What Does the Volatility Risk Premium Say About Liquidity Provision and Demand for Hedging Tail Risk?
Princeton University - Bendheim Center for Finance
Michael B. Imerman
Lehigh University; Princeton University
Princeton University - Department of Operations Research & Financial Engineering (ORFE)
October 28, 2015
Journal of Business and Economic Statistics (Forthcoming)
This paper 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.
Number of Pages in PDF File: 58
Keywords: volatility risk premium, integrated volatility, ultra-high-frequency data, microstructure noise, Fourier transform, tail risk, Big Data risk analytics
JEL Classification: C01, C55, C58, G12, G17
Date posted: March 17, 2013 ; Last revised: February 3, 2016
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