No abstract

I develop a new class of closed‐form option pricing models that incorporate variance risk premium and symmetric or asymmetric double exponential jump diffusion. These models decompose the jump component into upward and downward jumps using two independent exponential distributions and thus capture the impact of good and bad news on asset returns and option prices. The empirical results show that the model with an asymmetric double exponential jump diffusion improves the fit on Shanghai Stock Exchange 50ETF returns and options and provides relatively better in‐ and out‐of‐sample pricing performance.

We develop a closed‐form VIX futures valuation formula based on the inverse Gaussian GARCH process by Christoffersen et al. that combines conditional skewness, conditional heteroskedasticity, and a leverage effect. The new model outperforms the benchmark in fitting the S&P 500 returns and the VIX futures prices. The fat‐tailed innovation underlying the model substantially reduced pricing errors during the 2008 financial crisis. The in‐ and out‐of‐sample pricing performance indicates that the new model should be a default modeling choice for pricing the medium‐ and long‐term VIX futures.

The importance of jump clustering is widely recognized in the financial market. We use the Hawkes process to capture jump propagation risks and study their role in modeling the Volatility Index (VIX) term structure. Applying the joint estimation approach to the S&P 500 index and the VIX term structure, we find that incorporating jump propagation risks is important to reconcile the dynamics of joint data and to model the VIX term structure, especially when the market is highly volatile. The long‐term variance factor further improves the description of the VIX term structure for low and medium market volatility levels.

No abstract