scholarly journals Risk, Uncertainty, and Expected Returns

2016 ◽  
Vol 51 (3) ◽  
pp. 707-735 ◽  
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.

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

2018 ◽  
Vol 21 (06) ◽  
pp. 1850043 ◽  
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.

Financial Uncertainty with Ambiguity and Learning

2021 ◽  
Author(s):  
Hening Liu ◽  
Yuzhao Zhang
Keyword(s):  
Risk Premium ◽  
Regime Switching ◽  
Ambiguity Aversion ◽  
Volatility Risk ◽  
Asset Pricing Model ◽  
Variance Risk Premium ◽  
Financial Uncertainty ◽  
Empirical Relations ◽  
The Mean ◽  
Variance Risk

We examine a production-based asset pricing model with regime-switching productivity growth, learning, and ambiguity. Both the mean and volatility of the growth rate of productivity are assumed to follow a Markov chain with an unobservable state. The agent’s preferences are characterized by the generalized recursive smooth ambiguity utility function. Our calibrated benchmark model with modest risk aversion can match moments of the variance risk premium in the data and reconcile empirical relations between the risk-neutral variance and macroeconomic quantities and their respective volatilities. We show that the interplay between productivity volatility risk and ambiguity aversion is important for pricing variance risk in returns. This paper was accepted by Tomasz Piskorski, finance.

scholarly journals Pricing Liquidity Risk with Heterogeneous Investment Horizons

2020 ◽  
pp. 1-36 ◽  
Author(s):  
Alessandro Beber ◽  
Joost Driessen ◽  
Anthony Neuberger ◽  
Patrick Tuijp
Keyword(s):  
Stock Returns ◽  
Risk Premium ◽  
Liquidity Risk ◽  
Asset Returns ◽  
Spillover Effects ◽  
Expected Returns ◽  
Pricing Model ◽  
Liquidity Premium ◽  
Asset Pricing Model ◽  
Illiquid Asset

We develop an asset pricing model with stochastic transaction costs and investors with heterogeneous horizons. Depending on their horizon, investors hold different sets of assets in equilibrium. This generates segmentation and spillover effects for expected returns, where the liquidity (risk) premium of illiquid assets is determined by investor horizons and the correlation between liquid and illiquid asset returns. We estimate our model for the cross-section of U.S. stock returns and find that it generates a good fit, mainly due to a combination of a substantial expected liquidity premium and segmentation effects, while the liquidity risk premium is small.

scholarly journals Variance risk premia for agricultural commodities

2019 ◽  
Vol 79 (3) ◽  
pp. 286-303
Author(s):  
Wenwen Xi ◽  
Dermot Hayes ◽  
Sergio Horacio Lence
Keyword(s):  
Risk Premium ◽  
Price Volatility ◽  
Risk Premia ◽  
Content Type ◽  
Risk Premiums ◽  
Variance Risk Premium ◽  
Revenue Insurance ◽  
Risk Neutral ◽  
Variance Risk ◽  
Practical Implications

Purpose The purpose of this paper is to study the variance risk premium in corn and soybean markets, where the variance risk premium is defined as the difference between the historical realized variance and the corresponding risk-neutral expected variance. Design/methodology/approach The authors compute variance risk premiums using historical derivatives data. The authors use regression analysis and time series econometrics methods, including EGARCH and the Kalman filter, to analyze variance risk premiums. Findings There are moderate commonalities in variance within the agricultural sector, but fairly weak commonalities between the agricultural and the equity sectors. Corn and soybean variance risk premia in dollar terms are time-varying and correlated with the risk-neutral expected variance. In contrast, agricultural commodity variance risk premia in log return terms are more likely to be constant and less correlated with the log risk-neutral expected variance. Variance and price (return) risk premia in agricultural markets are weakly correlated, and the correlation depends on the sign of the returns in the underlying commodity. Practical implications Commodity variance (i.e. volatility) risk cannot be hedged using futures markets. The results have practical implications for US crop insurance programs because the implied volatilities from the relevant options markets are used to estimate the price volatility factors used to generate premia for revenue insurance products such as “Revenue Protection” and “Revenue Protection with Harvest Price Exclusion.” The variance risk premia found implies that revenue insurance premia are overpriced. Originality/value The empirical results suggest that the implied volatilities in corn and soybean futures market overestimate true expected volatility by approximately 15 percent. This has implications for derivative products, such as revenue insurance, that use these implied volatilities to calculate fair premia.

scholarly journals Option-Based Estimation of the Price of Coskewness and Cokurtosis Risk

2020 ◽  
pp. 1-27
Author(s):  
Peter Christoffersen ◽  
Mathieu Fournier ◽  
Kris Jacobs ◽  
Mehdi Karoui
Keyword(s):  
Risk Premium ◽  
Risk Premia ◽  
Factor Models ◽  
Option Prices ◽  
Variance Risk Premium ◽  
Market Return ◽  
Price Of Risk ◽  
Variance Risk ◽  
Index Option

Abstract We show that the prices of risk for factors that are nonlinear in the market return can be obtained using index option prices. The price of coskewness risk corresponds to the market variance risk premium, and the price of cokurtosis risk corresponds to the market skewness risk premium. Option-based estimates of the prices of risk lead to reasonable values of the associated risk premia. An analysis of factor models with coskewness risk indicates that the new estimates of the price of risk improve the models’ performance compared with regression-based estimates.

scholarly journals Inflation Risk Premia, Yield Volatility, and Macro Factors

2018 ◽  
Vol 17 (3) ◽  
pp. 397-431 ◽  
Author(s):  
Andrea Berardi ◽  
Alberto Plazzi
Keyword(s):  
Term Structure ◽  
Risk Premium ◽  
Implied Volatility ◽  
Risk Premia ◽  
Output Growth ◽  
Inflation Risk ◽  
Variance Risk Premium ◽  
Real Risk ◽  
Variance Risk ◽  
Inflation Risk Premia

Abstract We incorporate a latent stochastic volatility factor and macroeconomic expectations in an affine model for the term structure of nominal and real rates. We estimate the model over 1999–2016 on U.S. data for nominal and TIPS yields, the realized and implied volatility of T-bonds, and survey forecasts of GDP growth and inflation. We find relatively stable inflation risk premia averaging at 40 basis points at the long-end, and which are strongly related to the volatility factor and conditional mean of output growth. We also document real risk premia that turn negative in the post-crisis period, and a non-negligible variance risk premium.

Information Content of Skewness Risk Premium

2018 ◽  
Vol 26 (4) ◽  
pp. 391-423
Author(s):  
Seok Goo Nam ◽  
Byung Jin Kang
Keyword(s):  
Stock Returns ◽  
Risk Premium ◽  
Asset Returns ◽  
Investment Strategy ◽  
Investment Strategies ◽  
Option Prices ◽  
Underlying Asset ◽  
Variance Risk Premium ◽  
The Difference ◽  
Variance Risk

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.

Sign in / Sign up

Export Citation Format

Share Document