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	1. Poisson process Models of extreme volatility of Bitcoin prices
	ZHANG Han,ZHANG Aidi,GAO Meng
	Mathematics 11 July 2023
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	Abstract:In recent years, digital currencies based on blockchain technology have received widespread attention from global investors and financial regulatory agencies, and the dramatic fluctuations of their price are the major conerns. Previous studies on asset price fluctuations mainly focused on traditional capital markets such as stocks and bonds, while there are less research on price fluctuations in the emerging digital currency market i.e. the Bitcoin. Bitcoin is a currency with intrinsic value that is difficult to quantify, produced entirely by computer computing power, and has no endorsement from any national government or financial institution as a financial asset. Therefore, as a financial asset, the Bitcoin prices often experience violent fluctuations due to numerous complex factors. In this study, two Poission process models, non-homogeneous Poisson process (NHPP) model and the fractional Poisson process (FPP) model, are used to fit the violent Bitcoin price volatility sequence. The NHPP model generalizes the intensity λ of the Poisson process to a function λ(t), reflecting the non-stationarity of violent Bitcoin price fluctuation events. The fractional Poisson process is also a generalization of the homogeneous Poisson process model, where the time interval distribution is extended from the exponential distribution to the Mittag-Leffler distribution. The fractional Poisson process reflects long-term memory effects. In this study, two Poisson point process models are applied to the event sequence of sharp fluctuations in the price of Bitcoin through estimating model parameters and graphical evaluation model fitting, and the ocurruing of the next is aslo predicted and analyzed.
	TO cite this article:ZHANG Han,ZHANG Aidi,GAO Meng. Poisson process Models of extreme volatility of Bitcoin prices[OL].[11 July 2023] http://en.paper.edu.cn/en_releasepaper/content/4761142


	2. SVM-GBDT Model and Internet Finance Credit Risk
	ZHANG Yanna,GONG Yicheng,YU Li
	Mathematics 07 November 2018
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	Abstract:To measure the users' credit risk of Internet finance, it is defined as the probability that users cannot repay on time. Considering the type of data may be continuous or discrete, the integrated regression estimation model GBDT is used. To adapt the increasing scale of data, this paper proposes a GBDT coupled with SVM model (SVM-GBDT), where SVM is used to select important training data first, and then a GBDT model is trained on the data corresponding to the support vectors of SVM. To test the model's effect, this paper analyzes the credit risk of an Internet financial loan institution's user data, which are offered by the "East Securities Futures Cup" Chinese University Statistical Model Contest. On the test set, the accuracy (A) and harmonic mean (F1) and running time (t) are respectively 0.9427 and 0.970035 and 4.5726 seconds for SVM-GBDT model. Then the SVM-GBDT model are compared with other pure models such as Logistic, SVM, CART, RF, and GBDT models, and the comparing results shows that the SVM-GBDT model has great performance than other models. It's the accuracy (A) and harmonic mean (F1) are slightly higher and the running efficiency are far faster than other five models. This model can help Internet financial companies make loan decisions under the background of big data, and also provide a new practice reference for data mining.
	TO cite this article:ZHANG Yanna,GONG Yicheng,YU Li. SVM-GBDT Model and Internet Finance Credit Risk[OL].[ 7 November 2018] http://en.paper.edu.cn/en_releasepaper/content/4746422


	3. Zero-inflated Negative Binomial Model and its Application in Car Insurance
	Jiang Tingting,Huang Wei
	Mathematics 13 April 2015
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	Abstract:Under many circumstances, most car insurance data appears to be zero-inflated and their risks are nonhomogeneous. These lead to general traditional claim frequency models lost their prediction effect, so they are unsuitable to fit this kind of data. This paper is using the car insurance data for GINB model, ZINB model and C-ZINB model to do empirical research, the results demonstrate that the C-ZINB model can overcome the risk of homogeneity and zero inflation very well. In addition, C-ZINB model has good simulation prediction effect.
	TO cite this article:Jiang Tingting,Huang Wei. Zero-inflated Negative Binomial Model and its Application in Car Insurance[OL].[13 April 2015] http://en.paper.edu.cn/en_releasepaper/content/4638882


	4. Optimal dividend, capital injection and reinsurance strategies with transaction costs
	WANG Rong-Ming, YAO Ding-Jun, XU Lin
	Mathematics 20 October 2014
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	Abstract:This paper investigates an optimal dividend and capital injection problemwith non-cheap excess-of-loss reinsurance policy. The insurancecompany can control its surplus by paying dividends and injecting capitals, which incur proportional and fixed transaction costs. Moreover, both insurer and reinsurer rely on theexpectation premium principle to calculate their premiums but withdifferent safety loadings. The goal of insurance company is todetermine optimal dividend, capital injection and reinsurance strategiesfor maximizing the difference between the expected discounteddividends minus the expected discounted capital injections untilthe time of bankruptcy. By solving the corresponding impulse control problem, closed-form expressions for the value functionand optimal strategy are derived.
	TO cite this article:WANG Rong-Ming, YAO Ding-Jun, XU Lin. Optimal dividend, capital injection and reinsurance strategies with transaction costs[OL].[20 October 2014] http://en.paper.edu.cn/en_releasepaper/content/4614350


	5. Empirical Likelihood Diagnosis of Varying Coefficient Errors-in-Variables Models with Longitudinal Data
	Wang Shuling,Zheng Lin,Dai Jiangtao
	Mathematics 27 May 2014
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	Abstract:In this paper,we investigate the empirical likelihood diagnosis of varying coefficient errors-in-variables models with longitudinal data.The empirical log-likelihood ratios fro the time-varying coefficient function based on the global variance structures are introduced.First,the estimation equation based on empirical likelihood method are estabilished.Then,some diagnostic statistics are proposed.At last,Monte Carlo simulation is given to illustrate our results
	TO cite this article:Wang Shuling,Zheng Lin,Dai Jiangtao. Empirical Likelihood Diagnosis of Varying Coefficient Errors-in-Variables Models with Longitudinal Data[OL].[27 May 2014] http://en.paper.edu.cn/en_releasepaper/content/4598788


	6. Improved Partial Least Square and Multi-group Structural Equation Model Using Distributed Computing
	Ruan Xiangwei,Tong Hengqing
	Mathematics 20 February 2009
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	Abstract:Structural equation model has become a very popular data-analytic technique. It is widely applied in Psychology and Sociology as well as other fields, especially in Customer Satisfaction Index (CSI) model, which was required by a series of ISO9000 criterions. Many papers focus on the extension of the SEM, such as multi-level SEM, multi-group SEM and nonlinear SEM. Having proposed the generalized linear model with a convex constraint, we may discuss multi-group SEM using our method in this paper. Distributed computing is a new kind of computing technology for complex scientific calculation which accompanied by the rapid development of Internet technology. In the modern times, the study fields cover many disciplines and involve many sectors, and the classifications are appropriately. And each of the subjects seems to have required a large amount of calculation. Distributed computing, considering its unique advantages-cheap and efficient, has attracted the concern of the society.
	TO cite this article:Ruan Xiangwei,Tong Hengqing. Improved Partial Least Square and Multi-group Structural Equation Model Using Distributed Computing[OL].[20 February 2009] http://en.paper.edu.cn/en_releasepaper/content/29382

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