【正文】 me t. A mon problem in the analysis of financial data is the question of stationarity for the discussed stochastic quantities. In particular we find in our analysis that the methods seem to be robust against nonstationarity effects. This may be due to the data selection. Note that the use of (conditional) returns of scale τ corresponds to a specific filtering of the data. Nevertheless the particular results change slightly for different data windows, indicating a possible influence of nonstationarity effects. In this paper we focus on the analysis and reconstruction of the processes for a given data window (time period). The analysis presented is mainly based on Bayer data for the time span of 1993–2020. The financial data sets were provided by the Karlsruher Kapitalmarkt Datenbank (KKMDB) . 2. Smallscale analysis One remarkable feature of financial data is the fact that the probability density functions (pdfs) are not Gaussian, but exhibit heavy tailed shapes. Another remarkable feature is the change of the shape with the size of the scale variable τ. To analyse the changing statistics of the pdfs with the scale t a nonparametric approach is chosen. The distance between the pdf p(y(τ)) on a timescaleτ and a pdf pT(y(T)) on a reference timescale T is puted. As a reference timescale, T=1 s is chosen, which is close to the smallest available timescale in our data sets and on which there are still sufficient events. In order to be able to pare the shape of the pdfs and to exclude 12 effects due to variations of the mean and variance, all pdfs p(y(τ)) have been normalised to a zero mean and a standard deviation of 1. As a measure to quantify the distance between the two distributions p(y(τ)) and pT(y(T)), the Kullback– Leibler entropy is used. dK(τ)= )pT (y(T ))))((ln())((????? typtydy p (2) The evolution of dK with increasing t is illustrated. This quantifies the change of the shape of the pdfs. For different stocks we found that for timescales smaller than about 1 min a linear growth of the distance measure seems to be universally present. If a normalised Gaussian distribution is taken as a reference distribution, the fast deviation from the Gaussian shape in the smalltimescale regime bees evident. For larger timescales dK remains approximately constant, indicating a very slow change of the shape of the pdfs. 3. Medium scale analysis Next the behaviour for larger timescales (τ1 min) is discussed. We proceed with the idea of a cascade. it is possible to grasp the plexity of financial data by cascade processes running in the variable τ. 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 Fokker– Planck equation. The underlying idea of this approach is to access statistics of all orders of the financial data by the general joint nscale probability densities p(y1, τ1。 yN, τN)＝ p(y1, τ1│y2) (3) Consequently, p(y1, τ1。 t) in oneparticle phase space, where x and p are position and momentum, respectively. In analogy to this we have obtained for 15 the financial data a Fokker– Planck equation for the scale t evolution of conditional probabilities, p(yi, τi│yi+1, τi+1). In our cascade picture the conditional probabilities cannot be reduced further to single probability densities, p(yi, τi), without loss of information, as it is done for the kiic gas theory. As a last point, we would like to draw attention to the fact that based on the information obtained by the Fokker– Planck equation it is possible to generate artificial data sets. The knowledge of conditional probabilities can be used to generate time series. One important point is that increments y(τ) with mon right end points should be used. By the knowledge of the nscale conditional probability density of all y(τi) the stochastically correct next point can be selected. We could show that time series for turbulent data generated by this procedure reproduce the conditional probability densities, as the central quantity for a prehensive multiscale characterisation. 16 Banking crisis and financial structure: A survivaltime analysis Ay?e Y. Evrensel Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 620261102, United States Received 20 October 2020。 Thakor, 1992). Although petition among banks provides greater freedom in allocating assets, it can undermine prudent bank behavior by taking excessive risk or ―gambling.‖ While regulations such as capital requirements may reduce gambling incentives by putting bank equity at risk, they also can harm banks39。 risktaking behavior under different assumptions regarding the deposit insurance scheme and the dissemination of information. While bank petition increases banks39。 Vives, 1996。Kunt, and Levine (2020), hereafter BDL, represents a rare study that applies pooled data to the relationship between bank crisis and bank concentration. Their empirical results support the concentrationstability hypothesis that, controlling for macroeconomic, financial, and regulatory characteristics of sample countries, bank concentration is inversely related with bank fragility. This paper uses the BDL data and contributes to the discussion in two ways. First, as opposed to the BDL study that uses discriminant analysis, this paper employs survival analysis. Based on Cox and Oakes (1984) and Hosmer and Lemeshow (1999), there are four issues to consider with respect to BDL39。Hara amp。s 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 risk taking, and banking crisis in th