Impact of exogenous events on volatility derivatives pricing

Abstract

Investors and risk managers looking to manage or diversify risks by investing in volatility derivatives continue to face the challenge of accurately valuing variance and volatility swaps. This article proposes an alternative approach for the valuation of variance and volatility derivatives on discrete samples by developing an enlarged Markov switching stochastic volatility model with jumps. Merging these three economic and financial properties enables our model to account for both endogenous and exogenous factors. A key theoretical contribution is the derivation of partial integro-differential equations (PIDEs) that characterize the fair delivery price of discretely sampled variance swaps within this framework. We perform comprehensive numerical simulations that demonstrate the model’s ability to capture important market features, including regime shifts, jumps, and volatility clustering. Findings suggest that the model provides an enhanced framework for pricing discretely sampled variance swaps.