On the forecasting of multivariate financial time series using hybridization of DCC-GARCH model and multivariate ANNs

Fatima, Samreen; Uddin, Mudassir

doi:10.1007/s00521-022-07631-5

Cited by 8 publications

(4 citation statements)

References 43 publications

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“…The collected data underwent processing with the assistance of NNs, specifically the multilayer perceptron (MLP). The MLPs were utilised to study volatility during the COVID-19 pandemic (Fatima & Uddin, 2022;Ibrahim et al, 2022;Khansari et al, 2022;Naveed et al, 2023) and post-pandemic period (Sahiner et al, 2021). These models describe the intricate relationships between independent predictor variables (Lu et al, 2016).…”

Section: Methodsmentioning

confidence: 99%

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Effect of Health System Performance on Volatility During the Covid-19 Pandemic: A Neural Network Approach

Gheorghe,

Panazan

2024

Journal of Business Economics and Management

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The study proposes an assessment of the link between the performance of national health systems and volatility during the COVID-19 pandemic. Data from the World Health Organization was accessed regarding the Global Health Security Index of the states considered in the analysis as well as the categories based on which it is determined. To characterise volatility, a representative stock market index was considered for each of the 60 states analysed. Data processing was carried out using an artificial neural network. The main results show that: i) before the pandemic, the link between market volatility and the performance of national health systems was weak; ii) during the pandemic, the connection between the two variables is much stronger; iii) between the six categories that define the Global Health Security Index, norms, health, and prevention had the greatest influence on volatility.

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Section: Methodsmentioning

confidence: 99%

“…One such method that contributed to volatility estimation included the neural network (NN; Ge et al, 2022). This approach has acquired significant popularity due to successful applications in other domains (Fatima & Uddin, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Effect of Health System Performance on Volatility During the Covid-19 Pandemic: A Neural Network Approach

Gheorghe,

Panazan

2024

Journal of Business Economics and Management

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“…The volatility typically seen in stock prices, the GARCH model is widely used for modeling volatility and forecasting stock prices ( Mahajan, Thakan & Malik, 2022 ; Zeghdoudi, Lallouche & Remita, 2014 ). The prediction performance of the GARCH model is very sensitive to the accuracy with which its parameters are chosen ( Fatima & Uddin, 2022 ; Brooks & Burke, 2003 ). The volatility or uncertainty in asset values is a crucial aspect of stock price analysis ( Sen, Mehtab & Dutta, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Novel grey wolf optimizer based parameters selection for GARCH and ARIMA models for stock price prediction

Bagalkot,

Naik

2024

PeerJ Computer Science

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Stock price data often exhibit nonlinear patterns and dynamics in nature. The parameter selection in generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive integrated moving average (ARIMA) models is challenging due to stock price volatility. Most studies examined the manual method for parameter selection in GARCH and ARIMA models. These procedures are time-consuming and based on trial and error. To overcome this, we considered a GWO method for finding the optimal parameters in GARCH and ARIMA models. The motivation behind considering the grey wolf optimizer (GWO) is one of the popular methods for parameter optimization. The novel GWO-based parameters selection approach for GARCH and ARIMA models aims to improve stock price prediction accuracy by optimizing the parameters of ARIMA and GARCH models. The hierarchical structure of GWO comprises four distinct categories: alpha (α), beta (β), delta (δ) and omega (ω). The predatory conduct of wolves primarily encompasses the act of pursuing and closing in on the prey, tracing the movements of the prey, and ultimately launching an attack on the prey. In the proposed context, attacking prey is a selection of the best parameters for GARCH and ARIMA models. The GWO algorithm iteratively updates the positions of wolves to provide potential solutions in the search space in GARCH and ARIMA models. The proposed model is evaluated using root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE). The GWO-based parameter selection for GARCH and ARIMA improves the performance of the model by 5% to 8% compared to existing traditional GARCH and ARIMA models.

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“…To predict US and Taiwan stocks, a graph-based CNN-LSTM model was proposed [28]. In multivariate time-series stock price data, which consisted of daily price data of five stock markets, a dynamic conditional correlation GARCH with an artificial NN was proposed by [29] for predicting risk volatility. In addition, in the case of multivariate time-series analysis, [30] proposed an encoder-decoder-based deep learning algorithm to discover interdependencies between timeseries.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Model for Multivariate High-Frequency Time-Series Data: Financial Market Index Prediction

Noh,

Kim,

Hong

et al. 2023

Mathematics

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The stock index is actively used for the realization of profits using derivatives and via the hedging of assets; hence, the prediction of the index is important for market participants. As market uncertainty has increased during the COVID-19 pandemic and with the rapid development of data engineering, a situation has arisen wherein extensive amounts of information must be processed at finer time intervals. Addressing the prevalent issues of difficulty in handling multivariate high-frequency time-series data owing to multicollinearity, resource problems in computing hardware, and the gradient vanishing problem due to the layer stacking in recurrent neural network (RNN) series, a novel algorithm is developed in this study. For financial market index prediction with these highly complex data, the algorithm combines ResNet and a variable-wise attention mechanism. To verify the superior performance of the proposed model, RNN, long short-term memory, and ResNet18 models were designed and compared with and without the attention mechanism. As per the results, the proposed model demonstrated a suitable synergistic effect with the time-series data and excellent classification performance, in addition to overcoming the data structure constraints that the other models exhibit. Having successfully presented multivariate high-frequency time-series data analysis, this study enables effective investment decision making based on the market signals.

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On the forecasting of multivariate financial time series using hybridization of DCC-GARCH model and multivariate ANNs

Cited by 8 publications

References 43 publications

Effect of Health System Performance on Volatility During the Covid-19 Pandemic: A Neural Network Approach

Effect of Health System Performance on Volatility During the Covid-19 Pandemic: A Neural Network Approach

Novel grey wolf optimizer based parameters selection for GARCH and ARIMA models for stock price prediction

Deep Learning Model for Multivariate High-Frequency Time-Series Data: Financial Market Index Prediction

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