Flexible and robust mixture models for the identification of structural shocks in financial time series

向量自回归模型中的结构效应是模型经济内容的重要体现，因此，识别向量自回归模型中的结构效应是多元时间序列分析中的一个重要问题。一个重要的例子是分析金融市场冲击的传导和（可能）蔓延效应。我们可以测量资产之间的相关性，并观察到这些相关性在牛市和熊市时期往往有很大的不同。然而，风险管理者和决策者通常需要更多的信息。也就是说，他们需要知道特定市场的冲击有多强，以什么方向传递到其他市场，以及冲击传递的模式在繁荣和危机时期是否相同。本研究的目的是扩展和开发新的方法，以帮助识别结构性冲击，特别是在金融数据中。众所周知，金融时间序列的典型分布特性可以用来实现这一目标。这些是，特别是，条件异方差以及大多数金融数据的明显的尖峰，即，经验分布通常比高斯分布具有更厚的尾部和更高的峰值。这些特征可能已经足以识别结构效应。本研究项目中要开发和研究的模型是状态转换模型类的成员。这些模型在实证金融学中相当流行，因为它们很好地拟合和经济解释所提取的制度。目前现有的方法来识别通过政权切换效应，通过和扩展，以实现最佳的适合研究中的数据的相关属性。因此，模型的构建包括厚尾创新，独立成分和马尔可夫转换甘精胰岛素效应，其中后一类模型已被证明可以提供特别接近的拟合，以更高的频率，如每日或每周测量的财务回报。随后，模型的有用性，说明了他们在金融经济学中的一系列相关问题，如冲击的传递，在外汇市场的价格形成，在商品市场的投机的影响。

Identification of the contemporaneous structural effects in vector autoregressive models is an important issue in the analysis of multivariate time series since it is the structural effects which incarnate the economic content of a model. An important example is the analysis of transmission and (possibly) contagion effects of shocks in financial markets. We can measure the correlations between assets and observe that often these correlations are substantially different in bull and bear market periods. However, risk managers and policy makers typically need more information. Namely, they need to know how strong and in which directions shocks in specific markets are transmitted to other markets, and whether the pattern of shock transmission is the same in boom and crisis periods. This information cannot be directly read off the correlations structure.This research project aims at extending and newly developing methods which can help to identify structural shock particularly in financial data. It is known that typical distributional properties of financial time series can be exploited to reach this goal. These are, in particular, the conditional heteroskedasticity as well as the pronounced leptokurtosis of most financial data, i.e., the fact that the empirical distribution typically has thicker tails and higher peaks than the Gaussian distribution. These features may already be sufficient to identify the structural effects.The models to be developed and investigated in this research project are members of the class of regime-switching models. These models are rather popular in empirical finance due to their good fit and economic interpretability of the extracted regimes. Currently existing approaches to identification via regime-switching effects are adopted and extended in order to achieve an optimal fit to the pertinent properties of the data under study. Thus models are constructed incorporating thick-tailed innovations, independent components, and Markov-switching GARCH effects, where the latter class of models has been proven to deliver a particularly close fit to financial returns measured at higher frequencies such as daily or weekly. Subsequently, the usefulness of the models is illustrated by applying them to a set of relevant problems in financial economics, such as transmission of shocks, price formation in foreign exchange markets, and the effects of speculation in commodity markets.