Open AccessPrediction Research and Application of Financial Time Series Based on Big Data
Author(s) -
Rui Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1881/2/022093
Subject(s) - statistic , econometrics , test statistic , value at risk , value (mathematics) , series (stratigraphy) , function (biology) , parametric statistics , time series , mathematics , kernel density estimation , statistics , economics , statistical hypothesis testing , finance , risk management , paleontology , biology , estimator , evolutionary biology
Financial risk is conductive in the market. In this paper, correlation function is used to model the relevant structure of the financial market. The j-B test statistic value of four stocks is far greater than the critical value of 6.7325, and the critical value of LJung-Box-Pierce test is 32.1342. At the same time, it is proved that the selection of edge distribution function is independent of the dependent structure, but the risk value estimated by semi-parametric POT model is higher than that estimated by kernel density function.