Volatility Risk Premium Research Articles

Pricing Poseidon: Extreme Weather Uncertainty and Firm Return Dynamics

ABSTRACTWe empirically analyze firm‐level uncertainty generated from extreme weather events, guided by a theoretical framework. Stock options of firms with establishments in a hurricane's (forecast) landfall region exhibit large implied volatility increases, reflecting significant uncertainty (before) after impact. Volatility risk premium dynamics reveal that investors underestimate such uncertainty. This underreaction diminishes for hurricanes after Sandy, a salient event that struck the U.S. financial center. Despite constituting idiosyncratic shocks, hurricanes affect hit firms' expected stock returns. Textual analysis of calls between firm management, analysts, and investors reveals that discussions about hurricane impacts remain elevated throughout the long‐lasting high‐uncertainty period after landfall.

Higher-Order Moment Risk and ESG Sentiment: Evidence from China’s Options Market

The relationship between risk and return is an important topic in financial research. In this paper, we investigate the factors affecting the higher-order moment risk premiums. The results indicate that there is a significant interaction between variance, skewness and kurtosis when considering risk premiums. This implies that there are both common and independent sources of risk information. In addition, ESG news sentiment has a significant positive effect on volatility risk premium.

Quantitative Easing and Direct Lending in Response to the COVID‐19 Crisis

AbstractThis paper develops a dynamic general equilibrium model to study quantitative easing (QE) and direct lending to firms. QE works through three channels: expanding bank reserves raises liquidity and lowers the liquidity premium, purchasing assets withdraws risk and lowers the volatility risk premium, and the resulting economic stimulus lowers the credit risk premium. When bank reserves are higher, the liquidity premium channel is weaker, and QE is less expansionary. Direct lending is more expansionary than QE because it substitutes bank lending and mitigates the credit risk frictions associated with bank lending, while QE stimulates bank lending and worsens the frictions.

Very Noisy Option Prices and Inference Regarding the Volatility Risk Premium

ABSTRACTThe stylized fact that volatility is not priced in individual equity options does not withstand scrutiny. First, we show that the average return of heavily traded deep out‐of‐the‐money call options on stocks is −116 basis points per day. Second, Fama‐MacBeth estimates of the volatility risk premium in stock options are similar to those in S&P 500 Index call options. Third, the mean return of heavily traded delta‐hedged at‐the‐money calls (puts) is −23 (−30) basis points. Fourth, the variance risk premium in stock options is negative. Our analysis highlights the importance of microstructure biases and robustness in empirical work with options.

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

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

The asymmetry in day and night option returns: Evidence from an emerging market

AbstractDelta‐hedged option selling strategies typically yield positive returns, owing to the volatility risk premium embedded in the option price. Recent research based on S&P 500 options has found a day–night asymmetry in option returns. We find a similar disparity in the returns for short Nifty option strategies. Positive and significant overnight option returns are accompanied by negative intraday returns. The day–night asymmetry is robust across option categories and subsamples but weaker on days with significant jumps in the underlying. We confirm that the variance risk premium earned by option sellers is mainly a reward for overnight risk.

The Common Factor in Volatility Risk Premia

The Common Factor in Volatility Risk Premia

Forecasting day-ahead expected shortfall on the EUR/USD exchange rate: The (I)relevance of implied volatility

Forecasting day-ahead expected shortfall on the EUR/USD exchange rate: The (I)relevance of implied volatility

Can Volatility Risk Premia Improve Fama-French Three-Factor Model in the Explanation of Stock Index Returns? — Evidence from US Industrial Stock Indices

Can Volatility Risk Premia Improve Fama-French Three-Factor Model in the Explanation of Stock Index Returns? — Evidence from US Industrial Stock Indices

The volatility index and volatility risk premium in China

The volatility index and volatility risk premium in China

Dynamic Relationship between Volatility Risk Premia of Stock and Oil Returns

This study investigates the relationship between the volatility risk premia (VRP) of stock and oil returns. Using daily data on VRP from 10 May 2007 to 16 May 2017, VAR analyses on the stock and oil VRP are conducted, and it is found that the effects of the stock VRP on the oil VRP are limited and, if any, short-lived. In contrast, the VRP of oil has significantly positive and long-lasting effects on the stock VRP after the financial crisis. These results suggest that investors’ sentiments (measured by VRP) are transmitted from the oil to the stock market over time, but not vice versa. This is unexpected because the financialization of commodities means a massive increase in investment in commodities by investors in the traditional stock and bond markets; hence, the direction of effects is thought to be from the stock to the commodity market.

Swaption and term structures of volatility risk premiums

Swaption and term structures of volatility risk premiums

Uncertainty‐driven oil volatility risk premium and international stock market volatility forecasting

AbstractForecasting international stock market volatility using the oil volatility risk premium (OVRP) is important for asset allocation and financial risk management. In previous literature, the issue of biased OVRP has not been well addressed. The OVRP is defined as the difference between the ex ante risk‐neutral expectation of oil return variation and the oil realized variance. This is a biased estimator as argued by previous studies. In this paper, we propose a new measure of OVRP, namely, the uncertainty‐driven OVRP (UOVRP), in which the expected realized variation is the conditional variance of oil futures returns estimated by a typical GARCH‐MIDAS model, and the long‐term volatility component of conditional variance is directly determined by several uncertainty indices. Using heterogeneous autoregressive models (HAR), we find that UOVRPs significantly and positively predict most of the international stock market volatility in‐sample. Furthermore, it significantly outperforms the realized volatility, the “model‐free” OVRP, and recently proposed volatility predictors for most American, European, and Pacific markets out‐of‐sample. On average, UOVRPs driven by the world uncertainty index and the U.S. economic policy uncertainty perform the best among all UOVRPs. The predictive ability of UOVRPs is robust across a series of checks.

