We decompose total variance into its bad and good components and measure the premia associated with their fluctuations using stock and option data from a large cross-section of firms.
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.