A short cut: Directly pricing VIX futures with discrete‐time long memory model and asymmetric jumps

Fangsheng Yin, Yang Bian and Tianyi Wang ()

Journal of Futures Markets, 2021, vol. 41, issue 4, 458-477

Abstract: This paper proposes a simple but rich framework to directly price volatility index (VIX) futures by applying the heterogeneous autoregressive structure and asymmetric jumps to the logarithm of the VIX. Compared with other discrete‐time models, our model imposes fewer parameter constraints. The analytical solution is also free from time‐consuming and sometimes unstable numerical integration. Empirical results suggest that our model can significantly reduce pricing errors compared with existing models using realized variance, both in‐ and out‐of‐sample. The improvement indicates that besides looking for a better measure of current volatility, it is also important to utilize information embedded in the VIX itself.

Date: 2021
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https://doi.org/10.1002/fut.22183

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