Determining the relationship between speculative activity and crude oil price volatility, using artificial neural networks

Khan, Saqib; Hasanabadi, Hamed Shafiee; Mayorga, René V.

doi:10.1109/icict.2017.8320178

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Attempts have been made to incorporate stylized facts into the ML model. Most hybrid solutions take the outputs or parameters of stochastic models as features input to different types of NNs, like Multi-layer perceptron (Khan, Hasanabadi, and Mayorga 2017;Pyo and Lee 2018;Kristjanpoller and Minutolo 2016), LSTM (Kristjanpoller, Fadic, and Minutolo 2014;Liu and So 2020;Rahimikia and Poon 2020;Kim and Won 2018), Transformer (Ramos-Pérez, Alonso-González, and Núñez-Velázquez 2021), and attention neural network (Lin and Sun 2021;Zheng et al 2019). The ensemble approach combines the outputs from GARCH and NN (Kakade, Jain, and Mishra 2022;Hu, Ni, and Wen 2020).…”

Section: Neural Network Volatility Forecasting Modelsmentioning

confidence: 99%

From GARCH to Neural Network for Volatility Forecast

Zhao,

Zhu,

et al. 2024

AAAI

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Volatility, as a measure of uncertainty, plays a crucial role in numerous financial activities such as risk management. The Econometrics and Machine Learning communities have developed two distinct approaches for financial volatility forecasting: the stochastic approach and the neural network (NN) approach. Despite their individual strengths, these methodologies have conventionally evolved in separate research trajectories with little interaction between them. This study endeavors to bridge this gap by establishing an equivalence relationship between models of the GARCH family and their corresponding NN counterparts. With the equivalence relationship established, we introduce an innovative approach, named GARCH-NN, for constructing NN-based volatility models. It obtains the NN counterparts of GARCH models and integrates them as components into an established NN architecture, thereby seamlessly infusing volatility stylized facts (SFs) inherent in the GARCH models into the neural network. We develop the GARCH-LSTM model to showcase the power of GARCH-NN approach. Experiment results validate that amalgamating the NN counterparts of the GARCH family models into established NN models leads to enhanced outcomes compared to employing the stochastic and NN models in isolation.

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Section: Neural Network Volatility Forecasting Modelsmentioning

confidence: 99%

From GARCH to Neural Network for Volatility Forecast

Zhao,

Zhu,

et al. 2024

AAAI

View full text Add to dashboard Cite

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Neural Network–Based Financial Volatility Forecasting: A Systematic Review

Lalbakhsh

Isai³

et al. 2022

ACM Comput. Surv.

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Volatility forecasting is an important aspect of finance as it dictates many decisions of market players. A snapshot of state-of-the-art neural network–based financial volatility forecasting was generated by examining 35 studies, published after 2015. Several issues were identified, such as the inability for easy and meaningful comparisons, and the large gap between modern machine learning models and those applied to volatility forecasting. A shared task was proposed to evaluate state-of-the-art models, and several promising ways to bridge the gap were suggested. Finally, adequate background was provided to serve as an introduction to the field of neural network volatility forecasting.

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Determining the relationship between speculative activity and crude oil price volatility, using artificial neural networks

Cited by 2 publications

References 6 publications

From GARCH to Neural Network for Volatility Forecast

From GARCH to Neural Network for Volatility Forecast

Neural Network–Based Financial Volatility Forecasting: A Systematic Review

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