Quyen Nguyen

Quyen Nguyen

University of Otago

Publications

Nguyen, Q., Diaz-Rainey, I., & Kuruppuarachchi, D. (2023). In search of climate distress risk. International Review of Financial Analysis, 85, 102444.

Using the Merton distance to default model we investigate whether a firm’s climate risk affects its default (distress) risk. S&P 500 non-financial firms during 2010–2019 are analysed and we employ both corporate carbon footprints and climate risk disclosures in annual filings to measure climate risk. Our results show that climate risk has a negative impact on firms’ distance to default. This impact is limited to the disclosure of transition risk in annual filings. In contrast, disclosures of physical or non-specific risk do not affect firm-level default risk, while the impact of corporate carbon footprints is inconsistent but insignificant in most models. We also find that the negative effect of climate transition risk on firms’ distance to default is stronger for firms headquartered in states with carbon pricing (California and states covered by the Regional Greenhouse Gas Initiative) and temporarily strengthens because of the Paris Agreement in 2015. However, this ‘Paris’ effect is short-lived and fades away in subsequent years.

Presentations [* co-author]:

Nguyen, Q., Diaz-Rainey, I., Kuruppuarachchi, D., McCarten, M., & Tan, E. K. (2023). Climate transition risk in US loan portfolios: Are all banks the same? International Review of Financial Analysis, 85, 102401

We take advantage of a combination of a severe weather event from 3 to 4 June 2015 and a local policy, to investigate the housing market response to climate change-related flooding hazard. The study focuses on a residential area in a low-lying coastal suburb of Dunedin, New Zealand, where the groundwater level is shallow and close to sea level. An unusually heavy rain event in June 2015 resulted in flooding of a significant portion of land in especially low-lying areas. The city council responded by reviewing processes for storm-water management and by imposing minimum-floor-level [MFL] requirements on new construction in the low-lying areas previously identified as at risk of flooding. Applying a ‘diff-in-diff-in-diff’ strategy in hedonic regression analyses, we find that houses in the MFL zone sell for a discount of about 5 per cent prior to the flood. This discount briefly tripled in the area that flooded, but disappeared within 15 months, indicating either very short memory among homebuyers or no long-run change in perception of hazard.

Presentations [* co-author]:

Nguyen, Q., Thorsnes, P., Diaz‐Rainey, I., Moore, A., Cox, S., & Stirk‐Wang, L. (2022). Price recovery after the flood: risk to residential property values from climate change‐related flooding. Australian Journal of Agricultural and Resource Economics, 66(3), 532-560.

We take advantage of a combination of a severe weather event from 3 to 4 June 2015 and a local policy, to investigate the housing market response to climate change-related flooding hazard. The study focuses on a residential area in a low-lying coastal suburb of Dunedin, New Zealand, where the groundwater level is shallow and close to sea level. An unusually heavy rain event in June 2015 resulted in flooding of a significant portion of land in especially low-lying areas. The city council responded by reviewing processes for storm-water management and by imposing minimum-floor-level [MFL] requirements on new construction in the low-lying areas previously identified as at risk of flooding. Applying a ‘diff-in-diff-in-diff’ strategy in hedonic regression analyses, we find that houses in the MFL zone sell for a discount of about 5 per cent prior to the flood. This discount briefly tripled in the area that flooded, but disappeared within 15 months, indicating either very short memory among homebuyers or no long-run change in perception of hazard.

Presentation:

Nguyen, Q., Diaz-Rainey, I., & Kuruppuarachchi, D. (2021). Predicting corporate carbon footprints for climate finance risk analyses: a machine learning approach. Energy Economics, 95, 105129.

Corporations have come under pressure from investors and other stakeholders to disclose and reduce their greenhouse gas emissions (GHG). Corporate GHG footprints, proxying for transition risk, are dominated by carbon emissions from energy use. Thus the growing attention on the carbon emissions of corporations from, principally, their energy use, motivates firms to invest in energy efficiency and renewable energy. However, only a subset of corporations disclose their GHG/carbon footprints. This paper uses machine learning to improve the prediction of corporate carbon emissions for risk analyses by investors. We introduce a two-step framework that applies a Meta-Elastic Net learner to combine predictions from multiple base-learners as the best emission prediction approach. It results in an accuracy gain based on mean absolute error of up to 30% as compared with the existing models. We also find that prediction accuracy can be further improved by incorporating additional predictors (energy production/consumption data) and additional firm disclosures in particular sectors and regions. This provides an indication of where policymakers should concentrate their efforts for greater level of disclosure.

Presentations [* co-author]: