摘要翻译：
本文提出了一种利用高频数据进行波动率建模和预测的改进方法。利用基于已实现GARCH的多时频分解已实现波动率测度的预测模型，研究了不同时间尺度对波动率预测的影响。将波动率分解为几个时间尺度，近似于交易者在相应投资水平上的行为。此外，由于最近提出的跳跃小波两尺度实现的波动率估计器，所提出的方法能够考虑跳跃的影响。我们提出了一个基于最大似然估计和观测驱动的广义自回归评分估计框架的两个版本的Jump-GARCH模型。我们用几种流行的已实现波动率度量方法对涵盖最近金融危机的外汇期货数据进行了预测比较。我们的结果表明，从积分变分中分离跳跃变分对于预测性能是很重要的。它的多尺度分解也提供了对波动过程的有趣见解。我们发现，未来波动率的大部分信息来自于代表非常短投资期限的高频部分。我们新提出的模型在单日和多期预测方面都优于流行的和传统的模型。
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英文标题：
《Modeling and forecasting exchange rate volatility in time-frequency
  domain》
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作者：
Jozef Barunik and Tomas Krehlik and Lukas Vacha
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最新提交年份：
2015
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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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英文摘要：
  This paper proposes an enhanced approach to modeling and forecasting volatility using high frequency data. Using a forecasting model based on Realized GARCH with multiple time-frequency decomposed realized volatility measures, we study the influence of different timescales on volatility forecasts. The decomposition of volatility into several timescales approximates the behaviour of traders at corresponding investment horizons. The proposed methodology is moreover able to account for impact of jumps due to a recently proposed jump wavelet two scale realized volatility estimator. We propose a realized Jump-GARCH models estimated in two versions using maximum likelihood as well as observation-driven estimation framework of generalized autoregressive score. We compare forecasts using several popular realized volatility measures on foreign exchange rate futures data covering the recent financial crisis. Our results indicate that disentangling jump variation from the integrated variation is important for forecasting performance. An interesting insight into the volatility process is also provided by its multiscale decomposition. We find that most of the information for future volatility comes from high frequency part of the spectra representing very short investment horizons. Our newly proposed models outperform statistically the popular as well conventional models in both one-day and multi-period-ahead forecasting.
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PDF链接：
https://arxiv.org/pdf/1204.1452