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1. [CrossRef] Feng, Jingyu, Ying Yuan, and Mingxuan Jiang. 2024. Are stablecoins better safe havens or hedges against global stock markets than other assets? comparative analysis during the COVID-19 pandemic. International Review of Economics & Finance 92: 275–301.

2. [CrossRef] Forbes, Kristin, and Roberto Rigobon. 2001. Measuring contagion: Conceptual and empirical issues. In International Financial Contagion.
   Paper not yet in RePEc: Add citation now
   
3. [CrossRef] J. Risk Financial Manag. 2024, 17, 567 15 of 15 Khan, Mohammed Arshad, Md. Mobashshir Hussain, Asif Pervez, Mohd Atif, Rohit Bansal, and Hamad A. Alhumoudi. 2022. Intraday price discovery between spot and futures markets of nifty 50: An empirical study during the times of COVID-19. Journal of Mathematics 2022: 9. [CrossRef] Kim, Ha Young, and Chang Hyun Won. 2018. Forecasting the volatility of stock price index: A hybrid model integrating lstm with multiple garch-type models. Expert Systems with Applications 103: 25–37. [CrossRef] LE, Thao Phan Thi Dieu, and Hieu Luong Minh TRAN. 2021. The contagion effect from U.S. stock market to the vietnamese and the philippine stock markets: The evidence of DCC—GARCH model. Journal of Asian Finance, Economics and Business 8: 759–70.
   Paper not yet in RePEc: Add citation now
   
4. Ben-Ahmed, Kais, Saliha Theiri, and Nazih Kasraoui. 2023. Short-term effect of COVID-19 pandemic on cryptocurrency markets: A dcc-garch model analysis. Heliyon 9: e18847. [CrossRef] [PubMed] Ben Salem, Leila, Montassar Zayati, Ridha Nouira, and Christophe Rault. 2024. Volatility spillover between oil prices and main exchange rates: Evidence from a dcc-garch-connectedness approach. Resources Policy 91: 104880. [CrossRef] Billah, Masum, Azmery Sultana, Farzana Bhuiya, and Mohammed Golam Kaosar. 2024. Stock price prediction: Comparison of different moving average techniques using deep learning model. Neural Computing & Applications 36: 5861–71. [CrossRef] Bollerslev, Tim. 1986. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31: 307–27. [CrossRef] Botunac, Ive, Jurica Bosna, and Maja Matetić. 2024. Optimization of traditional stock market strategies using the lstm hybrid approach.
   Paper not yet in RePEc: Add citation now
   
5. bioinspired optimization methods and their applications. Bioma 2020: 161–73. [CrossRef] Tsukuda, Yoshihiko, Junji Shimada, and Tatsuyoshi Miyakoshi. 2020. The multivariate garch model and its application to east asian financial market integration. In Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning (In 4 Volumes).
   Paper not yet in RePEc: Add citation now
   
6. Complexity 2021: 13. [CrossRef] Jung, Gunho, and Sun-Yong Choi. 2021. Forecasting foreign exchange volatility using deep learning autoencoder-lstm techniques.
   Paper not yet in RePEc: Add citation now
   
7. Complexity 2021: 6647534. [CrossRef] Kaur, Manjot, and Aakash Mohta. 2019. A review of deep learning with recurrent neural network. Paper presented at the 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, November 27–29, pp. 460–45.
   Paper not yet in RePEc: Add citation now
   
8. Econometrica 50: 987–1007. [CrossRef] Engle, Robert. 2002. Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics 20: 339–50. [CrossRef] Felix, Robert. 2016. Examining the spread of high quality reporting through the corporate network. Advances in Accounting 32: 31–41.
   Paper not yet in RePEc: Add citation now
   
9. Information 15: 136. [CrossRef] Celik, Sibel. 2012. The more contagion effect on emerging markets: The evidence of dcc-garch model. Economic Modelling 29: 1946–59.
   Paper not yet in RePEc: Add citation now
   
10. International Journal of Project Management 39: 217–22. [CrossRef] Sevim, Cuneyt, Asil Oztekin, Ozkan Bali, Serkan Gumus, and Erkam Guresen. 2014. Developing an early warning system to predict currency crises. European Journal of Operational Research 237: 1095–104. [CrossRef] Si, Yuancheng, Saralees Nadarajah, Zongxin Zhang, and Chunmin Xu. 2024. Modeling opening price spread of shanghai composite index based on arima-gru/lstm hybrid model. PLoS ONE 19: e0299164. [CrossRef] Thill, Markus Thill, Wolfgang Konen, and Thomas Back. 2020. Time series encodings with temporal convolutional networks.
    Paper not yet in RePEc: Add citation now
    

1. Analysis of Financial Contagion and Prediction of Dynamic Correlations During the COVID-19 Pandemic: A Combined DCC-GARCH and Deep Learning Approach. (2024). Mansilla, Alan ; Espinoza, Jenny ; Chung, Victor.
   In: JRFM.
   RePEc:gam:jjrfmx:v:17:y:2024:i:12:p:567-:d:1546158.

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