解耦随机波动率的短期和长期行为

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英文标题：
《Decoupling the short- and long-term behavior of stochastic volatility》
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作者：
Mikkel Bennedsen, Asger Lunde, Mikko S. Pakkanen
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最新提交年份：
2021
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英文摘要：
  We introduce a new class of continuous-time models of the stochastic volatility of asset prices. The models can simultaneously incorporate roughness and slowly decaying autocorrelations, including proper long memory, which are two stylized facts often found in volatility data. Our prime model is based on the so-called Brownian semistationary process and we derive a number of theoretical properties of this process, relevant to volatility modeling. Applying the models to realized volatility measures covering a vast panel of assets, we find evidence consistent with the hypothesis that time series of realized measures of volatility are both rough and very persistent. Lastly, we illustrate the utility of the models in an extensive forecasting study; we find that the models proposed in this paper outperform a wide array of benchmarks considerably, indicating that it pays off to exploit both roughness and persistence in volatility forecasting.
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中文摘要：
我们引入了一类新的资产价格随机波动的连续时间模型。这些模型可以同时包含粗糙和缓慢衰减的自相关，包括适当的长记忆，这是波动率数据中经常发现的两个典型事实。我们的素数模型基于所谓的布朗半平稳过程，我们推导了该过程的一些理论性质，与波动率建模相关。将这些模型应用于涵盖大量资产的已实现波动率度量，我们发现证据与以下假设一致：已实现波动率度量的时间序列既粗糙又非常持久。最后，我们举例说明了模型在广泛预测研究中的实用性；我们发现，本文提出的模型在很大程度上优于一系列基准，这表明在波动率预测中利用粗糙度和持久性是值得的。
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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：Mathematical Finance        数学金融学
分类描述：Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法，包括随机、概率和泛函分析、代数、几何和其他方法
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关键词：波动率 Mathematical Quantitative correlations Econophysics