Variance Risk Premiums And Its Risk Factors

It has been well documented that return variance is stochastic. When investing a security, an investor faces at least two sources of uncertainty, the uncertaint y generated by the volatile of return as well as the volatility itself. The corres ponding volatility risk premium must exist once volatility has been seen as a r esource of risk, and this fact accounts for the inconsistency of asset price dyna mics under alternative circumstances of risk neutral measure and physical meas ure. Hence how to precisely extract volatility risk premium and investigate the contagion between volatility risks premiums of multiple markets will provides a new perspective to discuss the price discovery problem.What’s more, in order to discuss which factor could affect the volatility risk p remium,this paper proposes a functional coefficient regression technique to esti mate time-varying betas and alpha in the conditional capital asset pricing mode1of the volatility risk premium. Functional coefficient representation relaxes the strict assumptions on the structure of betas and alpha by combining the predi ctors into an index that best captures time variations in the betas and alpha an d that estimates them nonparametrically. This index of betas and alpha is usefu1to determine which economic variables we should track and more importantly, in what combination, and an appropriate index variable is selected by applyin g the smoothly clipped absolute deviation penalty to index coefficients. Throug h this method, we can easily know what are the major variables we should fo cus to influence the volatility risk premium and the capital market.In this paper, we want to find out whether the macroeconomic variables could affect the volatility risk premium. To my best knowledge, this is the first pap er that using functional coefficient regression technique to estimate the affectin g variables of volatility risk premium, and fortunately, we got the result we ex actly wanted.