英文标题：
《Bayesian prediction of jumps in large panels of time series data》
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作者：
Angelos Alexopoulos, Petros Dellaportas, Omiros Papaspiliopoulos
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最新提交年份：
2021
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英文摘要：
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.
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中文摘要：
我们以一种新的视角来看待每日股票回报的波动性和跳跃过程。我们首先为具有泊松驱动跳跃的单变量随机波动率模型提供了一个计算框架，为现有工具提供了一种竞争性推理的替代方法。然后将该方法推广到一大组股票，我们假设它们未观察到的跳跃强度通过动态因子模型在时间上共同演化。为了评估所提出的建模方法，我们进行了样本外预测，并比较了从不同模型获得的后验预测分布。我们提供的证据表明，跳跃的联合建模提高了随机波动率模型的预测能力。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Statistics 统计学
二级分类：Computation 计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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