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Precise carbon price prediction is crucial for informing climate policies, maintaining carbon markets, and driving global green transformation. Currently, decomposition integration methods are extensively employed for carbon price forecasting. ...

The carbon market serves as a potent tool for attaining the objectives of Carbon Peaking and Carbon Neutrality, and factors influencing the carbon price and their forecasts have a direct impact on the functioning of this market. In this paper, the ...

To enhance the accuracy of carbon futures volatility forecasting, a deep learning model combining Transformer-LSTM with multifactor analysis is proposed. This model is integrated with the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) ...

The 2008 financial crisis has highlighted the lack of precision in the market risk metrics that financial institutions must report to the regulator. The use of Machine Learning techniques in stock markets and the treasury (Front–Back Office) of ...

• Financial institutions need to optimize volatility forecasts.
• Volatility forecasts are needed to efficiently estimate market risk metrics.
• We propose a hybrid methodology to capture the volatility of market risk factors.
• Our ...

Bitcoin has gradually gained acceptance as a payment method that, unlike electronic payments in dollars or euros, passes through the international trading system with zero or lower fees. Moreover, Bitcoin and e-commerce have become increasingly ...

Energy commodity prices usually fluctuate, non-linear and non-stationary. These stylish facts pose a big challenge in predicting the volatility of energy commodity prices because they usually contain long memory. In the energy market, energy ...

This research compares modeling and forecasting the volatility of the IHSG, N225, and BSESN30 capital market indices using the GARCH variation model against the GARCH-fractional cointegration variation. The data used is secondary data obtained ...

Forecasting cryptocurrency volatility can help investors make better-informed investment decisions in order to minimize risks and maximize potential profits. Accurate forecasting of cryptocurrency price fluctuations is crucial for effective ...

• Comprehensive study of the forecasting methods for cryptocurrency volatility.
• 12 popular methods compared including HAR, GARCH, LASSO, SVR, MLP, RF, LSTM.
• No single best method for each cryptocurrency.
• Different models perform ...

Machine learning mechanism is establishing itself as a promising area for modelling and forecasting complex time series over conventional statistical models. In this article, focus has been made on presenting a machine learning algorithm with ...

• A novel hybrid of ARIMA-LSTM model using random forest algorithm for selecting suitable input lags for LSTM.
• The proposed model has the capability to deal with volatile data sets, with inherent skewedness.
• The model has been ...

The Bayesian estimation of GARCH-family models has been typically addressed through Monte Carlo sampling. Variational Inference is gaining popularity and attention as a robust approach for Bayesian inference in complex machine learning models; however, ...

This study aims to assess how global economic policy uncertainty influences the volatility of precious metals prices especially in the case of “gold and silver” that occupy the two largest shares in the global precious metals market. It covers ...

Financial stability plays an important role in the economic and social development of any economy. This study selects daily frequency data of 20 indicators from the money market, stock market, bond market and foreign exchange market in ...

M-regression and quantile methods have been suggested to estimate generalized autoregressive conditionally heteroscedastic (GARCH) models. In this paper, we propose an M-quantile approach, which combines quantile and M-regression to obtain a ...

The Fama-French three-factor model advances the capital asset pricing model by expanding size risk and value risk factors to market risk factors. A quantile Fama-French three-factor model with GARCH-type dynamics, leptokurtosis, and skewness via ...

This paper proposes a new GARCH specification that adapts the architecture of a long-term short memory neural network (LSTM). It is shown that classical GARCH models generally give good results in financial modeling, where high volatility can be ...

A functional time series approach is proposed for investigating spatial correlation in daily maximum temperature forecast errors for 111 cities spread across the U.S. The modelling of spatial correlation is most fruitful for longer forecast ...

Although cryptocurrencies are catching the fancy of investors for various benefits such as decentralization, low transaction costs, and inflation hedging, their extreme volatility is sometimes keeping many away. Consequently, modeling and forecasting ...

Innovations in technology have rendered the financial world more virtual and digitised, but in the new financial era, we are still not able to grasp the cryptocurrency market dynamically. The tools used for predicting and modelling the cryptocurrency ...

The Covid-19 pandemic has highlighted the importance of forecasting in managing public health. The two of the most commonly used approaches for time series forecasting methods are autoregressive (AR) and deep learning models (DL). While there ...

This research aims to analyze the calculation of volatility stage from five cryptocurrency products, which are Bitcoin, Ethereum, Binance Coin, Dashcoin, and Litecoin from 1st January 2018 to 1st April 2021 where it consists of calculation of each of ...