Investigation of platinum price seasonality using high-order autoregression
Year: 2025
Type: article
Abstract: This research investigates platinum price seasonality using high-order autoregressive modeling. The research object is daily platinum price dynamics (LME data, 2015–2024), focusing on long-term dependencies and cyclical patterns. The method employs stepwise decomposition of a 270-day lag autoregression AR(270) into computationally efficient 15-day lag sub-models, enabling significance testing of all coefficients while minimizing resource demands. Results identify the one-day lag as the dominant predictor, with marginal effects at 6–15-day lags and MAPE (1.15%) confirm model robustness. Conclusions indicate no statistically significant weekly cycles due to the overwhelming influence of short-term lags, though the method’s applicability in low-resource environments (e.g., Microsoft Excel) facilitates accessible highorder autoregression.
Author: Klimentov, Andrei
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W7078158123
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