摘要翻译：
金融时间序列表现出许多用简单模型难以解释的有趣性质。这些性质包括价格波动（或收益）分布中的胖尾，在较长的时间尺度上缓慢消除，绝对收益中的强自相关，但收益本身的零自相关，以及多重分形尺度。尽管这些特征的根本原因尚不清楚，但越来越多的证据表明，它们源于波动行为，即价格波动幅度的行为。在本文中，我们提出了一个反馈机制的波动率，密切地再现非平凡性质的经验价格。该模型很简洁，只包含两个易于估计的参数，比标准模型更适合经验数据，并且可以建立在一个简单的框架中，其中波动率的波动是由外生泊松率的估计误差驱动的。
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英文标题：
《A Stochastic Feedback Model for Volatility》
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作者：
Raoul Golan and Austin Gerig
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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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一级分类：Quantitative Finance 数量金融学
二级分类：General Finance 一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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英文摘要：
Financial time series exhibit a number of interesting properties that are difficult to explain with simple models. These properties include fat-tails in the distribution of price fluctuations (or returns) that are slowly removed at longer timescales, strong autocorrelations in absolute returns but zero autocorrelation in returns themselves, and multifractal scaling. Although the underlying cause of these features is unknown, there is growing evidence they originate in the behavior of volatility, i.e., in the behavior of the magnitude of price fluctuations. In this paper, we posit a feedback mechanism for volatility that closely reproduces the non-trivial properties of empirical prices. The model is parsimonious, contains only two parameters that are easily estimated, fits empirical data better than standard models, and can be grounded in a straightforward framework where volatility fluctuations are driven by the estimation error of an exogenous Poisson rate.
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