GARCH pricing and hedging of VIX options

Abstract

We are the first to study the pricing and hedging of VIX options via Monte Carlo (MC) under GARCH(1,1) and Glosten–Jagannathan–Runkle GARCH(1,1) models. Our pricing is ab initio and out‐of‐sample and can be implemented in real time. Importantly, we propose the so‐called single‐option hedge error, a better measure than that of Bakshi et al., and suggest several techniques to expedite MC by over 1000 times, which rivals the potential speedup of quantum computers. Empirically, our proposed approach outperforms two types of benchmarks; further, the asymmetric Glosten–Jagannathan–Runkle outperforms the symmetric GARCH. Overall, our paper has both theoretical and practical implications.

Can technical indicators based on underlying assets help to predict implied volatility index

“…It provides a new perspective to analyze the interrelationship between stock market and the corresponding derivatives market, and opens the avenue for future research. Second, the evidence from the impact of the PDMAs on estimated errors of IV indexes at extreme has implications for pricing third‐generation volatility products such as VIX option, which is the focus of the vast literature (Liu et al, 2022; Mencia & Sentana, 2013; Wang & Daigler, 2011, etc.). Finally, we propose a flexible model framework to comprise the information of technical indicators to improve the estimation of the key risk measure: Value at Risk (VaR).…”