Inversion of Convex Ordering: Local Volatility Does Not Maximize the Price of VIX Futures

TL;DR

This paper challenges the common belief that local volatility models maximize VIX futures prices by constructing a stochastic volatility model where VIX futures are more expensive, demonstrating an inversion of convex ordering.

Contribution

It introduces a continuous stochastic volatility model that contradicts the prevailing assumption about local volatility models maximizing VIX futures prices.

Findings

VIX futures can be more expensive in stochastic models than in local volatility models.

An inversion of convex ordering between local and stochastic variances is demonstrated.

The model can be extended to preserve convex ordering for long maturities.

Abstract

It has often been stated that, within the class of continuous stochastic volatility models calibrated to vanillas, the price of a VIX future is maximized by the Dupire local volatility model. In this article we prove that this statement is incorrect: we build a continuous stochastic volatility model in which a VIX future is strictly more expensive than in its associated local volatility model. More generally, in this model, strictly convex payoffs on a squared VIX are strictly cheaper than in the associated local volatility model. This corresponds to an inversion of convex ordering between local and stochastic variances, when moving from instantaneous variances to squared VIX, as convex payoffs on instantaneous variances are always cheaper in the local volatility model. We thus prove that this inversion of convex ordering, which is observed in the SPX market for short VIX maturities, can be produced by a continuous stochastic volatility model. We also prove that the model can be extended so that, as suggested by market data, the convex ordering is preserved for long maturities.