مهدیه توسلی

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.

The present study aims to identify the most important variables affecting the fluctuations of gold prices. It is the first Iranian research in which the fluctuations in this market are modeled using non-linear Bayesian Model Averaging (BMA) and deep neural network approaches.

It is applied research where monthly data collected from 2010 to 2022 were used. It evaluates 35 factors playing a role in gold price fluctuations. GARCH and random fluctuation models are used to extract gold price fluctuations. TVPDMA, TVPDMS, and BMA models are used to identify the most important variables causing gold price fluctuations. Furthermore, the deep learning approach is used to investigate how effective the selected variables are in gold price fluctuations.

The results indicated that Support Vector (SV) models were more accurate than GARCH models in capturing fluctuations and that BMA outperformed TVPDMA and TVPDMS. Additionally, 12 variables were identified as influential in gold price fluctuations, with in-market factors playing a more significant role than out-of-market factors. The study also employed Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP) neural network models in deep learning mode to predict gold price fluctuations. It was concluded that global interest rates had the most significant impact on fluctuations in the gold price, with the Pivot Point DeMark's Index making the greatest contribution.

Originality/Value: 

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 research attempted to develop an intelligent model for forecasting fluctuations in the gold price.