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1、本 科 生 毕 业 论 文外 文 资 料 翻 译 专 业 工商管理（财务管理方向） 班 级 姓 名 指导教师 所在学院 附 件1.外文资料翻译译文;2.外文原文中小规模的金融数据分析Andreas P. Nawroth, Joachim Peinke物理研究所，Carl-von-Ossietzky奥尔登堡大学，D - 26111奥尔登伯格，德国网上提供2007年3月30日摘要财务数据随机分析已经被提出，特别是我们探讨如何统计在不同时间记录返回的变化。财务数据的时间规模依赖行为可分为两个区域：第一个时间范围是被描述为普遍特征的小时间区域（范围秒）。第二个时间范围是增加了几分钟的可以被描述为随机马

2、尔可夫规模的级联过程的中期时间范围。相应的Fokker - Planck方程可以从特定的数据提取，并提供了一个非平衡热力学描述的复杂的财务数据。关键词：经济物理学；金融市场；随机过程；Fokker - Planck方程1 导言复杂的金融市场的其中一个突出特点是资金数量显示非高斯统计往往被命名为重尾或间歇统计。描述金融时间序列x(t) 的波动 ，最常见的就是log函数或价格增量的使用。在这里我们认为，log函数y()超过一定时间t的统计，被定义为：y()=logx(t+)-logx(t) （1）其中x(t)是指在时间t时资产的价格。在财务分析数据中一个常见的问题是讨论随机数量的平稳性，尤其是我们

3、发现在我们的分析中采用什么样的方法似乎是强大的非平稳性的影响，这可能是由于数据的选择。请注意，有条件的应用相当于一个特定的数据过滤。尽管如此，特殊的结果略微改变了不同的数据窗口，显示出非平稳性影响的可能性。在本文中，对于一个特定的数据窗口（时间段）我们侧重于分析和重建进程。目前已有的分析主要是基于1993至2003年的拜耳数据，财务数据集是由Kapitalmarkt Datenbank （ KKMDB ） 提供。2 小规模分析财务数据的一个突出特点是事实上概率密度函数（pdfs）不是Gaussian，而是展览重尾形状。另一个显著的特点是形状伴随着可变规模的大小而变化。分析pdfs伴随着规模的变

4、化的统计，非参数方法是一种选择。Pdf p(y()的时间T和PT（y(T)）的参考时间T之间的差距是可以计算的。作为一个参考的时间，在我们的数据集上接近最小的可用时间但仍然有足够的活动，T=1 s是选择。为了能够比较pdfs，并排除由于不同的均值和方差的影响 ，所有的pdfs p(y()正常化为零平均，标准偏差为1 。作为衡量量化两个分布p (y() 和PT (y(T) 之间的距离，需使用Kullback Leibler：dK()= （2）dK 随着t的增加而变化，量化的改变pdfs的形状。对于不同的股票，目前我们发现时间小于1分钟的线性增长的距离测度似乎是普遍的。如果正常化的Gaussian

5、分布是作为参考分布的，在小型时间表制度中快速偏离Gaussian变得很明显。对于较大的时间规模dK仍然接近常数，这表明pdfs的形状改变的非常缓慢。3 中等规模的分析接下来，对于较大的时间尺度（1分钟）进行讨论。我们从级联观点着手，有可能通过级联运行过程中的变量掌握复杂的财务数据，尤其是它已被证明，有可能从给出的随机级联过程Fokker - Planck方程的形式中直接估计数据。这一做法的基本意图是为了获取所有的财务数据的一般性联合正规模概率密度p(y1, 1;y2, 2;yN, N)的订单统计。在这里，我们使用速记符号y1=y(1),采取完整的概括性的ii+1，包含在较大的y(i+1)中的较

6、小的y(i)都取决于t 。复合的pdfs可由多个条件概率密度p(yi, iyi+1, i+1; . . . ; yN, N)来表达，包含众多点n的数据集n大概的数值范围，基本上可以简化为马尔可夫过程中的一个随机变化过程。这种情况下，如果条件密度符合下列关系：p(y1, 1y2, 2;y3, 3; . . . ; yN, tN)p(y1, 1y2) （3）因此，p(y1, 1;yN, N)= p(y1, 1y2)p(yN-1, N-1yN, N)p(yN, N) （4）公式4显示马尔可夫过程中有条件的pdf的重要性。p(y, y0, 0) (和0是任意数，1 min) is discussed.

