Measuring Information Capacity of Volatility Risk Premium Using Quantile Regression

As the latest addition to the basic equity smart beta factors, the volatility risk premium (VRP) has become an attractive potential source of additional returns for investors. The VRP is generally defined as the difference between the implied volatility of options and the subsequently realized volatility. However, few VRP-based investment products are available that both deliver consistent returns and are low cost. The focus of this article is how investors can incorporate the VRP into typical portfolios, exemplified by a balanced 60/40 portfolio and an equal-weight, multi-asset diversified portfolio. The article explores two different methods, the dedicated VRP construct (long only) and the overlay VRP construct (long–short) and concludes that both methods can potentially enhance investors’ portfolio returns without significantly altering the portfolio’s risk profile because the VRP is an attractive and traditionally untapped source of returns that has exhibited low correlations with traditional risk premiums. The conclusions drawn in the article may be applicable to the task of incorporating other smart beta factors into portfolios.

Comovement and Volatility Risk Premia

Volatility Feedback and Risk Premium in GARCH Models with Generalized Hyperbolic Distributions

This paper examines whether and how differences in investors' information environments (measured by a country's information disclosure, accounting standards, and financial transparency) are related to cross-country differences in the market risk premium volatility. We use the vector-autoregressive and implied cost of capital methods to extract time variation in risk premiums for 41 developed and emerging markets worldwide. Consistent with theoretical predictions, countries with better information environments tend to experience a lower risk premium volatility, even after controlling for various country variables that are potentially associated with variation in risk premiums. Our analysis of two exogenous events, specifically the 1997 Asian financial crisis and 2008 global financial crisis, further corroborates our key finding that information environments play an important role in explaining market risk premium variability.This paper was accepted by Wei Xiong, finance.

The Cross-Sectional Variation of Volatility Risk Premia

Assessing the compensation for volatility risk implicit in interest rate derivatives

Volatility risk premium, good volatility and bad volatility: Evidence from SSE 50 ETF options

Global Currency Misalignments, Crash Sensitivity, and Downside Insurance Costs

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.

In this paper, we provide a novel representation of delta-hedged option returns in a stochastic volatility environment. The representation of delta-hedged option returns provided in this paper consists of two terms: volatility risk premium and parameter estimation risk. In an empirical analysis, we examine delta-hedged option returns based on the result of a historical simulation with the USD-JPY currency option market data from October 2003 to June 2010. We find that the delta-hedged option returns for OTM put options are strongly affected by parameter estimation risk as well as the volatility risk premium, especially in the post-Lehman shock period.

I analyze the impact of competition on the risk premia of R&D ventures engaged in a multiple-stage patent race with technical and market uncertainty. After solving in closed form for the case of a two-stage race in continuous time, I show that a firm's risk premium decreases as a consequence of technical progress and increases when a rival pulls ahead. Compared to the case where firms collude, R&D competition erodes the option value to mothball a project, reduces the completion time and the failure rate of R&D, and causes higher and more volatile risk premia. Numerical simulations reveal that competition can generate risk premia up to 500 annual basis points higher and up to three times more volatility than in a collusive industry.

Ten years have passed since the trough of the Global Financial Crisis, and the US equity market has experienced one of the longest stretches of ascent in history. Some investors...

We use data for 24 European countries, spanning from 1994 to 2015, in order to examine how changes in macroeconomic conditions influence country risk premium volatility proxied by sovereign spreads variance. In the first part of the empirical analysis, we estimate the univariate generalized autoregressive conditional heteroskedasticity (GARCH) model in order to obtain the conditional variance of sovereign bond spreads. We show that an increase in this variance coincides with economic and financial crisis occurring either in the country or globally. In the second part of the empirical analysis, we estimate the panel vector autoregression (panel VAR) model in order to model the interplay between macroeconomic fundamentals (inflation, output gap, public debt and interest rates) and the country’s risk premium volatility. We show that overheating of the economy, along with an unexpected increase in public debt, inflation and interest rates, increase the country’s risk premium volatility. We also show that a sudden increase in the country´s risk premium volatility depresses the economy, exerts deflationary pressures on consumer prices, and is followed by a strong and permanent increase in public debt.

Identifying Volatility Risk Premia from Fixed Income Asian Options

We use a novel pricing model to filter times series of diffusive volatility and jump intensity from S&P 500 index options. These two measures capture the ex-ante risk assessed by investors. We find that both components of risk vary substantially over time, are quite persistent, and correlate with each other and with the stock index. Using a simple general equilibrium model with a representative investor, we translate the filtered measures of ex-ante risk into an ex-ante risk premium. We find that the average premium that compensates the investor for the risks implicit in option prices, 10.1 percent, is about twice the premium required to compensate the same investor for the realized volatility, 5.8 percent. Moreover, the ex-ante equity premium that we uncover is highly volatile, with values between 2 and 32 percent. The component of the premium that corresponds to the jump risk varies between 0 and 12 percent.