| dc.contributor | 統計系 | - |
| dc.creator (作者) | 鄭宗記 | - |
| dc.creator (作者) | Cheng, Tsung-Chi | - |
| dc.creator (作者) | Lai, Hung-Neng | - |
| dc.date (日期) | 2020-09 | - |
| dc.date.accessioned | 30-五月-2022 16:04:23 (UTC+8) | - |
| dc.date.available | 30-五月-2022 16:04:23 (UTC+8) | - |
| dc.date.issued (上傳時間) | 30-五月-2022 16:04:23 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/140183 | - |
| dc.description.abstract (摘要) | Two advances have been made in the estimation of probability of informed trading (PIN) models. First, an initial-value-setting scheme has been proposed, that sets up a grid for initial values of mixture probabilities and uses the probabilities to divide the sample so as to derive the initial values of Poisson parameters. Second, the mixture bivariate normal distribution can help approximate the compound Poisson distribution in estimating PIN models. This study implements two approaches to simulated and real data for the PIN and Adjusted PIN models and compares their performance with the literature. The new initial-value-setting scheme performs better than those of Yan and Zhang [An improved estimation method and empirical properties of the probability of informed trading. J. Banking Finance, 2012, 36(2), 454–467] and Ersan and Alıcı [An unbiased computation methodology for estimating the probability of informed trading (PIN). J. Int. Financ. Markets, Inst. Money, 2016, 43, 74–94], and using the normal distribution outperforms the Poisson distribution under certain variance specifications. | - |
| dc.format.extent | 109 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Quantitative Finance, 21(5), 771-796 | - |
| dc.subject (關鍵詞) | Probability of informed trading; Maximum likelihood method; Mixture distribution; Market microstructure | - |
| dc.title (題名) | Improvements in estimating the probability of informed trading models | - |
| dc.type (資料類型) | article | - |
| dc.identifier.doi (DOI) | 10.1080/14697688.2020.1800805 | - |
| dc.doi.uri (DOI) | https://doi.org/10.1080/14697688.2020.1800805 | - |
