On the Normal Inverse Gaussian Stochastic Volatility Model
扩展了Barndorff-Nielsen的正态逆高斯随机波动率模型，使其滞后结构更灵活，推导了二阶和四阶矩，并通过模拟和实际股票、汇率数据检验了模型拟合与预测性能。
In this article, the normal inverse Gaussian stochastic volatility model of Barndorff-Nielsen is extended. The resulting model has a more flexible lag structure than the original one. In addition, the second-and fourth-order moments, important properties of a volatility model, are derived. The model can be considered either as a generalized autoregressive conditional heteroscedasticity model with nonnormal errors or as a stochastic volatility model with an inverse Gaussian distributed conditional variance. A simulation study is made to investigate the performance of the maximum likelihood estimator of the model. Finally, the model is applied to stock returns and exchange-rate movements. Its fit to two stylized facts and its forecasting performance is compared with two other volatility models.