On Financial Distributions Modelling Methods: Application on Regression Models for Time Series

The financial market is a complex system with chaotic behavior that can lead to wild swings within the system. This drive into variety of interesting phenomenon such as phase transitions, bubbles, and crashes, so on. Of interest in modelling identifying distribution stylized facts particular time series, determine if volatility present, resulting risk contagion. Regression has been used this study methodology identify goodness-of-fit between original generated series model, which serves criterion for model selection. Different methods include common Box–Jenkins ARIMA, ARMA-GARCH type methods, Geometric Brownian Motion models Tsallis entropy based when data size permits, use Determining utility, allows further opportunities bivariate regression copula modelling, apart from usual forecasting. also identification parameters are forecasting model. Carbon Emissions Futures price dates 1 May 2012 2022, highlight application modelling.