Variance Risk Premium, Higher Order Risk Premium, and Expected Stock Returns

The variance risk premium defined as the difference between risk neutral variance and physical variance is one of the most crucial information recovered from option prices. It does not, however, reflect the asymmetry in upside and downside movements of underlying asset returns, and also has limitation in reflecting asymmetric preference of investors over gains and losses. In this sense, this paper decomposes variance risk premium into downside - and upside-variance risk premium, and then derives the skewness risk premium and examines its effectiveness in predicting future underlying asset returns. Using KOSPI200 option prices, we obtained the following results. First, we found out that the estimated skewness risk premium has meaningful forecasting power for future stock returns, while the estimated variance risk premium has little forecasting power. Second, by utilizing our results of skewness risk premium, we developed a profitable investment strategy, which verifies the effectiveness of skewness risk premium in predicting future stock returns. In conclusion, the empirical results of this paper can contribute to the literature in that it helps us understand why variance risk premium, in most global markets except the US market, has not been successful in forecasting future stock returns. In addition, our results showing the profitability of investment strategies based on skewness risk premium can also give important implications to practitioners.

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Financial Markets with Trade on Risk and Return

Review of Financial Studies ◽

10.1093/rfs/hhz013 ◽

2019 ◽

Vol 32 (10) ◽

pp. 4042-4078 ◽

Cited By ~ 4

Author(s):

Kevin Smith

Keyword(s):

Stock Returns ◽

Risk Premium ◽

Private Information ◽

Trading Volume ◽

Variance Risk Premium ◽

Oxford University ◽

Editorial Decision ◽

Variance Risk ◽

The Relationship ◽

Published Paper

Abstract In this paper, I develop a model in which risk-averse investors possess private information regarding both a stock’s expected payoff and its risk. These investors trade in the stock and a derivative whose payoff is driven by the stock’s risk. In equilibrium, the derivative is used to speculate on the stock’s risk and to hedge against adverse fluctuations in the stock’s risk. I analyze the derivative price and variance risk premium that arise in this equilibrium and their predictive power for stock returns. Finally, I examine the relationship between prices and trading volume in the stock and derivative. Received July 31, 2017; editorial decision December 3, 2018 by Editor Stijn Van Nieuwerburgh. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

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OUT-OF-SAMPLE STOCK RETURN PREDICTION USING HIGHER-ORDER MOMENTS

International Journal of Theoretical and Applied Finance ◽

10.1142/s0219024918500437 ◽

2018 ◽

Vol 21 (06) ◽

pp. 1850043 ◽

Cited By ~ 2

Author(s):

JOSÉ AFONSO FAIAS ◽

TIAGO CASTEL-BRANCO

Keyword(s):

Stock Returns ◽

Cross Section ◽

Risk Premium ◽

Risk Premia ◽

Market Returns ◽

Variance Risk Premium ◽

Stock Return Prediction ◽

Out Of Sample ◽

The Cross ◽

Variance Risk

We analyze variance, skewness and kurtosis risk premia and their option-implied and realized components as predictors of excess market returns and of the cross-section of stock returns. We find that the variance risk premium is the only moment-based variable to predict S&P 500 index excess returns, with a monthly out-of-sample [Formula: see text] above 6% for the period between 2001 and 2014. Nonetheless, all aggregate moment-based variables are effective in predicting the cross-section of stock returns. Self-financed portfolios long on the stocks least exposed to the aggregate moment-based variable and short on the stocks most exposed to it achieve positive and significant Carhart 4-factor alphas and a considerably higher Sharpe ratio than the S&P 500 index, with positive skewness.

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Risk, Uncertainty, and Expected Returns

Journal of Financial and Quantitative Analysis ◽

10.1017/s0022109016000417 ◽

2016 ◽

Vol 51 (3) ◽

pp. 707-735 ◽

Cited By ~ 69

Author(s):

Turan G. Bali ◽

Hao Zhou

Keyword(s):

Stock Returns ◽

Risk Premium ◽

Risk Premia ◽

Expected Returns ◽

Cross Sectional ◽

Asset Pricing Model ◽

Variance Risk Premium ◽

Equity Portfolios ◽

Variance Risk ◽

Highly Correlated

AbstractA conditional asset pricing model with risk and uncertainty implies that the time-varying exposures of equity portfolios to the market and uncertainty factors carry positive risk premia. The empirical results from the size, book-to-market, momentum, and industry portfolios indicate that the conditional covariances of equity portfolios with market and uncertainty predict the time-series and cross-sectional variation in stock returns. We find that equity portfolios that are highly correlated with economic uncertainty proxied by the variance risk premium (VRP) carry a significant annualized 8% premium relative to portfolios that are minimally correlated with VRP.

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Stock Return Extrapolation, Option Prices, and Variance Risk Premium

Review of Financial Studies ◽

10.1093/rfs/hhab051 ◽

2021 ◽

Author(s):

Adem Atmaz

Keyword(s):

Stock Returns ◽

Risk Premium ◽

Stock Return ◽

Dynamic Equilibrium ◽

Investor Sentiment ◽

Option Prices ◽

Market Returns ◽

Stock Market Returns ◽

Variance Risk Premium ◽

Variance Risk

Abstract This paper presents a tractable dynamic equilibrium model of stock return extrapolation in the presence of stochastic volatility. In the model, consistent with survey evidence, investors expect future returns to be higher (lower) but also less (more) volatile following positive (negative) stock returns. The biased volatility expectation introduces a new channel through which past returns and investor sentiment affect derivative prices. In particular, through this novel channel, the model reconciles the otherwise puzzling evidence of past returns affecting option prices and the evidence of variance risk premium predicting future stock market returns even after controlling for the realized variance.

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