Extreme Dependencies and the Idiosyncratic Volatility Puzzle

在前一个项目的背景下，我们可以证明：(I)对市场表现出强烈极端依赖性的股票(即对崩盘敏感的股票)提供显著的正回报溢价，(Ii)对流动性也可以观察到类似的影响，以及(Iii)回报模式在国际上也适用。根据已开发的基于Copulas的衡量系统风险的方法-允许对非线性依赖关系进行灵活建模-这笔续期赠款的项目将侧重于特殊的波动性。由于一只股票的总收益波动率由其系统成分和特殊成分组成，这分别是初始项目的自然延续和延伸。如果系统性风险的衡量标准和程度发生变化，那么针对给定总波动率水平的特殊波动率也必然会发生变化。在新项目的背景下，将分析以下两个具体的子项目：(1)基于Copula的特殊波动率和特殊波动率谜题(IVP)：我们想要分析以传统方式衡量的特殊波动率(即不是通过对风险因素的线性敞口来解释的风险)是否不同于基于Copula的系统(最终是特殊的)波动率的更精确测量的情况下的特殊波动率。此外，我们还想分析股票收益率与特质波动率之间理论上意想不到的负相关关系(IVP)是否源于之前工作中对特质波动率的错误测量，以及一旦我们使用新提出的基于Copula的特质波动率衡量标准，这种负相关关系是否会被降低。(2)每日股票排名和独立投资计划：许多报章和网站定期刊登每日回报率最高和最低的股票排名，因此读者非常容易看到。我们预计潜在投资者的注意力将被驱使到这些股票上。因此，在第二个子项目中，我们想要分析出现在每日回报排名中的股票(无论它们出现在最高排名还是最低排名中)是否由于它们引起的关注而在短期内被高估，并随后与没有出现在每日排名中的股票相比呈现负的异常回报。此外，我们想要分析这种潜在的模式是否也有助于解决IVP。这并不是不可能，因为出现在每日排名中的股票可能也表现出更高的波动性。对IVP的建议分析可以使用传统的或我们新开发的基于Copula的特殊波动率指标进行，因此这里建议的分析也与第一个分项目密切相关。

In the context of the previous project, we could show (i) that stocks that exhibit strong extreme dependence with the market (i.e. crash-sensitive stocks) deliver a pronounced positive return premium, (ii) that similar effects can be observed for liquidity, and (iii) that the return patterns also hold internationally. Based on the developed methodology to measure systeamtic risk based on copulas---that allows for a flexible modelling of non-linear dependencies---the projects from this continution grant will focus on idiosyncratic volatility. As the total return volatility of a stock is comprised of its systematic and its idiosyncratic component, this is a natural continution and extension, respectively, of the initial project. If the measurement and extent of systematic risk changes, the amount of idiosyncratic volatility for a given level of total volatility necessarily also has to change. In the context of the new project, the following two specific subprojects will be analyzed: (1) Copula-based idiosyncratic volatility and the idiosyncratic volatility puzzle (IVP): We want to analyze whether idiosyncratic volatility measured in the traditional way (i.e. as risk not explained by linear exposures to risk factors) is different from idiosyncratic volatility for the case of a more precise copula-based measurement of systematic (and eventually idiosyncratic) volatility. Furthermore, we then want to analyze whether the theoretically unexpected negative relationship between stock returns and idiosyncratic volatility (the IVP) is due to the incorrect measurement of idiosyncratic volatility in previous work and whether it is reduced once we use our newly suggested copula-based measure of idiosyncratic volatility. (2) Daily stock rankings and the IVP: Many newspapers and websites regularly publish rankings of the stocks with the highest and lowest daily returns and are thus very visible for readers. We expect that the attention of potential investors is driven towards these stocks. In the second sub-project we thus want to analyze whether stocks that appear in daily return rankings (irrespective of whether they appear in top- or bottom-rankings) are overvalued in the short-term due to the attention they raise and subsequently exhibit negative abnormal returns as compared to stocks that do not appear in the daily rankings. Furthermore, we want to analyze whether such a potential pattern could also contribute in sovling the IVP. This is not unlikely as stocks that appear in the daily rankings probably also exhibit higher idiosyncrativ volatility. The suggested analyses of the IVP can be conducted with traditional or our newly developed, copula-based measures for idiosyncratic volatility and the analysis suggested here is thus also closely linked to the first subproject.