VAR Modeling in the Automotive Industry – Prediction of Volkswagen Prices - Szostak, Radosław; Świercz, Wojciech (Model VAR w branży motoryzacyjnej – predykcja cen akcji firmy Volkswagen, 2024)
The purpose of this study is to develop and verify the effectiveness of a Vector Autoregression (VAR) model for use in an investment trading strategy. The research object is the VAR model, which forecasts the daily opening prices of Volkswagen stocks. To achieve stationarity, the model was estimated on the first differences of stock prices. Initially, the model included price changes of five different rivals of Volkswagen from the automotive industry (along with the AR values of Volkswagen), but ultimately, only the price changes of Volkswagen, General Motors, and Honda were included in the model. The study’s key findings indicate that the model exhibits strong statistical performance, characterised by a high R2 value and low Mean Squared Error (MSE) and Mean Absolute Error (MAE). However, the model’s prediction accuracy for the direction of price changes is approximately 49%, meaning the model’s directional forecasts are correct only almost half of the time.
Cytuj za: Szostak, R., Świercz, W. (2024). VAR Modeling in the Automotive Industry – Prediction of Volkswagen Prices. In M. Pauka, T. Słoński (Eds.), Finanse (pp. 51-59). Publishing House of Wroclaw University of Economics and Business.
Keywords: VAR; automative industry; Volkswagen; stock market forecasting; branża motoryzacyjna; predykcja rynku akcji