摘要
20世纪末21世纪初是金融危机爆发的高峰期,金融危机日渐频繁且危害日益严重。随着世界经济一体化程度的提高,金融危机的传染效应也逐步显现出来。最明显的例子就是2007年爆发的美国次贷危机传染至全球,导致了2009年全球金融风暴。针对金融危机传染的研究越来越多,促使金融危机传染研究逐渐从整个金融危机理论中分化出来,向着一个独立的国际经济学方向发展。因此,研究金融危机的传染机制具有较大的理论意义。全球金融风暴对我国经济也造成了很大的影响,因此,为了能够从各个角度应对金融危机传染,研究金融危机的传染机制具有较大的现实意义。
金融危机传染的现有研究在理论方面未能解析金融危机传染的非线性特性。金融危机传染研究的实证方面主要是通过方差——协方差矩阵或线性相关系数法来分析市场相关程度,或者使用VAR等方法,对金融危机传染的非线性特征解释不足;对金融危机期间金融危机传染研究的实时性不足。因此探索使用非线性相互依赖性来刻画多重金融时间序列间的非线性动力学特性,以期能更好地研究金融危机传染的非线性机制。
本文研究了基于非线性动力学模型的金融危机传染模型。该模型的基础是将各国的金融市场看作是具有不同程度的波动的动力学系统,各个动力学系统之间相互耦合,这些市场间的耦合或是由于贸易联系,或是由于新闻、资金以及金融资产的跨国界自由流动。使用非线性相互依赖性来描述动力学系统之间的耦合关系,从而金融冲击的跨国传输和金融危机传染,就可以通过动力学系统之间非线性相互依赖性的波动来反映。因此,可以采用非线性相互依赖性来刻画金融危机传染的非线性特征。
非线性相互预测算法是具体衡量动力学系统之间的非线性相互依赖性的一种方法。为了检验序列的非线性特征,在进行非线性相互预测之前,需要检验系统是否存在非线性特征,其后求取非线性算法中两个重要参数——最优时间延迟τ和最佳嵌入维数d。在此基础上进行非线性相互预测,并使用得到的非线性相互预测测度来度量动力学系统间的非线性相互依赖性。针对非线性相互预测原始算法预测精度不高的不足,以及金融危机期间样本量小的特点,本文采用支持向量机方法来改进原始算法。
将非线性相互预测原始算法与SVM结合后的算法应用到了东南亚金融危机中,对金融危机期间各国(或地区)的金融市场时间序列数据进行了非线性相互预测,计算得到每两条序列之间的非线性相互预测测度,以此衡量各国各市场间非线性相互依赖性,并分析其波动以研究金融危机传染的方向和强度。针对原始算法预测结果随机性强的问题,本文分别从选择非随机的对比精度、替换对比精度以及滚动预测三个方面改进了非线性相互预测测度,使其更加适合金融危机传染的分析要求。
将改进的非线性相互预测测度应用到2007-2009全球金融风暴中,通过分析得出以下结论:首先,本文使用的支持向量机改进非线性相互预测算法确实提高了非线性相互预测的预测精度;其次,各国(或地区)的金融市场时间序列数据之间非线性相互预测测度在金融危机期间确实发生了强烈的波动,这种波动表明,在金融危机期间,各国(或地区)之间非线性相互依赖性发生剧烈震荡,也即各国所在的非线性动力学系统之间耦合度发生剧烈波动。而在金融危机期间,各国(或地区)之间通过各种渠道发生了传染,由于传染造成的后果不同,所以耦合度是剧烈震荡的。该研究表明,金融危机传染的非线性特征可由非线性相互依赖性刻画出来。其后,本文从非线性动力学角度比较了东南亚金融危机和2007-2009全球金融风暴在金融危机传染。鉴于以上研究成果,计算了我国金融市场受主要贸易伙伴金融市场数据影响的非线性相互预测测度,将该测度作为金融危机传染的一个预警指标,从而为我国对金融危机传染的应对提供重要的工具。
The late 20th century and early 21st century is a peak period of financial crisis. The financial crisis becomes increasingly frequent and serious. With a higher level of international economic integration, the financial contagion effects have gradually revealed. The most obvious example is USA sub-prime crisis in 2007, which have global infection, and lead to the global financial crisis in 2009. Therefore, more and more researchers work over the financial contagion. The study of the financial contagion theory is gradually differentiated out of the financial crisis theory to become to an independent direction of international economics. The research on the machenism of financial contagion has very large theoretical significance. The global financial turmoil had a significant impact on China's economy, so the research of financial contagion can help managing financial contagion, and has a very large theoretical and practical significance.
The existing theory of financial contagion is not sufficient to resolve nonlinear transmission characteristics of financial contagion. The empirical study is mainly through the variance-covariance matrix or the linear correlation coefficient method to analyze the degree of market-related, or using VAR or other methods. These methods could not repit the nonlinear characteristics of financial contagion, and could not be real-time during the period of financial crisis. Therefore, we try to use nonlinear interdependence to characterize nonlinear dynamics of the multiple financial time series. We hope this method could be a better empirical method for the nonlinear machenism of financial contagion.
In this paper, we propose a financial contagion model based on the nonlinear dynamics. The financial market is treated as a dynamic system with different levels of volatility. The dynamic systems are coupled, and the coupling of these markets is due to freedom cross-border movement of trade links, or of the press, funds and financial assets. The cross-border transmission of financial shocks and financial contagion could be seen as fluctuations nonlinear interdependence between the nonlinear dynamic systems. So, we can use nonlinear interdependence to depict the nonlinear characteristics of the financial contagion.
We use the nonlinear mutual prediction algorithm to study the nonlinear interdependence of the dynamical systems. Before the nonlinear mutual prediction, we have to test the existence of nonlinear of the testing system. And we choose the optimal time delayτand the best embedding dimension d . On this basis we use the nonlinear mutual prediction algorithm to predict. The prediction accuracy of original nonlinear mutual prediction algorithm is not high enough, and during the financial crisis, the samples are small, so we use support vector machine to improve the original algorithm.
We use the improved nonlinear mutual prediction algorithm on the Southeast Asian financial crisis. We use the financial market time series data of different countries (or regions) to nonlinear mutual predict and gain the nonlinear mutual prediction measure between every two sequences. The forecast results of the original algorithm have strong random, so we range the nonlinear mutual predict measure from three aspects to improve, to make it more suitable for analytical requirements of financial contagion.
We use the improved nonlinear mutual predict measure on the global financial turmoil in 2007-2009. By analyzing the obtained nonlinear mutual prediction measures, we conclude that : First, the improved nonlinear mutual prediction algorithm by support vector machine did improve the prediction accuracy; Second, during the financial crisis, the nonlinear mutual prediction measures of the financial market time series data of between different countries (or regions) occurred strong fluctuations, such fluctuations show that during the financial crisis, nonlinear interdependence between the countries (or regions) dramatic shock, that is the coupling of nonlinear dynamic systems of the countries dramatic fluctuations. That is during the financial crisis, the contagion happened through various channels between the countries (or regions). Due to the consequences of infection are different, the coupling shocks. Finally, we compared the similarities and differences on financial contagion of the Southeast Asian financial crisis and the global financial turmoil in 2007-2009 from a dynamics point of view. On the basis of empirical research, we calculate the nonlinear mutual predict measure between China and main trading partners. This nonlinear mutual predict measure could be used as an early warning indicationr of financial contagion. So it is an important tool for China's response to the financial contagion.
引文
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