| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w29379 |
| 来源ID | Working Paper 29379 |
| Predicting the Oil Market | |
| Charles W. Calomiris; Nida Çakır Melek; Harry Mamaysky | |
| 发表日期 | 2021-10-18 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | We study the performance of many traditional and novel, text-based variables for in-sample and out-of-sample forecasting of oil spot, futures, and energy company stock returns, and changes in oil volatility, production, and inventories. After controlling for small-sample biases, we find evidence of in-sample predictability. Our text measures, derived using energy news articles, hold their own against traditional variables. While we cannot identify ex-ante rules for selecting successful out-of-sample forecasters, an analysis of all possible two-variable models reveals out-of-sample performance above that expected under random variation. Our findings provide new directions for identifying robust forecasting models for oil markets, and beyond. |
| 主题 | Econometrics ; Estimation Methods ; Financial Economics ; Financial Markets ; Portfolio Selection and Asset Pricing ; Environmental and Resource Economics ; Energy |
| URL | https://www.nber.org/papers/w29379 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/587053 |
| 推荐引用方式 GB/T 7714 | Charles W. Calomiris,Nida Çakır Melek,Harry Mamaysky. Predicting the Oil Market. 2021. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w29379.pdf（1715KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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