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。     

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。     

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.     

Chaos and Nonlinear Dynamics: Application to Financial Markets

After the stock market crash of October 19, 1987, interest in nonlinear dynamics, especially deterministic chaotic dynamics, has increased in both the financial press and the academic literature. This has come about because the frequency of large moves in stock markets is greater than would be expected under a normal distribution. There are a number of possible explanations. A popular one is that the stock market is governed by chaotic dynamics. What exactly is chaos and how is it related to nonlinear dynamics? How does one detect chaos? Is there chaos in financial markets? Are there other explanations of the movements of financial prices other than chaos? The purpose of this paper is to explore these issues.     

网红直播打赏收入影响因素的实证研究

网红经济是一种新型经济模式,打赏也成为消费者支配财富的一种新型方式。探索打赏背后的影响因素和经济规律不仅对网红娱乐平台的可持续发展至关重要,且可为相关监管规定的落实和完善提供有益参考,有助于引导“网红经济”持续健康发展。当前学术界尚缺乏探索网红主播打赏收入影响因素的研究,本文获取了5家网红经纪机构中41位游戏类网红主播每场直播的收入与直播时长数据,研究发现:首先,网红主播的直播收入和观众打赏强度均与直播时长显著正相关,这意味着网红主播对观众的更多娱乐陪伴使其获得了更多的打赏收入;其次,头部打赏者对网红主播的打赏使非头部打赏者的打赏强度更大,这表明观众对网红主播的打赏存在“羊群效应”。     

监管制度变迁对深圳证券市场效率的影响

本文回顾了十多年来我国证券市场监管制度的变化,运用统计分析和混沌理论来分析市场监管制度对深圳股票市场市场效率的影响.本文认为,深圳股票市场在经历了十年多的发展后市场效率在宏观上有所提高.对重大监管政策出台前后市场变动情况的统计分析表明,大量监管政策的出台在微观层面也有一定的改进.     

Nonlinearity and Low Deterministic Chaotic Behavior in Insurance Portfolio Stock Returns

This article presents new empirical evidence indicating a deterministic component in the portfolio return dynamics of life‐health and property‐liability insurance company stocks. Our research is motivated by the fact that nonlinearities are a fact of economic life for many financial applications the source of which is logically apparent, yet empirical evidence of their existence is at best weak. The primary reason attributed to the weak findings of nonlinearities reported in previous research is the use of aggregate data that can hide nonlinearities at the micro level. Insurance sector stock returns are analyzed because unique institutional characteristics indicate the possibility of identifying nonlinear dynamics. Tests based on the correlation dimension partially confirm the presence of nonlinearity. However, the more powerful Brock, Dechert, and Scheinkman (BDS) statistic strongly suggests the presence of nonlinearities in the insurance stock portfolio data. The BDS statistic applied to the standardized residuals of exponential generalized auto regressive conditional heteroskedasticity (EGARCH) models strongly rejects the null of independent and identically distributed, indicating that conditional heteroskedasticity is not responsible for the presence of the nonlinear structures in the data. In addition, tests for chaos based on locally weighted regressions indicate that insurance stock portfolio returns indicate low‐complexity chaotic behavior. This is an important result since most previous research has failed to report evidence of chaotic behavior in the time series of stock returns. Important contributions of this article are the application of tests of nonlinearities and chaos to more desegregated data sets and the findings of statistically significant evidence indicating nonlinearities and low‐deterministic chaotic behavior in insurance stock portfolio returns.     

区域金融发展与地方政府行为之间的相关性分析

本文主要通过时间序列分析法，运用回归模型和格兰杰因果检验对我国区域金融发展与地方政府行为关系进行了实证分析，结果发现我国四大区域金融发展与各地方的政府行为存在着一定的相关性；四大地区的金融发展与地方政府行为均存在着单向因果关系，其中东、西地区金融发展与政府行为之间存在正向因果关系，而中部与东北地区存在着制约关系。     

Deterministic nonlinearity in the stock returns of major European equity markets and the United States

By using recently developed statistical tools designed to overcome some of the limitations often associated with financial data, this study attempts to detect low-dimensional deterministic chaos in five major European stock markets and the United States. Country indexes exhibiting low-dimensional deterministic chaos may contain some informational inefficiency; thus, it may be possible to use nonlinear dynamics to predict future stock returns. The results do not provide evidence of the existence of low-dimensional chaotic systems in any of the examined indexes. As such, the notion of market efficiency in the examined indexes is not threatened by the findings of this study.     

金融市场的复杂性与金融风险调控：基于金融混沌理论

以金融混沌理论为代表的非线性金融理论是金融研究与金融实务领域的一个前沿工具.已有的研究表明金融市场是一个复杂的动力系统,具有显著的混沌效应.本文依据混沌控制的一般原理,提出了金融市场风险调控的原理与方法.这一研究结果将为探索金融市场与风险管理理论提供新的方向.     

大中型高技术工业企业创新文化对创新绩效的影响研究

创新文化作为一种新型的企业文化,逐渐受到企业的重视,以依赖创新文化而发展的高科技产业更是大力投入对创新文化的研究与利用。本文首先对创新文化相关研究进行整理和分析,取得高科技产业大中型工业企业创新文化相关的数据,然后确定创新文化指标及创新绩效指标,利用EVIEWS5.0软件进行相关数据分析,进行最小二乘估计法的一元线性回归分析,并进行假设检验,包括基于回归拟合的检验、回归参数估计值的显著性t检验,模型整体的显著性F检验。结果显示：创新文化与主营业务收入、营业利润正相关。     

基于空间计量模型的金融发展与技术创新研究

通过运用空间计量经济学的空间滞后模型和空间误差模型,对2005-2009年的省际数据进行分析来检验金融发展与技术创新之间的内在关系。结果表明：在样本期内,各地区间的技术创新能力在空间上并非随机分布,而是呈现出显著的空间自相关性,且金融发展对技术创新有显著的促进作用;同时发现,股票市场的发展比银行信贷的增长更能促进技术创新水平的提高。     

Tick size, the compass rose and market nanostructure

Economists have begun using methods borrowed from the physical sciences to search for non-linearities in economic and financial data. The so-called phase portrait from chaos theory, in which the values of a time-series are plotted against their delayed values, is one of the techniques employed for this purpose. It has recently been shown, however, that when returns on traded assets are plotted in this manner in two- or three-dimensional space, surprising patterns arise which seriously distort the conclusions that can be drawn about the underlying data. These patterns – which resemble a compass rose – reflect the microstructure of the market or, more specifically, the finite size of the “ticks” by which prices can change in a market.The present paper clarifies the reason for this phenomenon. It is shown that within the microstructure there exists nanostructure that becomes visible only when the computations are performed without approximation. High frequency data from a foreign exchange market are used to illustrate this phenomenon.