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State-Space Modelling of Commodity Prices: A Comparative Evaluation of State Space and Bayesian Structural Time Series Approaches for Retail Gold Prices in India - Evidence from daily retail gold price data in India, 2014–2025

Author(s) Rajib Bhattacharya
Country India
Abstract This study undertakes a comparative analysis of two dynamic time-series modelling frameworks—State-Space Modelling (SSM) and Bayesian Structural Time Series (BSTS)—for forecasting daily retail gold prices in India during the period 2014–2025. Gold, a critical financial and cultural asset, exhibits strong volatility and cyclical patterns influenced by both macroeconomic and behavioural factors. Traditional econometric methods often fall short in capturing such complex, evolving dynamics. The study therefore explores whether embedding Bayesian inference within a state-space structure enhances predictive accuracy, adaptability, and robustness compared to classical Kalman filter–based estimation.
Using the Kalman Filter for SSM and Markov Chain Monte Carlo (MCMC)–based posterior sampling for BSTS, both models are estimated and validated through standard accuracy measures—Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE)—as well as the Diebold–Mariano test for comparative predictive efficiency. The empirical findings reveal that while both models effectively capture latent trend and seasonal components of gold price dynamics, the BSTS model consistently outperforms SSM, achieving a lower MAPE (3.248% versus 4.3168%). This improvement underscores the superiority of Bayesian learning in accommodating parameter uncertainty, stochastic volatility, and structural breaks that characterize high-volatility financial series.
The study also finds that the BSTS model’s probabilistic framework provides not only point forecasts but also full predictive distributions, enabling a richer representation of forecast uncertainty. This feature enhances its relevance for risk management, policy formulation, and investment decision-making. In contrast, the deterministic SSM—despite its interpretability and computational simplicity—exhibits lagged adjustment during abrupt price movements due to fixed-parameter constraints.
Overall, the study establishes that Bayesian structural modelling, by integrating probabilistic inference with state-space decomposition, offers a more resilient and adaptive forecasting paradigm. The findings contribute to the advancement of econometric methodologies in commodity price forecasting and highlight the growing importance of Bayesian and structural approaches for modelling volatility and uncertainty in emerging financial markets.
Keywords This study undertakes a comparative analysis of two dynamic time-series modelling frameworks—State-Space Modelling (SSM) and Bayesian Structural Time Series (BSTS)—for forecasting daily retail gold prices in India during the period 2014–2025. Gold, a critical financial and cultural asset, exhibits strong volatility and cyclical patterns influenced by both macroeconomic and behavioural factors. Traditional econometric methods often fall short in capturing such complex, evolving dynamics. The study therefore explores whether embedding Bayesian inference within a state-space structure enhances predictive accuracy, adaptability, and robustness compared to classical Kalman filter–based estimation. Using the Kalman Filter for SSM and Markov Chain Monte Carlo (MCMC)–based posterior sampling for BSTS, both models are estimated and validated through standard accuracy measures—Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE)—as well as the Diebold–Mariano test for comparative predictive efficiency. The empirical findings reveal that while both models effectively capture latent trend and seasonal components of gold price dynamics, the BSTS model consistently outperforms SSM, achieving a lower MAPE (3.248% versus 4.3168%). This improvement underscores the superiority of Bayesian learning in accommodating parameter uncertainty, stochastic volatility, and structural breaks that characterize high-volatility financial series. The study also finds that the BSTS model’s probabilistic framework provides not only point forecasts but also full predictive distributions, enabling a richer representation of forecast uncertainty. This feature enhances its relevance for risk management, policy formulation, and investment decision-making. In contrast, the deterministic SSM—despite its interpretability and computational simplicity—exhibits lagged adjustment during abrupt price movements due to fixed-parameter constraints. Overall, the study establishes that Bayesian structural modelling, by integrating probabilistic inference with state-space decomposition, offers a more resilient and adaptive forecasting paradigm. The findings contribute to the advancement of econometric methodologies in commodity price forecasting and highlight the growing importance of Bayesian and structural approaches for modelling volatility and uncertainty in emerging financial markets.
Published In Volume 16, Issue 2, July-December 2025
Published On 2025-10-31
Cite This State-Space Modelling of Commodity Prices: A Comparative Evaluation of State Space and Bayesian Structural Time Series Approaches for Retail Gold Prices in India - Evidence from daily retail gold price data in India, 2014–2025 - Rajib Bhattacharya - IJAIDR Volume 16, Issue 2, July-December 2025. DOI 10.71097/IJAIDR.v16.i2.1593
DOI https://doi.org/10.71097/IJAIDR.v16.i2.1593
Short DOI https://doi.org/g98ncb
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