Volatility Risk Premium Research Articles

Oil uncertainty and firms' risk-taking

Motivated by the limited evidence for a relationship between oil uncertainty and firms' risk-taking in China, we investigate how oil uncertainty (as proxied by the oil volatility risk premium) impacts firms' risk-taking and explain the mechanism. To highlight firms' expectations, growth opportunities, and ability to process new information, we use three proxies for firms' risk-taking (the implied cost of capital, the idiosyncratic volatility and the idiosyncratic skewness). Our empirical results show that oil uncertainty increases firms' risk-taking through the channel of risk compensation or real options related to firms' growth opportunities rather than risk-aversion. Oil uncertainty has a positive effect on firms' risk-taking proxied by the implied cost of capital and the idiosyncratic volatility but a negative effect on firms' risk-taking proxied by the idiosyncratic skewness related to contract options. Various variables involved in ownership, operating leverage and industrial conditions interact with oil uncertainty to affect firms' risk-taking through different channels. Our findings contribute to a greater understanding of the uncertainty in crude oil markets and firms' risk-taking behavior.

Volatility Risk Premium, Return Predictability, and ESG Sentiment: Evidence from China’s Spots and Options’ Markets

This study investigates the volatility risk premium on the emerging financial market. We also consider the expected return and ESG sentiment. Based on the SSE 50 ETF 5‐minute high‐frequency spots and daily options data from 2016 to 2021, we adopt nonparametric model‐free approaches to calculate realized and implied volatilities. And the volatility risk premium is constructed by subtracting these volatility series. We examine the relations between the volatility risk premium and future excess returns as well as ESG sentiment through multifactor specifications. We find that the volatility risk premium also exists in the Chinese market and is significantly negative. In addition, the statistically positive correlation between the volatility risk premium and aggregate returns is an outlier compared to the empirically negative pattern in developed markets. At last, ESG sentiment is positively associated with the volatility risk premium, especially the impact of environmental and social. This evidence supports the agency theory, which indicates that investors perceive ESG investments as waste resources in a short term and become potentially risky.

Income Enhancement with Options

Investors have always tried to use various trading strategies to juice their returns. Writing options has often been thought of as a low-risk way to get some additional income (premiums) while not disturbing the underlying asset allocation. Sometimes, however, investors are caught off guard when their option strategy does more harm than good. In this educational piece we describe one of the most common option writing strategies–covered call writing–and the practicalities of how to manage these strategies so they hopefully don’t backfire. The key is to recognize that the returns from covered call strategies are related to the volatility risk premium (also known as the variance risk premium) as well as the equity risk premium.

Is oil risk important for commodity-related currency returns?

This paper investigates the relationship between oil and commodity-related currency markets using the volatility risk premium (VRP) of oil as an oil risk proxy. We find that oil VRP can forecast typical commodity currency returns except for the New Zealand dollar. The one-month-ahead forecasting model with oil VRP allows us to beat a random walk model for most commodity currency returns in the out-of-sample study and to outperform the classical return forecasting model. Different economic periods influence the predictive power of oil VRP. Oil VRP contains specific information that cannot be captured by classical fundamentals and global risks, and in most cases, it is a better predictor of currency returns than these traditional variables. A strategy using oil VRP to forecast currency returns helps investors reduce portfolio losses. This paper provides a risk channel for oil information transmitting from the oil markets to commodity-related currency markets.

The pricing of volatility risk in the US equity market

We analyze whether the pricing of volatility risk depends on the asset pricing framework applied in the tests, the specified volatility proxies, and the portfolio sorts used for spanning the asset universe. For this purpose, we compare the results using a macroeconomic and fundamental based asset pricing model using three proxies of volatility and uncertainty, using size/value sorted and industry sector portfolios. Our results reveal that the marginal pricing effect of the VIX volatility factor is strong and statistically significant throughout the models and specifications, while the effect of an EGARCH-based volatility factor is mixed, mostly smaller but with the correct sign. In most cases, the EGARCH factor does not impair the pricing effect of the VIX. The portfolio sorts have a substantial impact on the volatility premiums in both model frameworks. The size of the volatility risk premium is more uniform across the models if the industry sector portfolio sort is used. Finally, the size/value portfolio sort generates larger volatility risk premiums for both models.

Measuring the Time Series of High-Frequency Risk Attitude from Volatility Risk Premium: The Case of Emerging Markets

ABSTRACT This study establishes a state-space model and measures the time-varying relative risk aversion using the volatility risk premium from stock market data. This model can measure the time series of high-frequency risk aversion. Based on the stock market data of Brazil, Russia, India, and China, the measurement results of this study show that during the period between January 1, 2009, and December 31, 2020, the mean values of the corresponding implied risk aversion coefficients are 5.6543, 5.7561, 7.5345, and 6.3675, respectively. Our method can solve the mismatch between low-frequency risk aversion and high-frequency market data.

