Stock Return Extrapolation, Option Prices, and Variance Risk Premium

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.

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.

scholarly journals Stock Return Predictability and Variance Risk Premia: Statistical Inference and International Evidence

2014 ◽  
Vol 49 (3) ◽  
pp. 633-661 ◽  
Author(s):  
Tim Bollerslev ◽  
James Marrone ◽  
Lai Xu ◽  
Hao Zhou
Keyword(s):  
Risk Premium ◽  
The United States ◽  
Finite Sample ◽  
Market Returns ◽  
Stock Return Predictability ◽  
Stock Market Returns ◽  
Variance Risk Premium ◽  
The United Kingdom ◽  
Panel Regressions ◽  
Variance Risk

AbstractRecent empirical evidence suggests that the variance risk premium predicts aggregate stock market returns. We demonstrate that statistical finite sample biases cannot “explain” this apparent predictability. Further corroborating the existing evidence of the United States, we show that country-specific regressions for France, Germany, Japan, Switzerland, the Netherlands, Belgium, and the United Kingdom result in quite similar patterns. Defining a “global” variance risk premium, we uncover even stronger predictability and almost identical cross-country patterns through the use of panel regressions.

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.

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.

Investor sentiment, variance risk premium and delta-hedged gains

2016 ◽  
Vol 48 (31) ◽  
pp. 2952-2964 ◽  
Author(s):  
Yankun Chen ◽  
Jinghong Shu ◽  
Jin E. Zhang
Keyword(s):  
Risk Premium ◽  
Investor Sentiment ◽  
Variance Risk Premium ◽  
Variance Risk

scholarly journals Stock Returns and The Weather Effect: the Case of Ghana

2020 ◽  
Author(s):  
Sampson Atuahene ◽  
Kong Yusheng ◽  
Geoffrey Bentum-Micah
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Exchange ◽  
Investor Sentiment ◽  
Weather Conditions ◽  
Market Returns ◽  
Stock Market Returns ◽  
Economic Significance ◽  
Weather Changes ◽  
Brokerage Firms

In every economy, Stock markets are part of the key elements the build it up. A few decades ago, there has been a significant change in Ghana stock market returns (GSE). Our study examines the statistical and economic significance of investor sentiment, based on weather conditions/changes, on stock market returns. OLS models, assisted by unit root tests were employed in analyzing the data obtained from the Ghana stock exchange platform from 2000 to 2017. From our literature review, we discovered that investors’ perceptions play a central role in finalizing the direction of stock market returns. Regarding our empirical results, we tested whether weather variations influence the investment decisions of investors; we discovered that temperature and cloud cover significantly influences stock market returns. This is because of mood changes is associated with weather conditions variations. However, sunshine per our regression coefficient shows a statistically insignificant impact on investors’ investment choices. Precipitation to a large extend influence stock market activities further affecting its results negatively as our regression results depicted. We concluded stock brokerage firms, companies, and investors (foreign/local) must incorporate weather changes/effects when strategizing about their investment outcomes.

The Variation in Variance Risk Premium and its Predictive Power: Evidence from Option Market Sentiments

2020 ◽  
Vol 10 (03) ◽  
pp. 2050010
Author(s):  
Y. Peter Chung ◽  
Sun-Joong Yoon
Keyword(s):  
Risk Premium ◽  
Predictive Power ◽  
Investor Sentiment ◽  
Individual Investors ◽  
Trading Behavior ◽  
Variance Risk Premium ◽  
The Poor ◽  
Variance Risk ◽  
Return Dynamics ◽  
The U.S

We show that the highly volatile variance risk premium (VRP) can be theoretically and empirically reconciled with investor sentiment captured by temporary variation in risk aversion. In an effort to understand the poor predictive power of the VRP in non-U.S. markets, we propose a new investor sentiment index, the Variance Sentiment Index(VSI), obtained from the trading behavior of individual investors. We show that the VSI predicts local return dynamics, in a similar way to what the VRP does in the U.S. market. Moreover, the VSI does not lose its predictive power even in the presence of the global VRP.

scholarly journals The Nonlinear Relationship between Investor Sentiment, Stock Return, and Volatility

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Gang He ◽  
Shuzhen Zhu ◽  
Haifeng Gu
Keyword(s):  
Stock Market ◽  
Stock Return ◽  
Investor Sentiment ◽  
Chinese Stock Market ◽  
Stock Market Volatility ◽  
Market Returns ◽  
Stock Market Returns ◽  
Bear Market ◽  
Stock Market Return ◽  
Space Effect

Based on the DSSW model, we analyze the nonlinear impact mechanism of investor sentiment on stock return and volatility by adjusting its hypothesis in Chinese stock market. We examine the relationship between investor sentiment, stock return, and volatility by applying OLS regression and quantile regression. Our empirical results show that the effects of investor sentiment on stock market return are asymmetric. There is “Freedman effect” in Chinese stock market, but only optimistic sentiment has a significant nonlinear impact on stock market returns when the stock market is a balanced market or a bear market. Meanwhile, “create the space effect” does exist in Chinese stock market too. It only exists when the market is in equilibrium, and only pessimistic sentiment has the nonlinear effect on stock market volatility.

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