This paper provides a novel methodology for estimating option pricing models based on risk-neutral moments. We synthesize the distribution extracted from a panel of option prices and exploit linear relationships between risk-neutral cumulants and latent factors within the continuous time affine stochastic volatility framework.
Advances in variance analysis permit the splitting of the total quadratic variation of a
jump diffusion process into upside and downside components. Recent studies establish
that this decomposition enhances volatility predictions, and highlight the
upside/downside variance spread as a driver of the asymmetry in stock price
distributions.
We decompose the variance risk premium into upside and downside variance risk premia. These components reflect market compensation for changes in good and bad uncertainties. Their difference is a measure of the skewness risk premium (SRP), which captures asymmetric views on favorable versus undesirable risks.
