The Cross-Sectional Variation of Volatility Risk Premia

Assessing the compensation for volatility risk implicit in interest rate derivatives

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

Comovement and Volatility Risk Premia

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.

Identifying Volatility Risk Premia from Fixed Income Asian Options

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.

Volatility is a very important variable in financial research, and so is the volatility risk premium. This article constructs upward, downward and overall volatility risk premiums based on the daily data of Shanghai 50 ETF options and the 5-minute high-frequency data of the Shanghai 50 Index from February 9, 2015 to February 28, 2020. The quantile regression method is used to predict the return of the Shanghai 50 Index in the next 14 days, 30 days, 60 days, 90 days, 180 days, 270 days, and 360 days. Our study finds that the 0.05 quantile has the best prediction effect, and the overall volatility risk premium is better than the upward and downward volatility risk premiums in most of the time, which shows overall risk premium has more information than others.

We discover a new currency strategy with highly desirable return and diversification properties, which uses the predictive ability of currency volatility risk premia for currency returns. The volatility risk premium—the difference between expected realized volatility and model-free implied volatility—reflects the costs of insuring against currency volatility fluctuations. The strategy sells high insurance-cost currencies and buys low insurance-cost currencies. A distinctive feature of the strategy’s returns is that they are mainly generated by movements in spot exchange rates instead of interest rate differentials. We explore explanations for the profitability of the strategy, which cannot be understood using traditional risk factors.

Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities

Dynamic Estimation of Volatility Risk Premia and Investor Risk Aversion from Option-Implied and Realized Volatilities

Cross-Section of Option Returns and the Volatility Risk Premium

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.

Global Currency Misalignments, Crash Sensitivity, and Downside Insurance Costs

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.

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.