7、 We proceed with the idea of a cascade. it is possible to grasp the complexity of financial data by cascade processes running in the variable . In particular it has been shown that it is possible to estimate directly from given data a stochastic cascade process in the form of a FokkerPlanck equation

8、. The underlying idea of this approach is to access statistics of all orders of the financial data by the general joint n-scale probability densities p(y1, 1;y2, 2;yN, N). Here we use the shorthand notation y1=y(1) and take without loss of generality ii+1. The smaller log returns y(i) are nested ins

9、ide the larger log returns y(i+1) with common end point t.The joint pdfs can be expressed as well by the multiple conditional probability densities p(yi, tiyi+1, ti+1; . . . ; yN, tN). This very general n-scale characterisation of a data set, which contains the general n-point statistics, can be sim

10、plified essentially if there is a stochastic process in t, which is a Markov process. This is the case if the conditional probability densities fulfil the following relations:p(y1, 1y2, 2;y3, 3; . . . ; yN, N)p(y1, 1y2) (3)Consequently,p(y1, 1;yN, N)= p(y1, 1y2)p(yN-1, N-1yN, N)p(yN, N) (4)holds. Eq

11、. (4) indicates the importance of the conditional pdf for Markov processes. Knowledge of p(y, y0, 0) (for arbitrary scales and 0 with 0) is sufficient to generate the entire statistics of the increment, encoded in the N-point probability density p(y1, 1;y2, 2;yN, N).For Markov processes the conditio

12、nal probability density satisfies a master equation, which can be put into the form of a KramersMoyal expansion for which the KramersMoyal coefficients D(K)(y, ) are defined as the limit 0 of the conditional moments M(K)(y, , ): (5) (6)For a general stochastic process, all KramersMoyal coefficients

13、are different from zero. According to Pawulas theorem, however, the KramersMoyal expansion stops after the second term, provided that the fourth order coefficient D(4)(y, ) vanishes. In that case, the KramersMoyal expansion reduces to a FokkerPlanck equation (also known as the backwards or second Ko

14、lmogorov equation): (7)D(1) is denoted as drift term, D(2) as diffusion term. The probability density p(y, ) has to satisfy the same equation, as can be shown by a simple integration of Eq. (7).4. Results for Bayer dataThe KramersMoyal coefficients were calculated according to Eqs. (5) and (6). The

15、timescale was divided into half-open intervals1/2(i-1+i),1/2(i+i+1)assuming that the KramersMoyal coefficients are constant with respect to the timescalein each of these subintervals of the timescale. The smallest timescale considered was 240 s and all larger scales were chosen such that i0.9*i+1. T

16、he KramersMoyal coefficients themselves were parameterised in the following form:D(1)0+1y (8)D(2)0+1y+2y2 (9)This result shows that the rich and complex structure of financial data, expressed by multi-scale statistics, can be pinned down to coefficients with a relatively simple functional form.5. Di

17、scussionThe results indicate that for financial data there are two scale regimes. In the small-scale regime the shape of the pdfs changes very fast and a measure like the KullbackLeibler entropy increases linearly. At timescales of a few seconds not all available information may be included in the p

18、rice and processes necessary for price formation take place. Nevertheless this regime seems to exhibit a well-defined structure, expressed by the very simple functional form of the KullbackLeibler entropy with respect to the timescale . The upper boundary in timescale for this regime seems to be ver

