摘要翻译：
将Calvet和Fisher(2004)的MSM随机波动率模型应用于久期设置，引入了马尔可夫切换多重分形久期(MSMD)模型。尽管MSMD过程是指数的$\beta$-混合，但它能够产生高度持久的自相关。我们通过分析和模拟研究了MSMD过程产生的持续时间的这一特征如何传播到计数和实现的波动率。在Whittle近似的基础上，给出了MSMD参数的拟极大似然估计，并对一般MSMD规范建立了强相合性和渐近正态性。我们证明Whittle估计是最大似然估计的一种计算简单和快速的替代方法。最后，我们在一个基于三个主要外汇期货合约价格持续时间的样本外预测练习中，比较了MSMD模型与竞争的短记忆持续时间模型和长记忆持续时间模型的性能。结果表明，MSMD模型和LMSD模型性能相似，优于短记忆ACD模型。
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英文标题：
《Modeling and Forecasting Persistent Financial Durations》
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作者：
Filip Zikes, Jozef Barunik, Nikhil Shenai
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最新提交年份：
2013
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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 introduces the Markov-Switching Multifractal Duration (MSMD) model by adapting the MSM stochastic volatility model of Calvet and Fisher (2004) to the duration setting. Although the MSMD process is exponential $\beta$-mixing as we show in the paper, it is capable of generating highly persistent autocorrelation. We study analytically and by simulation how this feature of durations generated by the MSMD process propagates to counts and realized volatility. We employ a quasi-maximum likelihood estimator of the MSMD parameters based on the Whittle approximation and establish its strong consistency and asymptotic normality for general MSMD specifications. We show that the Whittle estimation is a computationally simple and fast alternative to maximum likelihood. Finally, we compare the performance of the MSMD model with competing short- and long-memory duration models in an out-of-sample forecasting exercise based on price durations of three major foreign exchange futures contracts. The results of the comparison show that the MSMD and LMSD perform similarly and are superior to the short-memory ACD models.
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PDF链接：
https://arxiv.org/pdf/1208.3087