Realized GARCH, CBOE VIX, and the Volatility Risk Premium

Abstract We show that the realized GARCH model yields closed-form expression for both the volatility index (VIX) and the volatility risk premium (VRP). The realized GARCH model is driven by two shocks, a return shock and a volatility shock, and these are natural state variables in the stochastic discount factor (SDF). The volatility shock endows the exponentially affine SDF with compensation for volatility risk. This leads to dissimilar dynamic properties under the physical and risk-neutral measures that can explain time-variation in the VRP. In an empirical application with the S&P 500 returns, the VIX, and the VRP, we find that the realized GARCH model significantly outperforms conventional GARCH models.

Belief Dispersion and Convex Cost of Adjustment in the Stock Market and in the Real Economy

I develop a continuous-time general equilibrium model with a continuum of states of the world and a continuum of agents endowed with heterogeneous beliefs. The model permits to analyze the interactions between financial markets and production. There is a single firm that faces convex adjustment costs and maximizes its terminal value. Equivalently, the firm uses decreasing returns to scale risk-return technology. The model is tractable and matches many of the empirical regularities in aggregate output and stock prices, such as a financial volatility that is higher than the macroeconomic volatility, skewness, kurtosis, short-term momentum, and volatility risk premium during recessions. All these aspects disappear when one assumes beliefs homogeneity or constant returns to scale. In particular, the impact of beliefs heterogeneity observed in endowment economies does not pertain when introducing production unless one assumes decreasing returns to scale in the risk-return technology. This paper was accepted by David Sraer, finance. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4495 .

Implied volatility information of Chinese SSE 50 ETF options

This paper examines the impact of the implied volatility information in SSE 50 ETF options on the underlying securities volatility. After segmenting options according to option type, moneyness, time to maturity and trading years, we confirm that options contain implied volatility information about the underlying securities, and there is a significant positive effect of the volatility risk premium on the volatility of underlying securities. As for the regression coefficient of volatility risk premium, the call option is higher than the put option due to the investors' risk aversion, and the long-term option is higher than the short-term option. With the moneyness from DOTM to DITM, the call options decrease while the put options increase for the existence of skew and kurtosis risk.

Trading the FX volatility risk premium with machine learning and alternative data

In this study, we show how both machine learning and alternative data can be successfully leveraged to improve and develop trading strategies. Starting from a trading strategy that harvests the EUR/USD volatility risk premium by selling one-week straddles every weekday, we present a machine learning approach to more skillfully time new trades and thus prevent unfavorable ones. To this end, we build probability-calibrated Random Forests on various predictors, extracted from both traditional market data and financial news, to predict the closing Sharpe ratio of short one-week delta-hedged straddles. We then demonstrate how the output of these calibrated machine learning models can be used to engineer intuitive new trading strategies. Ultimately, we show that our proposed strategies outperform the original strategy on risk-based performance measures. Moreover, the features that we derived from financial news articles significantly improve the performance of the approach. • The future performance of short one-week delta-hedged straddles on EUR/USD can be predicted using Random Forests. • Features extracted from financial news improve prediction performance. • Trading strategies based on probability-calibrated Random Forest predictions significantly outperform the original strategy on risk-based measures.

The volatility risk premium in the oil market

This article provides a comprehensive analysis of the volatility risk premium (VRP) in the oil market. We approach the problem from the practitioner’s perspective as an investment strategy that sells and delta-hedges oil options, paying particular attention to the strategy’s risk-adjusted returns and its drawdown characteristics. The results are differentiated across options with different moneyness and expirations and presented in the form of VRP smile and VRP term structure. Strategy results are analyzed using alternative delta-hedging techniques that vary hedging frequencies, hedging thresholds, and volatilities used to calculate options’ delta. We discuss the performance under different regimes and highlight the structural break driven by the changing behavior among main participants in the oil options market.

Jump and volatility risk in the cross-section of corporate bond returns

Investigating the pricing of jump and volatility risk in the cross-section of corporate bonds, we find that bonds with high jump and volatility betas have low expected returns. The jump and volatility risk effects are economically significant, exhibit an intra-rating pattern, and increase as ratings decrease. While both jump and volatility risk effects heighten during the subprime crisis period, jump risk becomes more important than volatility risk in times of stress. The pronounced negative jump and volatility risk premiums cannot be explained by coskewness, cokurtosis, or downside risk exposure and are robust to controlling for conventional risk factors and bond characteristics. • Build a parsimonious structural model that incorporates both jump and volatility risk. • Construct aggregate jump and volatility risk factors based on investable option approach. • Both jump and volatility risk carry a negative premium, and the magnitude of jump risk effect is larger. • The effects of jump and volatility risk are pervasive and not concentrated in just a particular category of corporate bonds. • The significant jump and volatility risk premiums are robust to controlling for both stock and bond market factors.

The Pricing of Volatility and Jump Risks in the Cross-Section of Index Option Returns

AbstractExisting studies relate the puzzling low average returns on out-of-the-money (OTM) index call and put options to nonstandard preferences. We argue the low option returns are primarily due to the pricing of market volatility risk. When volatility risk is priced, expected option returns match the realized average option returns. Moreover, consistent with its theoretical effect on expected option returns, the volatility risk premium is positively related to future index option returns and this relationship is stronger for OTM options and at-the-money straddles. Finally, we find the jump risk premium contributes to some portion of OTM put option returns.