Forecasting Commodity Market Returns Volatility: A Hybrid Ensemble Learning GARCH‐LSTM based Approach

Kakade, Kshitij; Mishra, Aswini Kumar; Ghate, Kshitish; Gupta, Shivang

doi:10.1002/isaf.1515

Cited by 7 publications

(2 citation statements)

References 45 publications

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“…Seo et al [41] proposed to use the output of the GARCH model as the input of the neural network to improve the neural network's prediction performance. Serkan, Koo, Kakade, and Zolfaghari [21,[42][43][44] also proposed to apply a combination of econometric and artificial intelligence models to price forecasting. Table 1 provides the models, forecasting targets, time scales, and conclusions of these studies.…”

Section: Combined Models Based On Artificial Intelligence and Econome...mentioning

confidence: 99%

A Garlic-Price-Prediction Approach Based on Combined LSTM and GARCH-Family Model

et al. 2022

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The frequent and sharp fluctuations in garlic prices seriously affect the sustainable development of the garlic industry. Accurate prediction of garlic prices can facilitate correct evaluation and scientific decision making by garlic practitioners, thereby avoiding market risks and promoting the healthy development of the garlic industry. To improve the prediction accuracy of garlic prices, this paper proposes a garlic-price-prediction method based on a combination of long short-term memory (LSTM) and multiple generalized autoregressive conditional heteroskedasticity (GARCH)-family models for the nonstationary and nonlinear characteristics of garlic-price series. Firstly, we obtain volatility characteristic information such as the volatility aggregation of garlic-price series by constructing GARCH-family models. Then, we leverage the LSTM model to learn the complex nonlinear relationships between the garlic-price series and the volatility characteristic information of the series, and predict the garlic price. We applied the proposed model to a real-world garlic dataset. The experimental results show that the prediction performance of the combined LSTM and GARCH-family model containing volatility characteristic information of garlic price is generally better than those of the separate models. The combined LSTM model incorporating GARCH and PGARCH models (LSTM-GP) had the best performance in predicting garlic price in terms of evaluation indexes, such as mean absolute error, root mean-square error, and mean absolute percentage error. The combined model of LSTM-GARCH provides the best results in garlic price prediction and can provide support for garlic price prediction.

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Section: Combined Models Based On Artificial Intelligence and Econome...mentioning

confidence: 99%

A Garlic-Price-Prediction Approach Based on Combined LSTM and GARCH-Family Model

et al. 2022

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“…In this group, it is relevant to highlight the exercises developed byOu & Wang (2014),Jung & Choi (2021),Liu and Fu (2016),Hu et al (2020), andKakade et al (2022). In these articles, authors used hybrid models to forecast volatility related to copper Price, Chinese interbank offered rate, commodities, Nasdaq composite index and Exchange rates.…”

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confidence: 99%

Use of Machine Learning in Volatility: A Review Using K-Means

Muñoz

Castañeda²

2023

rev.univ.empresa

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Recently, the use of machine learning (ML) in scientific disciplines has experienced an unprecedented increase. Finance has not been an exception. Several works have been published in recent years using mltechniques. However, one of the topics with the least number of developed papers is volatility in this context. Nevertheless, the data analyzed here suggest changes regarding this issue. Data obtained from the Web of Science database show that between 2001 and 2010 there were 33 published papers associated with this topic. Surprisingly, between 2019 and 2023, 189 manuscripts have been published related to this topic. The purpose of this work is to review the works related to the applications of ml in volatility. For this, a classification of the main proposals on this topic is proposed following a narrative methodology, accompanied by a statistical and bibliometric analysis in which novel techniques such as K-means were used. The results are suggestive. Although most papers focus on volatility prediction through neural networks and support vector machines, there is a lack of studies related to volatility transmission, calibration of volatility surfaces,and corporate finance. Moreover, the obtained results indicate that there is a gap in the production of worksrelated to these topics in finance and economics specialized journals.

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Volatility forecasting and assessing risk of financial markets using multi-transformer neural network based architecture

Mishra,

Renganathan,

Gupta

2024

Engineering Applications of Artificial Intelligence

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Forecasting Commodity Market Returns Volatility: A Hybrid Ensemble Learning GARCH‐LSTM based Approach

Cited by 7 publications

References 45 publications

A Garlic-Price-Prediction Approach Based on Combined LSTM and GARCH-Family Model

A Garlic-Price-Prediction Approach Based on Combined LSTM and GARCH-Family Model

Use of Machine Learning in Volatility: A Review Using K-Means

Volatility forecasting and assessing risk of financial markets using multi-transformer neural network based architecture

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