An examination of nonlinear dependence in exchange rates,using recent methods from chaos theory

Interest in the relevance of nonlinear dynamics to finance and economics has spurred the evolution of new ways to analyze time series data. Tests for chaos, based on a metric approach which measures spatial correlations, led to the development of the correlation dimension test for chaos and the BDS test for nonlinearity. More recently, a topological method has been introduced into the scientific literature which employs a simple qualitative test for chaos that is adaptable to the characteristics of financial data. A quantitative version is also presented here. Conflicting evidence exists about the presence of chaotic behavior in exchange-rate data. The qualitative topological test does not support evidence of a chaotic generating mechanism in these series. The quantitative form finds nonlinear dependence and is a useful diagnostic to determine the adequacy of ARCH-type models for this nonlinear structure.     

Chaotic behavior in national stock market indices: New evidence from the close returns test

Attempts have been made to detect chaotic behaviour in financial markets data using techniques which require large, clean data sets. Although such data are common in the physical sciences where these tests were developed, financial returns data typically do not conform. The close returns test is a recent innovation in the literature and is better suited to testing for chaos in financial markets. This paper tests for the presence of chaos in a wide range of major national stock market indices using the close returns test. The results indicate that the data are not chaotic, although considerable nonlinearities are present. The commonly used BDS test is also applied to the data and, in comparison, the close returns test provides substantially more evidence of nonlinearity compared to the BDS test.     

金融混沌形成的微观机制分析

金融是现代经济的核心。金融系统的安全、稳定是经济社会稳定发展的关键。金融系统在运行过程中发生因确定性失稳而出现诸如金融市场的剧烈动荡、金融危机等金融混沌现象,给经济的增长与社会的稳定带来了很大的负面影响。从微观方面分析金融混沌的形成机制,研究发现金融混沌的形成主要是由金融市场固有的缺陷、过度的金融创新以及金融监管的缺失三方面因素共同作用的结果。探讨金融混沌的形成机制有助于为防范与控制金融混沌指明方向。     

商誉减值准则的变迁及启示:国际视野与中国实践

近年来,我国商誉减值乱象频发,很多上市公司因计提巨额商誉减值导致出现巨额亏损,引发了众多学者对商誉计量的高度关注。由于经济发展存在较大差异,不同国家的商誉准则也有很大区别。国际会计准则委员会、美国会计准则委员会及中国财政部会计准则委员会对有关商誉后续处理方法的准则规定存在很大不同,而大多数学者仅选取其中两者进行对比分析。纵观三者来看,我国商誉准则的变化受国际及美国会计准则委员会的综合影响较大,值得通过对比和分析来获得启示,为我国未来商誉准则的发展提供参考。     

经济危机视角下基于混沌控制的投资风险管理研究

美国的次贷危机引发的全球经济危机,让我们重新审视投资风险管理在金融领域扮演的重要角色,特别对于中国等发展中国家来说,风险管理显得尤为重要,另一方面宏观经济系统普遍存在混沌现象,这种非线性的动力学给予了投资风险管理很大的发展空间,本文在对投资风险管理进行混沌特性分析的基础上,基于房地产市场的投资风险管理进行了混沌控制的实证分析,并对控制变量进行了动态区间分析,最后提出政策建议。     

Multiscale analysis of economic time series by scale-dependent Lyapunov exponent

Economic time series usually exhibit complex behavior such as nonlinearity, fractal long-memory, and non-stationarity. Recently, considerable efforts have been made to detect chaos and fractal long-memory in finance. While evidence supporting fractal scaling in finance has been accumulating, it is now generally thought that financial time series may not be modeled by chaos or noisy chaos, since the estimated Lyapunov exponent (LE) is negative. A negative LE amounts to a negative Kolmogorov entropy, and thus implies simple regular dynamics of the economy. This is at odds with the general observation that the economy is highly complicated due to nonlinear and stochastic interactions among component systems and hierarchical regulations in the world economy. To resolve this dilemma, and to provide an effective means of characterizing fractal long-memory properties in non-stationary economic time series, we employ a multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE), to characterize economic time series. SDLE cannot only unambiguously distinguish low-dimensional chaos from noise, but also detect high-dimensional and intermittent chaos, as well as effectively deal with non-stationarity. With SDLE, we are able to show that the reported negative LE may correspond to large-scale convergence, but not imply the absence of small-scale divergence or noisy chaos in the world economy. Using US foreign exchange rate data as examples, we further show how SDLE can readily characterize fractal, persistent or anti-persistent long-range correlations in economic time series.     

地方政府职能转型与区域经济发展

中国各地产业结构的促同化趋势有强化之势,产业结构调整与升级难的问题一直困扰着经济的可持续发展。各地政府争资源、要政策是形成这个局面的主要因素。政府职能定位偏差与区域经济发展内涵混乱是造成这个局面的直接结果。重新强调政府职能转变、廓清区域经济发展的内涵在＂十二五＂期间仍是主要议题。     

UNIT ROOT TESTING: A CRITIQUE FROM CHAOS THEORY

A counter-example from chaos theory is used to challenge the augmented Dickey-Fuller (ADF) test and common prewhitening techniques. The ADF test is applied to data constructed from a fully deterministic nonlinear (chaotic) process. The null hypothesis, that a unit root is present, cannot be rejected; “stationarity” is achieved by prewhitening. The largest Lyapunov exponent and the correlation dimension are estimated for the original and conditioned series in efforts to detect the nonlinearity and ascertain information regarding its specification. This is repeated in the presence of additive white noise. In no case is the procedure successful, nor is misspecification avoided. Along the way, the tests for nonlinearity provide evidence in support of the results of Nelson and Plosser (1982), that the removal of deterministic trends from time series that appear to be unit root processes can lead to spurious results.     

Nonlinearity in investment grade Credit Default Swap (CDS) Indices of US and Europe: Evidence from BDS and close-returns tests

This paper is aimed at testing for nonlinearity and chaos in Investment Grade CDS indices of US and Europe. For this exercise, the author has chosen the two most liquid indices, namely CDX.NA.IG (US) and iTraxx.Europe (Europe). BDS test (Brock, Dechert, & Scheinkman, 1987) is employed to test for prevalence of nonlinearity in the US and European datasets. The author then subjects both the US and European datasets to the close-returns test (Gilmore, 1993, 1996, 2001) to examine whether the close-returns plots pertaining to these datasets exhibit any chaotic patterns. The CDS datasets were prepared differently for BDS and close-returns test. Since the BDS test cannot differentiate between linear and non-linear dependency, a best-fitting AR model was fitted to the transformed CDS datasets to remove linear-dependency in the data. The BDS test was then applied to the stationary, linearly-independent AR residuals pertaining to transformed US and European datasets. BDS test outcomes revealed rejection of null hypothesis (i.i.d.) with regard to US and European investment-grade CDS indices. The close-returns test outcomes revealed prevalence of an underlying structure that is neither random nor chaotic in nature. In short, the study's findings reveal prevalence of non-chaotic nonlinearity in the US and European CDS indices. These findings not only augment existing literature on nonlinearity of different asset classes, but also reflect the need for researchers and practitioners to accommodate and appropriately account for nonlinearity while modeling CDS indices spread movements.     

中国金融结构与产业结构关系研究

以我国1978～2011年金融结构与产业结构的关系作为研究对象,利用单位根检验、协整关系检验、误差修正模型以及格兰杰因果关系检验等方法,在理论分析的基础上,进行了模型的构造,找出二者之间的长期与短期关系。通过分析发现,二者互为因果,且存在稳定关系。但目前我国两种结构上还存在着滞后经济发展的问题,需要进行结构优化。