A new time-varying autoregressive stochastic volatility model with \(\alpha \)-stable innovations (TVAR\(\alpha \)SV) is proposed. This new model for time series data combines a time-varying autoregressive component and a stochastic scaling as known from stochastic volatility models with \(\alpha \)-stable distributed noise. Hence, the model can cover extreme events better than classical stochastic volatility models. Furthermore, we develop a Gibbs sampling procedure for the estimation of the model parameters. The procedure is based on the estimation strategy by Kim et al. (Rev Econ Stud 65(3): 361–393, 1998) for classical stochastic volatility models, however, the estimation procedure requires a deliberate approximation of \(\alpha \)-stable distributions by finite mixtures of normal distributions and the application of a simulation smoother for linear Gaussian state space models. A simulation study for the new estimation procedure illustrates the appealing accuracy. Finally, we apply the model to electricity spot price data.

提出了一个具有\（\ alpha \）稳定创新（TVAR \（\ alpha \） SV）的新的时变自回归随机波动率模型。这种用于时间序列数据的新模型结合了时变自回归分量和随机定标，这是从具有（（alpha））稳定分布噪声的随机波动率模型中得知的。因此，与经典随机波动率模型相比，该模型可以更好地覆盖极端事件。此外，我们开发了吉布斯采样程序来估计模型参数。该程序基于Kim等人的估算策略。（Rev Econ Stud 65（3）：361-393，1998），用于经典随机波动率模型，但是，估计程序需要故意近似\（\ alpha \）正态分布的有限混合得到的稳定分布，以及线性高斯状态空间模型的仿真平滑器的应用。针对新估算程序的仿真研究说明了这种吸引人的准确性。最后，我们将该模型应用于电力现货价格数据。