Bayesian Modeling and Forecasting of 24-Hour High-Frequency Volatility
Bayesian Modeling and Forecasting of 24-Hour High-Frequency Volatility
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24小时高频波动率的贝叶斯建模与预测
DOI：
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发表时间：
2012
期刊：
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通讯作者：
M. Johannes
中科院分区：
文献类型：
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作者：
Jonathan R. Stroud;M. Johannes
本文估计模型的高频指数期货回报率使用“全天候”5分钟回报，包括以下主要特征：多个持续的随机波动性因素，跳跃的价格和波动，季节性成分捕捉时间的一天的模式，回报和波动冲击之间的相关性，和公告效应。我们开发了一个综合的MCMC方法来估计日间和日内的参数和状态，使用高频数据，而不诉诸各种聚合措施，如已实现的波动。我们提供了一个案例研究，使用金融危机的数据从2007年到2009年，并使用粒子滤波器构建似然函数的模型比较和样本外预测从2009年到2012年。我们表明，我们的方法提高了已实现的波动率预测高达50%，超过现有的基准，也是有用的风险管理和交易应用程序。本文的补充材料可在网上查阅。
This article estimates models of high-frequency index futures returns using “around-the-clock” 5-min returns that incorporate the following key features: 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. We develop an integrated MCMC approach to estimate interday and intraday parameters and states using high-frequency data without resorting to various aggregation measures like realized volatility. We provide a case study using financial crisis data from 2007 to 2009, and use particle filters to construct likelihood functions for model comparison and out-of-sample forecasting from 2009 to 2012. We show that our approach improves realized volatility forecasts by up to 50% over existing benchmarks and is also useful for risk management and trading applications. Supplementary materials for this article are available online.