Structural Models of Volatility

多元波动率或相关性模型的发展已经成为金融学中一个迅速发展的分支，无论是在理论上还是在投资组合配置和优化、投资组合风险评估和资产定价等应用领域。这一发展尤其是由于引入了具有条件异方差性（MG）的多变量模型。在传递关于潜在波动动态的深刻信息的同时，MGREST模型，然而，在大多数研究中，冲击传递的潜在模型缺乏严格结构意义上的识别，在这个国际项目中，我们建立在识别宏观计量经济学结构向量自回归模型的最新进展的基础上，并提出两种不同的方法来识别多变量Gestival类型的结构随机波动率模型。我们从两个角度出发。一方面，我们研究了一个纯粹的统计方法，从假设，投机回报的二阶动态可以追溯到独特的和独立的结构性冲击。从这个假设，我们得到的时刻条件，确定结构MGC 3D模型。我们研究的方法在静态设置和动态，考虑到潜在的结构变化。另一方面，我们通过利用新闻分析数据固有的信息来进行识别。在第三步中，申请人和他们的研究小组将整合他们的见解，将两个方案应用于两个主要的实证研究领域：2008/2009年的石油价格冲击和银行危机。一方面，外部信息预计有助于对统计确定的冲击进行经济标记。另一方面，独立冲击和工具冲击的系统比较可以为后者增加坚实的概念支持，当涉及到通过共同脉冲响应的描述性分析时，通过构造，依赖于孤立的（即在非高斯情况下独立的）单位冲击的假设。 我们计划通过用于结构性波动模型分析的R框架向其他研究人员提供我们的研究。在此基础上，一个交互式的Web应用程序演示了检测，识别和可视化的结构变化的波动性传输与实时数据。通过开发开源研究软件，我们的目标是传播我们的研究成果，并开辟与科学界的新沟通渠道。

The development of multivariate volatility or correlation models has become a rapidly growing branch in finance, both in theory and in the applied fields of portfolio allocation and optimization, portfolio risk evaluation, and asset pricing. This development has been spurred especially by the introduction of multivariate models featuring conditional heteroskedasticity (MGARCH). While conveying insightful information about the underlying volatility dynamics, MGARCH models are, however, limited in the sense that in most studies the underlying model of shock transmissions lacks identification in a strictly structural sense.In this international project we build upon recent advances in identifying macroeconometric structural vector autoregressive models, and develop two alternative ways to identify structural stochastic volatility models of the multivariate GARCH type. We proceed from two perspectives. On the one hand, we study a purely statistical approach that proceeds from the assumption that second order dynamics of speculative returns can be traced back to unique and independent structural shocks. From this assumption, we derive moment conditions that identify the structural MGARCH model. We study the method in a static setting and dynamically, taking into account potential structural shifts. On the other hand, we approach identification by exploiting the information inherent to news analytics data. In a third step, both applicants and their research groups will integrate their insights in applying the two schemes to two major fields of empirical research: oil price shocks and the banking crisis in 2008/2009. On the one hand external information is expected helpful for the economic labeling of statistically identified shocks. On the other hand a systematic comparison of independent and instrumental shocks could add a solid conceptual support for the latter when it comes to the descriptive analysis by means of common impulse responses which, by construction, rely on the assumption of isolated (i.e. independent in the non-Gaussian case) unit shocks. We plan to provide our research via an R framework for the analysis of structural volatility models to other researchers. Based on this, an interactive web application demonstrates the detection, identification and visualization of structural changes in volatility transmissions with real-time data. By developing open source research software, we aim to disseminate our research and open up a new communication channel with the scientific community.