Forecasting of Realised Volatility with the Random Forests Algorithm

Luong, Chuong; Dokuchaev, Nikolai

doi:10.3390/jrfm11040061

Cited by 38 publications

(20 citation statements)

References 25 publications

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“…Using Machine learning algorithms to improve traditional methods: Hybrid machine learning techniques can be implemented to improve the performance of the traditional GARCH-based and stochastic methods [81], [83], [84]. Artificial Neural Networks can be stacked with both GARCH and Stochastic Volatility models to produce more accurate volatility forecasting.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Machine Learning for Financial Risk Management: A Survey

et al. 2020

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Financial risk management avoids losses and maximizes profits, and hence is vital to most businesses. As the task relies heavily on information-driven decision making, machine learning is a promising source for new methods and technologies. In recent years, we have seen increasing adoption of machine learning methods for various risk management tasks. Machine-learning researchers, however, often struggle to navigate the vast and complex domain knowledge and the fast-evolving literature. This paper fills this gap, by providing a systematic survey of the rapidly growing literature of machine learning research for financial risk management. The contributions of the paper are four-folds: First, we present a taxonomy of financial-risk-management tasks and connect them with relevant machine learning methods. Secondly, we highlight significant publications in the past decade. Thirdly, we identify major challenges being faced by researchers in this area. And finally, we point out emerging trends and promising research directions.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Machine Learning for Financial Risk Management: A Survey

et al. 2020

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“…Accuracy, recall, and precision measures are defined as follows (Luong and Dokuchaev 2018;Hamori et al 2018):…”

Section: Resultsmentioning

confidence: 99%

Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning

Hong

Yeo

Cho

et al. 2018

JRFM

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Since not all suppliers are to be managed in the same way, a purchasing strategy requires proper supplier segmentation so that the most suitable strategies can be used for different segments. Most existing methods for supplier segmentation, however, either depend on subjective judgements or require significant efforts. To overcome the limitations, this paper proposes a novel approach for supplier segmentation. The objective of this paper is to develop an automated and effective way to identify core suppliers, whose profit impact on a buyer is significant. To achieve this objective, the application of a supervised machine learning technique, Random Forests (RF), to e-invoice data is proposed. To validate the effectiveness, the proposed method has been applied to real e-invoice data obtained from an automobile parts manufacturer. Results of high accuracy and the area under the curve (AUC) attest to the applicability of our approach. Our method is envisioned to be of value for automating the identification of core suppliers. The main benefits of the proposed approach include the enhanced efficiency of supplier segmentation procedures. Besides, by utilizing a machine learning method to e-invoice data, our method results in more reliable segmentation in terms of selecting and weighting variables.

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“…Motegi et al (2020) propose moving average threshold HAR models as a combination of HAR and threshold autoregression. In contrast to these linear models for RV forecasting, Luong and Dokuchaev (2018) introduced a nonlinear model using the random forest method.…”

Section: Literature Review Of Volatility Forecasting Modelsmentioning

confidence: 99%

New Dataset for Forecasting Realized Volatility: Is the Tokyo Stock Exchange Co-Location Dataset Helpful for Expansion of the Heterogeneous Autoregressive Model in the Japanese Stock Market?

Higashide

Tanaka

Kinkyo

et al. 2021

JRFM

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This study analyzes the importance of the Tokyo Stock Exchange Co-Location dataset (TSE Co-Location dataset) to forecast the realized volatility (RV) of Tokyo stock price index futures. The heterogeneous autoregressive (HAR) model is a popular linear regression model used to forecast RV. This study expands the HAR model using the TSE Co-Location dataset, stock full-board dataset and market volume dataset based on the random forest method, which is a popular machine learning algorithm and a nonlinear model. The TSE Co-Location dataset is a new dataset. This is the only information that shows the transaction status of high-frequency traders. In contrast, the stock full-board dataset shows the status of buying and selling dominance. The market volume dataset is used as a proxy for liquidity and is recognized as important information in finance. To the best of our knowledge, this study is the first to use the TSE co-location dataset. The experimental results show that our model yields a higher forecast out-of-sample accuracy of RV than the HAR model. Moreover, we find that the TSE Co-Location dataset has become more important in recent years, along with the increasing importance of high-frequency trading.

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Forecasting of Realised Volatility with the Random Forests Algorithm

Cited by 38 publications

References 25 publications

Machine Learning for Financial Risk Management: A Survey

Machine Learning for Financial Risk Management: A Survey

Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning

New Dataset for Forecasting Realized Volatility: Is the Tokyo Stock Exchange Co-Location Dataset Helpful for Expansion of the Heterogeneous Autoregressive Model in the Japanese Stock Market?

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