英文标题：
《The stabilizing effect of volatility in financial markets》
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作者：
Davide Valenti, Giorgio Fazio, Bernardo Spagnolo
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最新提交年份：
2017
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英文摘要：
  In financial markets, greater volatility is usually considered synonym of greater risk and instability. However, large market downturns and upturns are often preceded by long periods where price returns exhibit only small fluctuations. To investigate this surprising feature, here we propose using the mean first hitting time, i.e. the average time a stock return takes to undergo for the first time a large negative or positive variation, as an indicator of price stability, and relate this to a standard measure of volatility. In an empirical analysis of daily returns for $1071$ stocks traded in the New York Stock Exchange, we find that this measure of stability displays nonmonotonic behavior, with a maximum, as a function of volatility. Also, we show that the statistical properties of the empirical data can be reproduced by a nonlinear Heston model. This analysis implies that, contrary to conventional wisdom, not only high, but also low volatility values can be associated with higher instability in financial markets.
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中文摘要：
在金融市场中，较大的波动性通常被认为是较大风险和不稳定性的同义词。然而，在市场大幅下跌和上涨之前，往往会有很长一段时间，价格回报率只会出现很小的波动。为了研究这一令人惊讶的特征，我们建议使用平均首次命中时间，即股票收益率第一次经历巨大的负或正变化所需的平均时间，作为价格稳定性的指标，并将其与波动性的标准度量相关联。在对纽约证券交易所1071美元股票的日收益率进行的实证分析中，我们发现，作为波动率的函数，这种稳定性指标表现出非单调行为，具有最大值。此外，我们还表明，经验数据的统计特性可以通过非线性Heston模型再现。这一分析表明，与传统观点相反，不仅高波动率值，而且低波动率值都可能与金融市场的更高不稳定性相关。
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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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