa model is as simple, more realistic, easier to implement and faster to calibrate than classical transform-based affine models. We therefore hope that the present work will foster further research on non-affine models like the Inverse Gamma stochastic volatility model, all the more so as this robust model is of great interest to the industry.