This dissertation aims to study the impact of the COVID-19 crisis on the credit risk of the accommodation services sector in Portugal using a structural model based on the one developed by Eisdorfer, Goyal, & Zhdanov (2019). Three major changes were implemented in the model: i) it was adapted to assess the impact of the pandemic crisis, ii) it was considered that firms have a cash account, and iii) a different decomposition of operating costs was made. Using the Central Balance Sheet - Harmonized Panel (CBHP) and the Fast and Exceptional Enterprise Survey (COVID-IREE) databases it was possible to implement the model to private Portuguese firms. The study is performed on four representative firms, that were constructed using a hybrid machine learning approach. The clustering variables - Adjusted Return on Assets, Fixed-Asset Turnover, Current Ratio and Long-Term Debt - were selected aiming to contemplate a different aspect regarding the firms’ pre-COVID-19 financial situation. While the results obtained indicate that all of the representative firms would see a decrease in their distance to default due to the pandemic shock, the dimension of the effect is heterogeneous on the four studied firms. The results also seem to confirm the intuition that firms with a better cash position before the shock suffer a less negative impact on their levels of credit risk. Additionally, less fixed obligations seem to influence firm’s chances of survival.
Date of Award | 28 Jun 2021 |
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Original language | English |
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Awarding Institution | - Universidade Católica Portuguesa
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Supervisor | Diana Bonfim (Supervisor) & Nuno Silva (Co-Supervisor) |
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- COVID-19
- Structural models
- Machine learning
- Credit risk
- Accommodation sector
- Private firms
- Portugal
The impact of the COVID-19 crisis on the credit risk of the accommodation services sector in Portugal: a contingent claims approach
Rosero, C. S. (Student). 28 Jun 2021
Student thesis: Master's Thesis