A Machine Learning Approach to Volatility Forecasting

Christensen, Kim; Siggaard, Mathias; Veliyev, Bezirgen

doi:10.1093/jjfinec/nbac020

Cited by 52 publications

(23 citation statements)

References 67 publications

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“…We also investigate if an EMS could be developed in order to deliver more interesting results than the usual robust strategy, which does not seek to maximize profits but to make sure H 2 is delivered. Machine learning has shown its ability to take advantage of both volatile and exogenous information [4,5], the kind of which is increasingly common for energy markets and for the behaviour of small systems.…”

Section: Contextmentioning

confidence: 99%

Usage of GAMS-based Digital Twins and Clustering to Improve Energetic Systems Control

Gronier¹,

Maréchal²,

Geissler³

et al. 2022

Preprint

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With increasing constraints on energy and resource markets and the non-decreasing trend in energy demand, the need for relevant clean energy generation and storage solutions is growing and is gradually reaching the individual home. But small-scale energy storage is still an expensive investment in 2022 and the risk/reward ratio is not yet attractive enough for individual homeowners. One solution is for homeowners not to store excess clean energy individually but to produce hydrogen for mutual use. In this paper a collective production of hydrogen for a daily filling of a bus is considered. Following our previous work on the subject, the investigation consists of finding an optimal buy/sell rule to the grid, and the use of the energy with an additional objective: mobility. The dominant technique in the energy community is reinforcement learning, which is however difficult to use when the learning data is limited as in our study. We chose a less data-intensive and yet technically well-documented approach. Our results show that rulebooks, different but more interesting than the usual robust rule, exist and can be cost-effective. But they require fine-tuning as to not deteriorate system performance. In some cases, it is worth missing the H2 production requirement in exchange for higher economic performance.

show abstract

Section: Contextmentioning

confidence: 99%

Usage of GAMS-based Digital Twins and Clustering to Improve Energetic Systems Control

Gronier¹,

Maréchal²,

Geissler³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…We also investigate if an EMS could be developed in order to deliver more interesting results than the usual robust strategy, which does not seek to maximize profits but to ensure that H 2 is delivered. Machine learning has shown its ability to take advantage of both volatile and exogenous information [6,7], the kind of which is increasingly common for energy markets and for the behavior of small systems.…”

Section: Contextmentioning

confidence: 99%

Usage of GAMS-Based Digital Twins and Clustering to Improve Energetic Systems Control

Gronier

Maréchal²,

Geissler³

et al. 2022

Energies

View full text Add to dashboard Cite

With the increasing constraints on energy and resource markets and the non-decreasing trend in energy demand, the need for relevant clean energy generation and storage solutions is growing and is gradually reaching the individual home. However, small-scale energy storage is still an expensive investment in 2022 and the risk/reward ratio is not yet attractive enough for individual homeowners. One solution is for homeowners not to store excess clean energy individually but to produce hydrogen for mutual use. In this paper, a collective production of hydrogen for a daily filling of a bus is considered. Following our previous work on the subject, the investigation consists of finding an optimal buy/sell rule to the grid, and the use of the energy with an additional objective: mobility. The dominant technique in the energy community is reinforcement learning, which however is difficult to use when the learning data is limited, as in our study. We chose a less data-intensive and yet technically well-documented approach. Our results show that rulebooks, different but more interesting than the usual robust rule, exist and can be cost-effective. In some cases, they even show that it is worth punctually missing the H2 production requirement in exchange for higher economic performance. However, they require fine-tuning as to not deteriorate the system performance.

show abstract

“…In addition, traditional machine learning has been viewed as a mainstay of volatility prediction, and its stable ability to fit with high accuracy cannot be ignored. Christensen, Siggaard, and Veliyev make a compelling case that multiple machine learning algorithms, including regularization and regression trees, have a more pronounced long-term memory advantage over heteroskedastic autoregressions (HARs) in volatility forecasting [8]. Wang and Guo went a step further by integrating multiple machine learning methods and proposed the DWT-ARIMA-GSXGB hybrid model [9].…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Performance of Different Machine Learning Methods in Predicting VIX Volatility

Zhai

2024

AEMPS

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As a matter of fact, index volatility has always been one of the key indicators of the state of an index and a reflection of investor confidence and expectations in the market. Among various indicators, the VIX, which is also known as the "Panic Index", has always been viewed by the market as a barometer of the state of the economy. With this in mind, the purpose of this study is to investigate the process of Random Forest, Support Vector Regression as well as XGBoost in predicting VIX volatility and to evaluate their performance. Based on the evaluations, experiments in this study show that XGBoost performs optimally for smaller, low-dimensional time series data. According to the analysis, this study strongly demonstrates the excellent performance as well as great potential of machine learning algorithms in volatility prediction. Overall, these results will provide a feasible solution for volatility prediction with machine learning algorithms.

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A Machine Learning Approach to Volatility Forecasting

Cited by 52 publications

References 67 publications

Usage of GAMS-based Digital Twins and Clustering to Improve Energetic Systems Control

Usage of GAMS-based Digital Twins and Clustering to Improve Energetic Systems Control

Usage of GAMS-Based Digital Twins and Clustering to Improve Energetic Systems Control

Comparison of the Performance of Different Machine Learning Methods in Predicting VIX Volatility

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