Value-at-Risk forecasting: A hybrid ensemble learning GARCH-LSTM based approach

Kakade, Kshitij; Jain, Ishan; Mishra, Aswini Kumar

doi:10.1016/j.resourpol.2022.102903

Cited by 25 publications

(7 citation statements)

References 41 publications

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“…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). (Ge et al 2022) systematically reviewed NN-based volatility forecasting.…”

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

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“…Hang et al (2021) proposed a new disintegration-integration paradigm VMD-GARCH/LSTM-LSTM model for carbon price prediction, in which the LSTM network predicts the low-frequency sub-model and GARCH model predicts the high-frequency sub-model, effectively reducing the prediction error [35]. Kakade et al (2023) proposed to combine LSTM and Bi LSTM models with GARCH-type models and found that the BI-LSTM model and GARCH-type model had the best performance and the lowest values of the two loss functions. Bi LSTM (bidirectional long short-term memory) is the process of making any neural network have sequential information in both the backward (future-to-past) or forward (past-to-future) directions [36].…”

Section: Introductionmentioning

confidence: 99%

“…Kakade et al (2023) proposed to combine LSTM and Bi LSTM models with GARCH-type models and found that the BI-LSTM model and GARCH-type model had the best performance and the lowest values of the two loss functions. Bi LSTM (bidirectional long short-term memory) is the process of making any neural network have sequential information in both the backward (future-to-past) or forward (past-to-future) directions [36]. BP and LSTM are both neural network methods, and LSTM is superior to the BP network method in terms of time series and sequence data processing and analysis.…”

Section: Introductionmentioning

confidence: 99%

Fluctuations and Forecasting of Carbon Price Based on A Hybrid Ensemble Learning GARCH-LSTM-Based Approach: A Case of Five Carbon Trading Markets in China

Liu,

Zhang,

Wang

et al. 2024

Sustainability

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Carbon trading risk management and policy making require accurate forecasting of carbon trading prices. Based on the sample of China’s carbon emission trading pilot market, this paper firstly uses the Augmented Dickey–Fuller test and Autoregressive conditional heteroscedasticity model to test the stationarity and autocorrelation of carbon trading price returns, uses the Generalized Autoregressive Conditional Heteroscedasticity family model to analyze the persistence, risk and asymmetry of carbon trading price return fluctuations, and then proposes a hybrid prediction model neural network (generalized autoregressive conditional heteroscedasticity–long short-term memory network) due to the shortcomings of GARCH models in carbon price fluctuation analysis and prediction. The model is used to predict the carbon trading price. The results show that the carbon trading pilots have different degrees of volatility aggregation characteristics and the volatility persistence is long, among which only the Shanghai and Beijing carbon trading markets have risk premiums. The other pilot returns have no correlation with risks, and the fluctuations of carbon trading prices and returns are asymmetrical. The prediction results of different models show that the root mean square error (RMSE) of Hubei, Shenzhen and Shanghai carbon trading pilots based on the GARCH-LSTM model is significantly lower than that of the single GARCH model, and the RMSE values are reduced by 0.0006, 0.2993 and 0.0151, respectively. The RMSE in the three pilot markets improved by 0.0007, 0.3011 and 0.0157, respectively, compared to the standalone LSTM model. At the same time, compared with the single model, the GARCH-LSTM model significantly increased the R^2 value in Hubei (0.2000), Shenzhen (0.7607), Shanghai (0.0542) and Beijing (0.0595). Therefore, compared with other models, the GARCH-LSTM model can significantly improve the prediction accuracy of carbon price and provide a new idea for scientifically predicting the fluctuation of financial time series such as carbon price.

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“…In the past, researchers have suggested that GARCH is ineffective when used as a single forecasting model [47]. Compared to a single model, a hybrid model, preferably with DL algorithm, is expected to overcome the weaknesses of GARCH and to improve forecasting performance [48,49]. According to recent research on forecasting stock volatility, GARCH combined with LSTM was found to provide the best forecasting model among the other proposed models to predict copper price [47].…”

Section: Introductionmentioning

confidence: 99%

Heteroscedasticity effects as component to future stock market predictions using RNN-based models

Sadon,

Ismail,

Khamis

et al. 2024

PLoS ONE

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Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility is a crucial task and challenging. This study proposes a hybrid model that predicts future stock volatility values by considering the heteroscedasticity element of the stock price. The proposed model is a combination of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and a well-known Recurrent Neural Network (RNN) algorithm Long Short-Term Memory (LSTM). This proposed model is referred to as GARCH-LSTM model. The proposed model is expected to improve prediction accuracy by considering heteroscedasticity elements. First, the GARCH model is employed to estimate the model parameters. After that, the ARCH effect test is used to test the residuals obtained from the model. Any untrained heteroscedasticity element must be found using this step. The hypothesis of the ARCH test yielded a p-value less than 0.05 indicating there is valuable information remaining in the residual, known as heteroscedasticity element. Next, the dataset with heteroscedasticity is then modelled using an LSTM-based RNN algorithm. Experimental results revealed that hybrid GARCH-LSTM had the lowest MAE (7.961), RMSE (10.466), MAPE (0.516) and HMAE (0.005) values compared with a single LSTM. The accuracy of forecasting was also significantly improved by 15% and 13% with hybrid GARCH-LSTM in comparison to single LSTMs. Furthermore, the results reveal that hybrid GARCH-LSTM fully exploits the heteroscedasticity element, which is not captured by the GARCH model estimation, outperforming GARCH models on their own. This finding from this study confirmed that hybrid GARCH-LSTM models are effective forecasting tools for predicting stock price movements. In addition, the proposed model can assist investors in making informed decisions regarding stock prices since it is capable of closely predicting and imitating the observed pattern and trend of KLSE stock prices.

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Value-at-Risk forecasting: A hybrid ensemble learning GARCH-LSTM based approach

Cited by 25 publications

References 41 publications

From GARCH to Neural Network for Volatility Forecast

From GARCH to Neural Network for Volatility Forecast

Fluctuations and Forecasting of Carbon Price Based on A Hybrid Ensemble Learning GARCH-LSTM-Based Approach: A Case of Five Carbon Trading Markets in China

Heteroscedasticity effects as component to future stock market predictions using RNN-based models

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