Abstract
Tracking market fear in distress periods is a highly challenging and essential task of paramount practical relevance. If the future figures of market fear can be predicted in conjunction with explaining the dependence structure on predictor variables, market players at different levels can be benefited. The current work endeavors to model the Chicago Board Options Exchange’s Volatility Index (CBOE VIX) of the US, reflecting the extent of market fear in the futures market through the lens of applied predictive modeling and explainable artificial intelligence (AI). The methodological framework deploys two advanced forecasting tools, namely, Facebook Prophet and Uber Orbit, to gauge the temporal pattern of the CBOE VIX. The exercises have been carried out across different regimes characterized by varying degrees of volatility and uncertainty. It is revealed that the market fear in the US was relatively more predictable during the Pre-COVID-19 phase. The outcome of explainable AI analysis using Shapley additive explanations (SHAP) and accumulated local effect (ALE) plots indicates the past information of CBOE VIX exerts significant predictive influence, which largely explains the variation.
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Desetti, S.S., Ghosh, I. (2024). Prediction and Deeper Analysis of Market Fear in Pre-COVID-19, COVID-19 and Russia-Ukraine Conflict: A Comparative Study of Facebook Prophet, Uber Orbit and Explainable AI. In: Dasgupta, K., Mukhopadhyay, S., Mandal, J.K., Dutta, P. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2023. Communications in Computer and Information Science, vol 1955. Springer, Cham. https://doi.org/10.1007/978-3-031-48876-4_16
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