19、y similar for different stocks. Based on a stochastic analysis we have shown that a second time range, the medium scale range exists, where multi-scale joint probability densities can be expressed by a stochastic cascade process. Here, the information on the comprehensive multi-scale statistics can

20、be expressed by simple conditioned probability densities. This simplification may be seen in analogy to the thermodynamical description of a gas by means of statistical mechanics. The comprehensive statistical quantity for the gas is the joint n-particle probability density, which describes the loca

21、tion and the momentum of all the individual particles. One essential simplification for the kinetic gas theory is the single particle approximation. The Boltzmann equation is an equation for the time evolution of the probability density p(x; p; t) in one-particle phase space, where x and p are posit

22、ion and momentum, respectively. In analogy to this we have obtained for the financial data a FokkerPlanck equation for the scale t evolution of conditional probabilities, p(yi, iyi+1, i+1). In our cascade picture the conditional probabilities cannot be reduced further to single probability densities

23、, p(yi, i), without loss of information, as it is done for the kinetic gas theory.As a last point, we would like to draw attention to the fact that based on the information obtained by the FokkerPlanck equation it is possible to generate artificial data sets. The knowledge of conditional probabiliti

24、es can be used to generate time series. One important point is that increments y() with common right end points should be used. By the knowledge of the n-scale conditional probability density of all y(i) the stochastically correct next point can be selected. We could show that time series for turbul

25、ent data generated by this procedure reproduce the conditional probability densities, as the central quantity for a comprehensive multi-scale characterisation.Banking crisis and financial structure: A survival-time analysisAye Y. EvrenselDepartment of Economics and Finance, Southern Illinois Univers

26、ity Edwardsville, Edwardsville, IL 62026-1102, United StatesReceived 20 October 2006; received in revised form 20 February 2007; accepted 1 July 2007Available online 17 July 2007AbstractThis paper applies non-parametric and parametric methods of survival analysis to the international bank crisis dat

27、a. The empirical results suggest that concentration in the banking sector increases the survival time. Additionally, the results associated with survival functions indicate that the G-10 and non-G10 countries constitute two distinct groups of countries, where the non- G10 countries have a higher inc

28、idence of failure (bank crisis). The parametric survival time regressions (Weibull) confirm the possibility that the effects of the covariates on bank crises may have different dynamics in the G-10 and non-G10 countries. Subsequent analysis on bank concentration reveals the sources of the different

29、dynamics associated with bank crises in developed and developing countries. The results suggest that higher bank concentration in developing countries may be related to the absence of competitive forces in the economic and political structure.Keywords: Bank crisis; Bank concentration; Bank regulatio

30、ns; Survival analysis1.IntroductionThe effects of concentration and competitiveness in banking on the industrys fragility have been investigated in many studies, which have produced contradictory results. Some studies find an inverse relationship between the degree of bank concentration, excessive r

31、isk taking, and banking crisis in that higher degrees of concentration are associated with lower probability of bank crisis. Two explanations can be provided. First, if concentration promotes market power and therefore enhance bank profits, bank managers may be less willing to take excessive risk, w

32、hich would reduce the probability of a bank crisis. Second, in a more concentrated banking system, the costs associated with monitoring and supervising banks may be lower, which is also expected to reduce the likelihood of systemic banking crises.Empirical studies on this issue emphasize the adverse

33、 effects of competition in the banking sector. It has been suggested that increases in competition caused bank charter values to decline in the early 1980s, which increased default risk through the increase in asset risk (Keeley, 1990). It has also been argued that there is a contradiction between d

34、eregulation and fairly priced, risk-sensitive deposit insurance in the presence of private information and moral hazard; risk-sensitive, incentive-compatible deposit insurance cannot be implemented in a competitive, deregulated environment (Chan, Greenbaum, & Thakor, 1992). Although competition among banks provides greater freedom in allocating assets, it can undermine prudent bank behavior by taking excessive risk or “gambling