Document Type : Original Article
Authors
1
Ph.D. student, Department of Information Technology Management, South Tehran Branch, Islamic Azad University, Tehran, Iran
2
Department of Economics, Modeling and Optimization Research Center in Engineering Sciences, South Tehran Branch, Islamic Azad University, Tehran, Iran
3
Department of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran
Abstract
Gold is widely used by investors as a hedge against other financial assets, underscoring its critical importance. Sharp fluctuations in gold prices have highlighted the need to identify and understand the factors influencing these price changes. The gold market is known for its volatility, and accurate predictions about its future can significantly impact decision-making. Understanding the gold price and making correct forecasts can help inform decisions about buying and selling gold in global markets, and determine the most favorable times for transactions and investments. Therefore, it is crucial to accurately predict the gold price from various perspectives. This study aims to model and predict gold price volatility in the global market. The research is applied in nature and utilizes monthly data from 2010 to 2022. We evaluated 35 factors potentially affecting gold price volatility. GARCH models and stochastic volatility approaches were employed to extract gold price volatility, while TVPDMA, TVPDMS, and BMA models were used to identify the most significant variables driving volatility. Results indicate that SV models outperform GARCH models in accurately extracting volatility. Among the TVPDMA, TVPDMS, and BMA models, the BMA model demonstrated superior accuracy. Findings reveal that 12 key variables—including the dollar index, oil prices, gold imports and exports, global interest rates, cryptocurrency index, Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Fibonacci Retracement, Average Directional Index (ADX), stock oscillator, and DeMark and Fibonacci Pivot Points—showed the highest likelihood of presence and significance in predicting price volatility. The results suggest that internal factors have a greater impact on gold price volatility than external factors.
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