A general equilibrium approach to pricing volatility risk.

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

doi:10.1371/journal.pone.0215032

Jianlei Han, Tom Smith + Show 4 more

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https://doi.org/10.1371/journal.pone.0215032

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Journal: PloS one	Publication Date: Apr 12, 2019
Citations: 3	License type: CC BY 4.0

Affiliation: Macquarie University, University of Western Australia

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.

Highlights

Volatility modelling has proceeded as a field separate from asset pricing
We show that the superior forecasting ability of our general equilibrium volatility measure has greater economic value for investors wishing to manage volatility
Using state prices estimated from S&P 500 index options, we illustrate how we can derive ex-ante volatility measures SVXI for industry portfolios, in which there are no traded options

Summary

Introduction

Volatility modelling has proceeded as a field separate from asset pricing. Statistical models, such as ARCH [1, 2], GARCH [3], stochastic volatility [4], and option prices [5] are commonly used to estimate volatility, without reference to modern asset pricing theory. We propose to price volatility using a general equilibrium asset-pricing framework. The advantage of such an approach is that volatility can be priced and measured in the most general setting available. State prices are obtained for each time based on options written on the aggregate market (S&P 500 index as a proxy)[8] We apply this state pricing approach to market volatility risk and are able to derive prices that are almost perfectly correlated with the CBOE Volatility Index (VIX) but are the result of a general equilibrium model. There are several advantages to treating volatility as any other asset and pricing it using the general equilibrium approach [6].

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