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This paper contributes to the ongoing debate on the nature and characteristics of the volatility transmission channels of major crash events in international stock markets between 03 July 1997 and 09 March 2021. Using dynamic conditional correlations (DCC) for conditional correlations and volatility clustering, GARCH-BEKK for the direction of transmission of disturbances, and the Diebold-Yilmaz spillover index for the level of volatility contagion, the paper finds that the climbs in external shock transmissions have long-lasting impacts in domestic markets due to the contagion effect during crisis periods. The findings also reveal that the heavier magnitude of financial stress is transmitted between Asian countries via the Hong Kong stock market. Additionally, the degree of volatility spillovers between advanced and emerging equity markets is smaller compared to the pure spillovers between advanced markets or emerging markets, offering a window of opportunity for international market participants in terms of portfolio diversification and risk management applications. Furthermore, the study introduces a novel early warning system created by integrating DCC correlations with a state-of-the-art deep learning model to predict the global financial crisis and COVID-19 crisis. The experimental analysis of long short-term memory network finds evidence of contagion risk by verifying bursts in volatility spillovers and generating signals with high accuracy before the 12-month crisis period. This provides supplementary information that contributes to the decision-making process of practitioners, as well as offering indicative evidence that facilitates the assessment of market vulnerability for policymakers. [ABSTRACT FROM AUTHOR]

Nowadays, event forecasting in Twitter can be considered an essential, significant and difficult issue. Maximum conventional methods are focusing on temporal events like sports or elections. These methods do not calculate the spatial features too their correlation analysis. Hence, this paper proposes an Improved Deep Belief Neural Network (iDBNN) for civil unrest event forecasting in twitter data. This proposed method is utilized to forecast the future event with the consideration of the tweets. The proposed method is designed with three phases named as pre-processing phase, feature extraction phase, and civil unrest event forecasting. Initially, the proposed method is used to train the Hong Kong Protest event 2019 tweet data for forecasting events. In the pre-processing phase, removal of special symbol, removal of URL, username removal, tokenization and stop word removal are done. After that, the essential features such as domain weight, event weight, textual similarity, spatial similarity, temporal similarity, and Relative Document-Term Frequency Difference (RDTFD) are extracted and then applied for training the proposed model. To empower the training phase of proposed iDBNN method, the Jellyfish Algorithm is utilized to select optimal weight parameter coefficients of DBNN for training the model parameters. The projected technique is authenticated by statistical capacities and compared with the conventional methods such as Hidden Markov Model (HMM) and Random Forest (RF) respectively. Comparing with other traditional methods, the proposed model shows better performance in terms of prediction and processing time. The iDBNN model shows 91% prediction accuracy that is much higher than the traditional DBNN. [ABSTRACT FROM AUTHOR]