Technical Proofs: Option Bounds for Short Variance Swaps and the Variance Risk Premium - Adjusting for Skewness

2013 ◽  
Author(s):  
Steven J. Jordan ◽  
Shirley J. Huang
Keyword(s):  
Risk Premium ◽  
Variance Swaps ◽  
Variance Risk Premium ◽  
Variance Risk ◽  
Option Bounds

The Variance Swaps based on KOSPI200 Index and the Term Structure of Variance Risk Premium

2013 ◽  
Vol 21 (4) ◽  
pp. 435-463
Author(s):  
Young Ho Eom ◽  
Woon Wook Jang
Keyword(s):  
Risk Factors ◽  
Term Structure ◽  
Risk Premium ◽  
Risk Averse ◽  
Realized Variance ◽  
Over The Counter ◽  
Variance Swaps ◽  
Variance Swap ◽  
Variance Risk Premium ◽  
Variance Risk

This study examines whether the variance risk is a priced risk factor in Korea using the over-the-counter variance swap quotes and realized variance data. We also study the term structure of variance risk premium. The empirical results show that the model with 2 stochastic variance risk factors with jumps in return is required to fit the variance swap and realized variance data. The analyses with the estimated models suggest that the variance risk premium in Korea are highly negative and the size of the premium increase with the maturities, meaning that risk averse investors in Korea are willing to pay a premium to hedge variance risk.

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 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.

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