Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/323350 
Authors: 
Year of Publication: 
2024
Citation: 
[Journal:] Computational Economics [ISSN:] 1572-9974 [Volume:] 65 [Issue:] 5 [Publisher:] Springer US [Place:] New York [Year:] 2024 [Pages:] 2853-2871
Publisher: 
Springer US, New York
Abstract: 
Abstract The study focuses on constructing a mathematical housing market threatened by a major catastrophic event or crash. It incorporates the worst-case scenario portfolio optimization problem as introduced in Korn and Wilmott (Int J Theor Appl Finance 5(02):171–187, 2002) into housing markets. The standard stochastic models for housing markets assume a geometric Brownian motion and neglect sudden housing price falls during crash times. However, the size, timing, and frequency of crashes have to be included in such models. By incorporating the worst-case portfolio optimization problem into housing markets, this study introduces a methodology to construct portfolios for large investors that are robust and resilient to extreme housing market conditions. The worst-case portfolio optimization approach can be used in housing markets to incorporate stress scenarios, minimize potential losses, utilize mathematical techniques, and manage housing investment risk effectively. This study provides valuable insights for large investors seeking to construct housing portfolios prioritizing downside protection and minimizing losses in extreme housing market conditions. Utilizing numerical illustrations, it provides insights into portfolio construction, demonstrating the effectiveness of adjusting portfolios to mitigate downside risks during housing market crises. The results highlight dynamic portfolio management’s significance in safeguarding wealth when housing prices undergo significant fluctuations.
Subjects: 
Housing markets
Large investors
Optimal portfolios
Stress scenarios
Worst-Case portfolio optimization
Minimum constant portfolio
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Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version
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