What Does the Volatility Risk Premium Say About Liquidity Provision and Demand for Hedging Tail Risk?

Abstract

This article provides a data-driven analysis of the volatility risk premium, using tools from high-frequency finance and Big Data analytics. We argue that the volatility risk premium, loosely defined as the difference between realized and implied volatility, can best be understood when viewed as a systematically priced bias. We first use ultra-high-frequency transaction data on SPDRs and a novel approach for estimating integrated volatility on the frequency domain to compute realized volatility. From that we subtract the daily VIX, our measure of implied volatility, to construct a time series of the volatility risk premium. To identify the factors behind the volatility risk premium as a priced bias, we decompose it into magnitude and direction. We find compelling evidence that the magnitude of the deviation of the realized volatility from implied volatility represents supply and demand imbalances in the market for hedging tail risk. It is difficult to conclusively accept the hypothesis that the direction or sign of the volatility risk premium reflects expectations about future levels of volatility. However, evidence supports the hypothesis that the sign of the volatility risk premium is indicative of gains or losses on a delta-hedged portfolio.

KEY WORDS:

• Big Data risk analytics
• Fourier transform
• Integrated volatility
• Microstructure noise
• Tail risk
• Ultra-high-frequency data
• Volatility risk premium

ACKNOWLEDGMENTS

We thank participants at the 2015 China International Conference in Finance (CICF); the 2015 FMA meetings in Orlando; the 20th Annual Conference on Pacific Basin Finance, Economics, Accounting, and Management at Rutgers University; and the Second Measuring Risk Conference at Princeton University as well as the Finance Department Seminar Series at Rutgers Business School. We are especially grateful for comments from Yacine Aït-Sahalia, Peter Carr, Hui Chen, Hao Zhou, and Hong Zhang (CICF discussant). This research was supported by National Science Foundation (NSF) grant DMS-0704337.