AI/ML Case Study: Multi-Domain Asset Class Risk Prediction (Equities, Crypto, and Real-Estate)

Authors

  • Shaikh Sarfarazurrehman

Keywords:

Multi-domain risk prediction, ensemble learning, volatility clustering, SHAP, VaR, cross-asset correlations

Abstract

This study develops an AI/ML framework for predicting risk across equities, cryptocurrencies,and real estate, addressing the challenges of data heterogeneity, temporal misalignment, andvolatility asymmetry

References

Alvarez, F., Roman-Rangel, E., & Montiel, L. V. (2022). Incremental learning for property

price estimation using location-based services and open data. Engineering Applications of

Artificial Intelligence, 107, 104513. https://doi.org/10.1016/j.engappai.2021.104513

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Published

2024-05-10

How to Cite

Shaikh Sarfarazurrehman. (2024). AI/ML Case Study: Multi-Domain Asset Class Risk Prediction (Equities, Crypto, and Real-Estate) . Journal of Computational Analysis and Applications (JoCAAA), 33(05), 1746–1765. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/2762

Issue

Vol. 33 No. 05 (2024): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.