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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | P. Tibprasorn | en_US |
| dc.contributor.author | K. Autchariyapanitkul | en_US |
| dc.contributor.author | S. Sriboonchitta | en_US |
| dc.date.accessioned | 2018-09-05T03:35:16Z | - |
| dc.date.available | 2018-09-05T03:35:16Z | - |
| dc.date.issued | 2017-02-01 | en_US |
| dc.identifier.issn | 1860949X | en_US |
| dc.identifier.other | 2-s2.0-85012887682 | en_US |
| dc.identifier.other | 10.1007/978-3-319-50742-2_35 | en_US |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012887682&origin=inward | en_US |
| dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/57124 | - |
| dc.description.abstract | © Springer International Publishing AG 2017. This study applies the principle of stochastic frontier model (SFM) to calculate the optimal frontier of the stock prices in a stock market. We use copula to measure dependence between the error terms in SFM by examining several stocks in Down Jones industrial. The results show that our modified stochastic frontier model is more applicable for financial econometrics. Finally, we use AIC for model selection. | en_US |
| dc.subject | Computer Science | en_US |
| dc.title | Stochastic frontier model in financial econometrics: A copula-based approach | en_US |
| dc.type | Book Series | en_US |
| article.title.sourcetitle | Studies in Computational Intelligence | en_US |
| article.volume | 692 | en_US |
| article.stream.affiliations | Chiang Mai University | en_US |
| article.stream.affiliations | Maejo University | en_US |
| Appears in Collections: | CMUL: Journal Articles | |
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