scholarly journals Jump-Diffusion Long-Run Risks Models, Variance Risk Premium, and Volatility Dynamics

2013 ◽  
Author(s):  
Jianjian Jin
2018 ◽  
Vol 10 (1) ◽  
pp. 481-497 ◽  
Author(s):  
Hao Zhou

This article reviews the predictability evidence on the variance risk premium: ( a) It predicts significant positive risk premia across equity, bond, currency, and credit markets; ( b) the predictability peaks at few-month horizons and dies out afterward; ( c) such a short-run predictability is complementary to the long-run predictability offered by the price-to-earnings ratio, forward rate, interest differential, and leverage ratio. Several structural approaches based on the notion of economic uncertainty are discussed for generating these stylized facts about the variance risk premium, which has broad implications for various empirical asset pricing puzzles.


2021 ◽  
Author(s):  
Hening Liu ◽  
Yuzhao Zhang

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.


2019 ◽  
Vol 79 (3) ◽  
pp. 286-303
Author(s):  
Wenwen Xi ◽  
Dermot Hayes ◽  
Sergio Horacio Lence

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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