摘要翻译：
代理人的异质性被认为是金融波动持续的驱动机制。我们着重研究了投资策略的多层性，将这一概念嵌入到连续时间随机波动率框架中，并从实际数据中证明了一个节省的、两尺度的版本可以有效地捕捉长记忆性。由于在随机波动率模型中估计参数具有挑战性，我们介绍了一种基于广义矩法的鲁棒方法，该方法由启发式选择正交条件支持。除了波动性聚类之外，估计模型还捕捉到了其他相关的程式化事实，成为金融时间序列建模的一个最小但现实和完整的框架。
---
英文标题：
《Stochastic Volatility with Heterogeneous Time Scales》
---
作者：
Danilo Delpini and Giacomo Bormetti
---
最新提交年份：
2013
---
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
--

---
英文摘要：
  Agents' heterogeneity is recognized as a driver mechanism for the persistence of financial volatility. We focus on the multiplicity of investment strategies' horizons, we embed this concept in a continuous time stochastic volatility framework and prove that a parsimonious, two-scale version effectively captures the long memory as measured from the real data. Since estimating parameters in a stochastic volatility model is challenging, we introduce a robust methodology based on the Generalized Method of Moments supported by a heuristic selection of the orthogonal conditions. In addition to the volatility clustering, the estimated model also captures other relevant stylized facts, emerging as a minimal but realistic and complete framework for modelling financial time series.
---
PDF链接：
https://arxiv.org/pdf/1206.0026