| dc.contributor | 國貿系 | |
| dc.creator (作者) | LeBaron, Blake;Yamamoto, Ryuichi | |
| dc.creator (作者) | 山本竜市 | zh_TW |
| dc.date (日期) | 2008 | |
| dc.date.accessioned | 2-Oct-2015 16:43:42 (UTC+8) | - |
| dc.date.available | 2-Oct-2015 16:43:42 (UTC+8) | - |
| dc.date.issued (上傳時間) | 2-Oct-2015 16:43:42 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/78855 | - |
| dc.description.abstract (摘要) | Recent research has documented that learning and evolution are capable of generating many well-known features in financial times series. We extend the results of LeBaron and Yamamoto (2007) to explore the impact of varying amounts of imitation and agent learning in a simple order-driven market. We show that in our framework, imitation is critical to the generation of long memory persistence in many financial time series. This shows that imitation across trader behavior is probably crucial for understanding the dynamics of prices and trading volume. | |
| dc.format.extent | 154389 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Eastern Economic Journal, 34(4), 504-517 | |
| dc.subject (關鍵詞) | learning;evolution;market microstructure;long memory | |
| dc.title (題名) | Symposium on Agent-Based Computational Economics: The Impact of Imitation on Long Memory in an Order-Driven Market | |
| dc.type (資料類型) | article | en |
