A general equilibrium approach to pricing volatility risk

Jianlei Han, Martina Linnenluecke, Zhangxin Liu, Zheyao Pan, Tom Smith

Research output: Contribution to journalArticle

Abstract

This paper provides a general equilibrium approach to pricing volatility. Existing models (e.g., ARCH/GARCH, stochastic volatility) take a statistical approach to estimating volatility, volatility indices (e.g., CBOE VIX) use a weighted combination of options, and utility based models assume a specific type of preferences. In contrast we treat volatility as an asset and price it using the general equilibrium state pricing framework. Our results show that the general equilibrium volatility method developed in this paper provides superior forecasting ability for realized volatility and serves as an effective fear gauge. We demonstrate the flexibility and generality of our approach by pricing downside risk and upside opportunity. Finally, we show that the superior forecasting ability of our approach generates significant economic value through volatility timing.

Original languageEnglish
Article number0215032
Number of pages18
JournalPLoS One
Volume14
Issue number4
DOIs
Publication statusPublished - 12 Apr 2019

Cite this

Han, Jianlei ; Linnenluecke, Martina ; Liu, Zhangxin ; Pan, Zheyao ; Smith, Tom. / A general equilibrium approach to pricing volatility risk. In: PLoS One. 2019 ; Vol. 14, No. 4.
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Han, J, Linnenluecke, M, Liu, Z, Pan, Z & Smith, T 2019, 'A general equilibrium approach to pricing volatility risk' PLoS One, vol. 14, no. 4, 0215032. https://doi.org/10.1371/journal.pone.0215032

A general equilibrium approach to pricing volatility risk. / Han, Jianlei; Linnenluecke, Martina; Liu, Zhangxin; Pan, Zheyao; Smith, Tom.

In: PLoS One, Vol. 14, No. 4, 0215032, 12.04.2019.

Research output: Contribution to journalArticle

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AU - Han, Jianlei

AU - Linnenluecke, Martina

AU - Liu, Zhangxin

AU - Pan, Zheyao

AU - Smith, Tom

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AB - This paper provides a general equilibrium approach to pricing volatility. Existing models (e.g., ARCH/GARCH, stochastic volatility) take a statistical approach to estimating volatility, volatility indices (e.g., CBOE VIX) use a weighted combination of options, and utility based models assume a specific type of preferences. In contrast we treat volatility as an asset and price it using the general equilibrium state pricing framework. Our results show that the general equilibrium volatility method developed in this paper provides superior forecasting ability for realized volatility and serves as an effective fear gauge. We demonstrate the flexibility and generality of our approach by pricing downside risk and upside opportunity. Finally, we show that the superior forecasting ability of our approach generates significant economic value through volatility timing.

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