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Bayesian modeling and forecasting of 24-hour high-frequency volatility

机译：24小时高频波动的贝叶斯建模与预测

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摘要

Examples of situations are discussed where financial crises provide a rich information source to learn about asset price dynamics and models used to capture these dynamics. In this paper, it has been proposed to develop models of 24-hr intraday equity index returns during and after the recent financial crisis. The high-frequency index futures returns of the models are estimated using around-the-clock 5-min returns that incorporate the key features such as multiple persistent stochastic volatility factors, jumps in prices and volatilities, seasonal components capturing time of the day patterns, correlations between return and volatility shocks, and announcement effects. In this regard, an integrated MCMC approach is used to estimate interday and intraday parameters with high-frequency data without resorting to various aggregation measures like realized volatility. A case study is provided using a two-year financial crisis data. Here, the particle filters are used to construct likelihood functions for model comparison and out-of-sample forecasting for the next three years. It is shown that the proposed approach improves realized volatility forecasts over to that of existing benchmarks and is also useful for risk management and trading applications. (42 refs.)
机译：讨论了一些例子，其中金融危机提供了丰富的信息源，以了解资产价格动态以及用于捕获这些动态的模型。在本文中，已经提出了在最近的金融危机期间和之后开发24小时盘中股票指数收益的模型。该模型的高频指数期货收益是使用全天候5分钟收益估算的，该收益包含关键特征，例如多个持续的随机波动性因素，价格和波动性的上涨，捕获每日时间的季节性成分，收益率和波动率冲击之间的相关性，以及公告效应。在这方面，集成的MCMC方法用于估计具有高频数据的日间和日内参数，而无需求助于诸如已实现的波动之类的各种汇总措施。案例研究使用了两年的金融危机数据。在这里，粒子滤波器用于构造似然函数，用于未来三年的模型比较和样本外预测。结果表明，所提出的方法将已实现的波动预测提高到了现有基准之上，并且对于风险管理和交易应用也很有用。 （42篇参考文献）

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