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

Assessing the compensation for volatility risk implicit in interest rate derivatives

Comovement and Volatility Risk Premia

The Cross-Sectional Variation of Volatility Risk Premia

Identifying Volatility Risk Premia from Fixed Income Asian Options

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.

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.

It is sometimes argued that an increase in stock market volatility raises required stock returns, and thus lowers stock prices. This paper modifies the generalized autoregressive conditionally heteroskedastic (GARCH) model of returns to allow for this volatility feedback effect. The resulting model is asymmetric, because volatility feedback amplifies large negative stock returns and dampens large positive returns, making stock returns negatively skewed and increasing the potential for large crashes. The model also implies that volatility feedback is more important when volatility is high. In U.S. monthly and daily data in the period 1926-88, the asymmetric model fits the data better than the standard GARCH model, accounting for almost half the skewness and excess kurtosis of standard monthly GARCH residuals. Estimated volatility discounts on the stock market range from 1% in normal times to 13% after the stock market crash of October 1987 and 25% in the early 1930's. However volatility feedback has little effect on the unconditional variance of stock returns.

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.

Global Currency Misalignments, Crash Sensitivity, and Downside Insurance Costs

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.

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.

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

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.