The Profitability of Volatility Trading on Exchange-traded Dollar-rupee Options: Evidence of a Volatility Risk Premium?

This article examines the profitability of short volatility strategies in the exchange-traded USDINR options market. Returns from delta-hedged short positions in straddles, strangles and individual call and put options are examined across different trading horizons and volatility regimes. The study finds that short volatility strategies yield significant mean and median returns regardless of the trading horizon and option moneyness before considering transaction costs. This is suggestive of a volatility risk premium priced in USDINR options. However, the returns are found to be insignificant and even negative after accounting for trading costs such as bid-ask spreads and brokerage. The study concludes that although USDINR options appear to be overpriced because of the volatility risk premium, short option strategies can be profitably exploited only by market makers and institutional investors facing low spreads and funding costs. The findings are suggestive of an informationally efficient market.

Pricing the Volatility Risk Premium with a Discrete Stochastic Volatility Model

Investors’ decisions on capital markets depend on their anticipation and preferences about risk, and volatility is one of the most common measures of risk. This paper proposes a method of estimating the market price of volatility risk by incorporating both conditional heteroscedasticity and nonlinear effects in market returns, while accounting for asymmetric shocks. We develop a model that allows dynamic risk premiums for the underlying asset and for the volatility of the asset under the physical measure. Specifically, a nonlinear in mean time series model combining the asymmetric autoregressive conditional heteroscedastic model with leverage (NGARCH) is adapted for modeling return dynamics. The local risk-neutral valuation relationship is used to model investors’ preferences of volatility risk. The transition probabilities governing the evolution of the price of the underlying asset are adjusted for investors’ attitude towards risk, presenting the asset returns as a function of the risk premium. Numerical studies on asset return data show the significance of market shocks and levels of asymmetry in pricing the volatility risk. Estimated premiums could be used in option pricing models, turning options markets into volatility trading markets, and in measuring reactions to market shocks.

The high frequency risk attitude implied by the volatility risk premium

Risk attitude measures risk preference, determines micro subjects’ investment portfolios and ultimately affects the macro financial market structure, and is commonly measured by risk aversion coefficient. However, constant or lower frequency risk attitude data makes it difficult to match financial variables in frequency. The risk aversion coefficient characterizes the risk premium of unit risk, so there should be a risk attitude behind each volatility and its risk premium. Based on this principle, this paper introduces risk aversion into the continuous-time stochastic volatility model and establishes a state-space model to separate the implied risk attitude time series from the high-frequency volatility and its risk premium time series, which solves the problem of mismatch between the low-frequency risk aversion and the high-frequency market data in the literature.

Delta-hedged gains of SSE 50 ETF options

ABSTRACT Using Chinese option market data, we construct a delta-neutral strategy for Shanghai Stock Exchange (SSE) 50ETF options and investigate the statistical properties of the strategy gains. The empirical results show that the delta-hedged gains are significantly negative, indicating that to hedge the volatility risk, the option buyer needs to pay a volatility risk premium to the option seller.

A Stochastic Volatility Model with Mean-reverting Volatility Risk Premium

The volatility risk premium (VRP) has long been the core issue in option pricing and risk management. The VRP is usually defined as a linear function of volatility which ignores the time-varying property of VRP and limits the degree of freedom of the model. In this paper, we adopt a CIR process in the stochastic volatility model (VRP-CIR-SV) to incorporate the mean-reverting and time-varying properties of VRP. We show that the decomposition of VRP is consistent to investor’s behaviour. Our Monte Carlo simulation results show that, compared with the traditional linear VRP model, the VRP-CIR-SV model can better depict the rich shapes of implied volatility curve. Our paper innovatively models the time-varying VRP with mean-reverting property, which may provide new thoughts for VRP estimation.

Practical Applications of Being Right Is Not Enough: Buying Options to Bet on Higher Realized Volatility

Practical Applications In Being Right Is Not Enough: Buying Options to Bet on Higher Realized Volatility, published in the Fall 2020 issue of The Journal of Alternative Investments, Roni Israelov, of NDVR Capital Management, and Harsha Tummala, of AQR Capital Management, illustrate that it is not easy to profit from long equity option positions. They do so by creating delta-hedged option portfolios and examining the returns to the resulting long-volatility strategy. The authors find that traders must not only correctly predict that volatility will increase during their holding period, but also must successfully time their entries and exits. Profitably accomplishing these tasks is particularly difficult due to the well-documented volatility risk premium (VRP) that usually accrues to option writers. The authors analyze the following measures and their relationships: the percentage change in realized volatility of the underlying asset (equity indexes), the ex post VRP, and long option portfolio returns levels. They also examine the usefulness of using the strategy to hedge equity losses. Additionally, they perform an event study to investigate the timing effects of entering and exiting long volatility positions. TOPICS:Options, performance measurement

Measuring Information Capacity of Volatility Risk Premium Using Quantile Regression

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.

