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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 |
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