SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted What are the comparative effects of the subprime crises and COVID-19 pandemic on US stock market volatility: an empirical study(Essex University, 2023-12-04) Alajmi, Mona; Nawosah, VivekThis study examines the effects of the subprime crises and COVID-19 on stock market volatility in the United States, utilizing the GJR GARCH model. The data utilized is the daily closing prices of the S&P 500 stock index. The study's findings highlight the prevalence of volatility clustering during the subprime crisis that occurred between 2007 and 2008. However, the lack of a substantial asymmetric evidence suggests an absence of compelling empirical proof for the existence of asymmetry within that period and during the COVID-19 pandemic, it was observed that there were occurrences of volatility clustering and asymmetry. This shows that compared to positive shocks, negative shocks have a more significant effect on increasing volatility which is commonly known as leverage effects.16 0Item Restricted Predicting Volatility for Cryptocurrencies: A Comparison in Using GARCH Models vs. Machine Learning LSTM Models(Saudi Digital Library, 2023-09-22) Bugis, Tala; Song, XiaojingFinancial researchers and traders seeking trustworthy forecasting tools have a formidable barrier in cryptocurrency markets, which are volatile and decentralised. This study explores cryptocurrency price volatility prediction and provides insights into digital asset-specific models and methods. The issue is that bitcoin markets defy predictability. These complex patterns are decoded using GARCH, recurrent neural networks, and hybrid models. The significant findings emphasise customised solutions. Bidirectional Long Short-Term Memory (BI-LSTM) models with 1D convolutional layers outperformed standard models in predicting Binance Coin (BNB) and Ripple (XRP) volatility due to their ability to capture complicated temporal connections. With asymmetric reactions, Ethereum's (ETH) volatility required unique approaches like the GRJ-GARCH model. This study concludes that the cryptocurrency ecosystem is complex and requires specialised solutions for each digital asset. Our findings further support the Efficient Market Hypothesis (EMH), which emphasises market efficiency in forecasting models. Future research and applications must incorporate robustness testing, regulatory compliance, and external factor integration as cryptocurrency marketplaces mature. Further research into hybrid models that combine GARCH and LSTM strengths is promising. This analysis helps us predict bitcoin volatility and shows how dynamic cryptocurrency markets are. Recognising their distinct traits and adapting forecasting models allows us to leverage the predictive potential needed in this continuously changing market.43 0