Statistical and Stochastic Modeling of the Dynamics of Stock Prices in Biopharmaceutical Industry

Abstract

This study analyzes the dynamics of Amgen’s stock prices using statistical and stochastic models. Change-point detection, anomaly detection, and Granger causality are applied to examine stock behavior. Time series models are used to forecast future trends. Geometric Brownian motion and the Ornstein-Uhlenbeck process are fitted to the data. The results suggest potential Granger causality between Amgen and a competitor, underscoring the value of statistical methods in understanding stock market behavior and offering actionable insights.