Machine Learning Solutions for Fast Real Estate Derivatives Pricing
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Economics, Econometrics and Finance (miscellaneous)
Link
https://link.springer.com/content/pdf/10.1007/s10614-023-10506-z.pdf
Reference45 articles.
1. Aimi, A., & Guardasoni, C. (2018). Collocation boundary element method for the pricing of geometric Asian options. Engineering Analysis with Boundary Elements, 92, 90–100. https://doi.org/10.1016/j.enganabound.2017.10.007
2. Arin, E., & Ozbayoglu, A. M. (2022). Deep learning based hybrid computational intelligence models for options pricing. Computational Economics, 59(1), 39–58. https://doi.org/10.1007/s10614-020-10063-9
3. Baldominos, A., Blanco, I., Moreno, A. J., Iturrarte, R., Bernardez, O., & Afonso, C. (2018). Identifying real estate opportunities using machine learning. Applied Sciences-Basel, 8(11), 2321. https://doi.org/10.3390/app8112321
4. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/a:1010933404324
5. Buttimer, R. J., Kau, J. B., & Slawson, V. C. (1997). A model for pricing securities dependent upon a real estate index. Journal of Housing Economics, 6(1), 16–30. https://doi.org/10.1006/jhec.1997.0202
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