Coherent risk measure using feedfoward neural networks
- Authors
- Lee, Hyoseok; Lee, Jaewook; Yoon, Younggui; Kim, Sooyoung
- Issue Date
- May-2005
- Publisher
- SPRINGER-VERLAG BERLIN
- Citation
- ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 2, PROCEEDINGS, v.3497, no.II, pp 904 - 909
- Pages
- 6
- Journal Title
- ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 2, PROCEEDINGS
- Volume
- 3497
- Number
- II
- Start Page
- 904
- End Page
- 909
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/53216
- DOI
- 10.1007/11427445_145
- ISSN
- 0302-9743
1611-3349
- Abstract
- Coherent risk measures have recently emerged as alternative measures that overcome the limitation of Value-at-Risk (VaR). In this paper, we propose a new method to estimate coherent risk measure using feedforward neural networks and an evaluation criterion to assess the accuracy of a model. Empirical results are conducted for KOSPI index daily returns from July 1997 to October 2004 and demonstrate that the proposed method is superior to the other existing methods in forecasting the conditional expectation of losses beyond the VaR.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Natural Sciences > Department of Physics > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/53216)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.