| dc.contributor | 財管系 | |
| dc.creator (作者) | 盧敬植 | |
| dc.creator (作者) | Lu, Ching-Chih;Chollete, Lorán;Hughen, Keener;Peng, Weijia | |
| dc.date (日期) | 2024-06 | |
| dc.date.accessioned | 30-Apr-2025 15:03:12 (UTC+8) | - |
| dc.date.available | 30-Apr-2025 15:03:12 (UTC+8) | - |
| dc.date.issued (上傳時間) | 30-Apr-2025 15:03:12 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/156769 | - |
| dc.description.abstract (摘要) | Environmentally responsible (‘green’) firms have important asset pricing implications. Whilst green firms’ performance has been formally studied in terms of returns and pricing (Bolton and Kacperczyk, 2022; Pástor et al., 2022), far less is known about their volatility. We analyze the volatility of green and brown firms from 2012 to 2021, through the multiple lens of GARCH, machine learning, and historical volatility. The unconditional volatilities of brown and green firms are similar. The forecasting of volatility, however, differs sharply between green and brown firms: it is much harder to forecast green firms’ volatility. | |
| dc.format.extent | 105 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Finance Research Letters, Vol.64, 105372 | |
| dc.subject (關鍵詞) | Green firms; Machine learning; Forecasting; Volatility | |
| dc.title (題名) | Assessing the volatility of green firms | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1016/j.frl.2024.105372 | |
| dc.doi.uri (DOI) | https://doi.org/10.1016/j.frl.2024.105372 | |
