Incorporating Smart Beta into Portfolios: A Case Study with the Volatility Risk Premium

Comovement and Volatility Risk Premia

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.

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

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.

Global Currency Misalignments, Crash Sensitivity, and Downside Insurance Costs

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.

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.

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.

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