The NIG-S&ARCH Model: a fat tailed, stochastic, and autoregressive conditional heteroskedastic volatility model

This paper examines the capabilities of the Normal Inverse Gaussian distribution as a model for stock returns. We extend the model of Barndorff-Nielsen (1997) to allow for a richer volatility structure and compare with the existing GARCH-type models. We conclude that the proportional model outperforms some of the most praised GARCH-M models. In particular, we make a big gain in modelling the skewness of equity returns