We take a new look at the problem of disentangling the volatility and jumps processes of daily stock returns. We first provide a computational framework for the univariate stochastic volatility model with Poisson-driven jumps that offers a competitive inference alternative to the existing tools. This methodology is then extended to a large set of stocks for which we assume that their unobserved jump intensities co-evolve in time through a dynamic factor model. To evaluate the proposed modelling approach we conduct out-of-sample forecasts and we compare the posterior predictive distributions obtained from the different models. We provide evidence that joint modelling of jumps improves the predictive ability of the stochastic volatility models.
翻译:我们重新审视了使每日股票回报的波动和跳跃过程脱钩的问题。 我们首先为单象学随机波动模型提供了一个计算框架, 由Poisson驱动的跳跃提供了一种替代现有工具的竞争性推论。 然后, 这种方法推广到大量库存, 我们假设这些未观察到的跃升强度通过一个动态要素模型而及时同时发生。 为了评价我们进行的模拟方法, 我们进行了抽样外预测, 我们比较从不同模型中获得的后方预测分布。 我们提供了证据, 跳跃联合建模可以提高蒸发性波动模型的预测能力。