گارچ

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.

Iran is a windy country due to its proximity to the sea and having various plateaus. The global statistics of the last 30 years indicate an increase in the world's energy needs. Therefore, the optimal use of renewable energy sources, including wind energy for electricity generation, especially based on environmental attitudes, is increasing in many countries of the world. But due to the instability of wind energy, its use faces a challenge, which can be effectively reduced by modeling wind speed volatilities. In this article, the weekly mean recorded data of wind speed in Ardabil meteorological station during 1380-1395 are modeled using time series GARCH models (including GARCH model and asymmetric GARCH models). Based on the Bayesian information criterion, the best model for wind speed volatilities in Ardabil meteorological station is the GARCH model. In this article, Box-Jenkins modeling method with Eviews and R software is used for data analysis.

We have introduced the two-sided Lomax-GARCH (TSLx-GARCH) model. We have used this model to create a more realistic value-at-risk value index than other distributions for all confidence levels. We find this index for applied data.

In this study, a new flexible distribution for GARCH models in predicting the value at risk is presented. Accurate modeling of financial returns requires proper innovation distribution.

Experimental results show that the GJR-GARCH model, with its innovative TSLx distribution, generates realistic value index predictions, realistic normal distribution, t-student and generalized error distributions for all levels of confidence. The proposed distribution flexibility opens up an opportunity to increase the accuracy of financial return modeling in GARCH models.

Originality/Value: 

We have used the TSLx-GARCH in data modeling and simulation and find both skewness and excess elongation in the financial return series and confidence levels for all levels.