Pricing Poseidon: Extreme Weather Uncertainty and Firm Return Dynamics

We present a framework to identify market responses to uncertainty faced by firms regarding both the potential incidence of extreme weather events and subsequent economic impact. Stock options of firms with establishments in forecast and realized hurricane landfall regions exhibit large increases in implied volatility, reflecting significant incidence uncertainty and long-lasting impact uncertainty. Comparing ex ante expected volatility to ex post realized volatility by analyzing volatility risk premia changes shows that investors significantly underestimate extreme weather uncertainty. After Hurricane Sandy, this underreaction diminishes and, consistent with Merton (1987), these increases in idiosyncratic volatility are associated with positive expected stock returns.

Liquidity Guided Machine Learning: The Case of the Volatility Risk Premium

The financial industry has eagerly adopted machine learning algorithms to improve on traditional predictive models. In this paper we caution against blindly applying such techniques. We compare forecasting ability of machine learning methods in evaluating future payoffs on synthetic variance swaps. Standard machine learning methods tend to identify contracts which are illiquid, and hard to trade. The most successful strategies turn out to be those where we pair machine learning with institutional and market/traders inputs and insights. We show that liquidity guided pre-selection of inputs to machine learning results in trading strategies with improved pay-offs to the writers of variance swap contract replicating portfolio.

Cross-Section of Option Returns and the Volatility Risk Premium

This paper presents a robust new finding that delta-hedged equity option returns include a volatility risk premium. To separate volatility risk premia from confounding effects, we estimate conditional quantile curves of implied volatilities using machine learning. We find that a zero-cost trading strategy that is long (short) in the portfolio with low (high) implied volatility -- conditional on the options' moneyness and realized volatility -- produces an economically and statistically significant average monthly return. Using conditional quantile curves not only helps in distinguishing volatility risk premia from other effects, most notably realized volatility, it also leads to returns that are higher than those reported in previous work on similar volatility strategies.

Single Stock Options and Systematic Volatility Risk: Evidence from India

Extant empirical evidence on volatility risk premium in emerging markets is limited to the market level index options. This study extends of understanding of volatility risk premium in single stock options in the Indian market. The study finds that volatility risk is priced systematically among the single stock options and the results are robust to alternative specifications of volatility and sampling frequencies. The study then examines the cross-sectional determinants of the systematic risk and finds that the options' systematic volatility risk is positively related to the hedging demand of tail risk and negatively associated with the options market liquidity.

Inflation Volatility Risk and the Cross-section of Corporate Bond Returns

As corporate bonds are primarily denominated in nominal terms, inflation uncertainty arises as a relevant source of risk. This paper analyzes the relevance of inflation volatility risk as an additional factor predicting the cross-section of corporate bond returns. I find a negative and significant inflation volatility risk premium (IVRP) obtained from the difference between high inflation and low inflation beta portfolios. Further, common risk factors in the equity and corporate bond markets do not explain the IVRP, it responds to ex-post inflation risk and is partially explained by market risk and monetary policy shocks. Lastly, I show that the IVRP is associated with firms incurring in debt maturity management to mitigate refinancing risks.

The impact of political risk on the currencies of emerging markets

This study investigates the effects of political risk on the exchange rate returns of Brazil, Chile, Mexico, and Russia. The results indicate the presence of a risk premium for all currencies. Political risk was observed to negatively impact trade returns for only the Brazilian real, a result of depreciating the exchange rate. This effect was not observed for the other countries analyzed. In Brazil, transitory risk-premium volatility was positively associated with both the VIX index and political risk, indicating that greater global and local political risk increased volatility. Furthermore, local political risk had a more significant impact on risk-premium volatility than global risk.

Joint estimation of volatility risk and tail risk premia with time-varying macro-state-dependent property

We propose a new method to jointly estimate volatility risk and two-tail risk price with state-dependent features. Rather than assuming a constant risk price, as in existing models, this new method estimates an extended pricing kernel with macro-state-dependent risk prices. In contrast to the widely accepted constant risk price assumption, we find that the prices for equity, volatility, positive jump, and negative jump risks are strongly dependent on economic conditions. The empirical evidence shows that this new estimation for the macro-state-dependent property adds new pricing information that existing constant risk-price models do not provide. The estimation of macro-state-dependent property has important economic implications for the underlying dynamics and derivative markets. State-dependent risk prices substantially improve the explanation of the dynamic link between the underlying and option markets, and are important factors in the option market. With the out-of-sample test, the new method provides a stable estimation of the risk price dynamics.