The Principles of Modelling Causal Relationships in Financial Time Series on the Example of Stock Indices

In the article, the authors consider the issues of modeling causal relationships using the example of financial time series. Many endogenous indicators with tens and hundreds of feedbacks characterise any complex dynamic system. The links between the elements of the system determine the impact on the result of the system through a variety of parameters that can lead to a distortion of the effect of managerial influence and, as a result, to a distortion of the system's efficiency as a whole. Modelling is an effective tool for determining the considered system parameters under the influence of external forces and restrictions on it.In the activity of the control system, the analytical tools of mathematical modelling based on the developed modelallow to see how the system will change over time and what factors have the most significant impact on it. The system is changeable in time, which causes the difference between the initial state and the state at a certain point. That is explained by causal relationships between the elements of the systems and the level of influence of external factors, which can be referred to as economic or market ones. The latter, in turn, can be analyzed and, as a result, predicted using mathematical methods. Speaking of external factors, many of them are not amenable to precise analysis, and examples of these are natural and ecological ones.In this article, the authors define the principles of modelling causal relationships in financial time series using the example of stock indices. In the course of the study, the unsteadiness of time series was verified by Dickey-Fuller tests, as well as using the KPSS test solution, which confirmed the unsteadiness of stock indices in the selected time period. The calculations were carried out using the modern statistical package Eviews.As a consequence, the relevance of this article is based on the search for an answer to the question of stationary financial time series.