SPX, VIX and scale-invariant LSV\footnote{Local Stochastic Volatility}

TL;DR

This paper introduces a novel approach to Local Stochastic Volatility models using relative, non-dimensional parameters derived from historical data, enhancing model stability, interpretability, and robustness for pricing, hedging, and risk management.

Contribution

It proposes a new specification for LSV models based on physically meaningful relative quantities, along with an efficient hybrid pricing method and robust scenario generation techniques.

Findings

Relative parameters are more stable and intuitive for trading.

The hybrid pricing method improves computational efficiency.

Scenario generation is more robust for risk management.

Abstract

Local Stochastic Volatility (LSV) models have been used for pricing and hedging derivatives positions for over twenty years. An enormous body of literature covers analytical and numerical techniques for calibrating the model to market data. However, the literature misses a potent approach commonly used in physics and works with absolute (dimensional) variables rather than with relative (non-dimensional) ones. While model parameters defined in absolute terms are counter-intuitive for trading desks and tend to be heavily time-dependent, relative parameters are intuitive and stable, making it easy to steer the model adequately and consistently with its Profit and Loss (PnL) explanation power. We propose a specification that first explores historical data and uses physically well-defined relative quantities to design the model. We then develop an efficient hybrid method to price derivatives under this specification. We also show how our method can be used for robust scenario generation purposes - an important risk management task vital for buy-side firms.\footnote{The authors would like to thank Prof. Marcos Lopez de Prado and Dr. Vincent Davy Zoonekynd for valuable comments.}