Regime-Switching Fractionally Integrated Asymmetric Power Neural Network Modeling of Nonlinear Contagion for Chaotic Oil and Precious Metal Volatilities

Bildirici, Melike; Ersin, Özgür Ömer

doi:10.3390/fractalfract6120703

Cited by 3 publications

(1 citation statement)

References 81 publications

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“…More recently, with the advance of machine learning methodologies, multi-layer perceptron neural networks have been developed to accommodate regime switching and long memory in volatility modeling. Examples of those models with applications in financial data can be found in Bildirici and Ersin [67], Bildirici and Ersin [68], and Bildirici and Ersin [69], among others. A systematic comparison of the proposed MRS-FIEGACH and those frameworks remains for the future.…”

Section: Discussionmentioning

confidence: 99%

Modeling Long Memory and Regime Switching with an MRS-FIEGARCH Model: A Simulation Study

Zhang

Shi

2023

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Recent research suggests that long memory can be caused by regime switching and is easily confused with it. However, if the causes of confusion were properly controlled, they could be distinguished. Motivated by this idea, our study aims to distinguish between the long memory and regime switching of financial volatility. We firstly modeled the long memory and regime switching of volatility using the Fractionally Integrated Exponential GARCH (FIEGARCH) and Markov Regime-Switching EGARCH (MRS-EGARCH) frameworks, respectively, and performed a simulation study on their finite-sample properties when innovations followed a non-normal distribution. Subsequently, we demonstrated the confusion between the FIEGARCH and MRS-EGARCH processes using simulations. A recent study theoretically proved that the time-varying smoothing probability series can induce the presence of significant long memory in the regime-switching process. To control for its effect, the two-stage two-state FIEGARCH and MRS-FIEGARCH frameworks are proposed. The Monte Carlo studies showed that both frameworks can effectively distinguish between the pure FIEGARCH and pure MRS-EGARCH processes. When the MRS-FIEGARCH model was further employed to fit series generated with the MRS-FIEGARCH process, it outperformed the ordinary FIEGARCH model. Finally, an empirical study of NASDAQ index return was conducted to demonstrate that our MRS-FIEGARCH model can provide potentially more reliable long-memory estimates, identify the volatility states and outperform both the FIEGARCH and MRS-EGARCH models.

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Section: Discussionmentioning

confidence: 99%

Modeling Long Memory and Regime Switching with an MRS-FIEGARCH Model: A Simulation Study

Zhang

Shi

2023

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Bitcoin’s Energy Consumption, Price Volatility, and Environmental Pollution: Contagion and Causality Dynamics Under Heteroskedasticity and Nonlinearity

Bildirici,

Ersin

2024

International Journal of Energy Research

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Bitcoin’s (BT) energy demand due to the proof of work mining algorithm and transaction methods has been criticized in recent literature because of BT’s global energy consumption (EC) being equal to or even greater than that of some industrialized economies. This study explores the contagion and causality dynamics under nonlinearity and heteroskedasticity with Markovian‐type regime switches among BT price volatility, EC from BT, and its effects on the global carbon dioxide (CO2) emissions with a daily sample of January 2, 2012–December 24, 2023. In the empirical methodology, following the preliminary tests that indicated nonlinearity and heteroskedasticity, this study employed novel Markov‐switching‐based nonlinear volatility copula and causality models to capture the marginal distributions of BT, its EC and CO2 emissions, and their joint distributions for contagion and tail dependence and determine nonlinear and asymmetric causality relations in distinct regimes of high and low volatility governed by Markov chains. The empirical results indicate significant and positive copula parameters with high magnitudes demonstrating asymmetric contagion and tail dependence occurring at the extreme levels of BT price volatility in the upper and lower tails during both regimes. Novel Markov‐switching‐based copula causality tests designated unidirectional causality from BT to CO2, as well as from BT’s EC to CO2 emissions during each regime coupled with existent bidirectional causality confirming cyclical feedback effects between BT prices and its EC in both regimes. For robustness and comparison, single‐regime copula models were employed, and their findings confirmed the results concerning significant and positive tail dependence and contagion. However, the single‐regime approach led to a set of inconsistencies in causality results owing to omitting nonlinearity while capturing heteroskedasticity and confirming the nonrejection of causality between BT, EC, and CO2 emissions. Important policy recommendations include green alternatives to cryptocurrency‐mining algorithms.

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Comparison of Standalone and Hybrid Machine Learning Models for Prediction of Critical Heat Flux in Vertical Tubes

et al. 2023

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Critical heat flux (CHF) is an essential parameter that plays a significant role in ensuring the safety and economic efficiency of nuclear power facilities. It imposes design and operational restrictions on nuclear power plants due to safety concerns. Therefore, accurate prediction of CHF using a hybrid framework can assist researchers in optimizing system performance, mitigating risk of equipment failure, and enhancing safety measures. Despite the existence of numerous prediction methods, there remains a lack of agreement regarding the underlying mechanism that gives rise to CHF. Hence, developing a precise and reliable CHF model is a crucial and challenging task. In this study, we proposed a hybrid model based on an artificial neural network (ANN) to improve the prediction accuracy of CHF. Our model leverages the available knowledge from a lookup table (LUT) and then employs ANN to further reduce the gap between actual and predicted outcomes. To develop and assess the accuracy of our model, we compiled a dataset of around 5877 data points from various sources in the literature. This dataset encompasses a diverse range of operating parameters for two-phase flow in vertical tubes. The results of this study demonstrate that the proposed hybrid model performs better than standalone machine learning models such as ANN, random forest, support vector machine, and data-driven lookup tables, with a relative root-mean-square error (rRMSE) of only 9.3%. We also evaluated the performance of the proposed hybrid model using holdout and cross-validation techniques, which demonstrated its robustness. Moreover, the proposed approach offers valuable insights into the significance of various input parameters in predicting CHF. Our proposed system can be utilized as a real-time monitoring tool for predicting extreme conditions in nuclear reactors, ensuring their safe and efficient operation.

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Regime-Switching Fractionally Integrated Asymmetric Power Neural Network Modeling of Nonlinear Contagion for Chaotic Oil and Precious Metal Volatilities

Cited by 3 publications

References 81 publications

Modeling Long Memory and Regime Switching with an MRS-FIEGARCH Model: A Simulation Study

Modeling Long Memory and Regime Switching with an MRS-FIEGARCH Model: A Simulation Study

Bitcoin’s Energy Consumption, Price Volatility, and Environmental Pollution: Contagion and Causality Dynamics Under Heteroskedasticity and Nonlinearity

Comparison of Standalone and Hybrid Machine Learning Models for Prediction of Critical Heat Flux in Vertical Tubes

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