A numerical study of adjusted parameter estimation in normal inverse Gaussian distribution
- Authors
- Yoon, Jeongyoen; Song, Seongjoo
- Issue Date
- 6월-2016
- Publisher
- KOREAN STATISTICAL SOC
- Keywords
- normal inverse Gaussian distribution; feasible domain; parameter estimation; Value at Risk
- Citation
- KOREAN JOURNAL OF APPLIED STATISTICS, v.29, no.4, pp.741 - 752
- Indexed
- KCI
- Journal Title
- KOREAN JOURNAL OF APPLIED STATISTICS
- Volume
- 29
- Number
- 4
- Start Page
- 741
- End Page
- 752
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/88513
- DOI
- 10.5351/KJAS.2016.29.4.741
- ISSN
- 1225-066X
- Abstract
- Numerous studies have shown that normal inverse Gaussian (NIG) distribution adequately fits the empirical return distribution of financial securities. The estimation of parameters can also be done relatively easily, which makes the NIG distribution more useful in financial markets. The maximum likelihood estimation and the method of moments estimation are easy to implement; however, we may encounter a problem in practice when a relationship among the moments is violated. In this paper, we investigate this problem in the parameter estimation and try to find a simple solution through simulations. We examine the effect of our adjusted estimation method with real data: daily log returns of KOSPI, S&P500, FTSE and HANG SENG. We also checked the performance of our method by computing the value at risk of daily log return data. The results show that our method improves the stability of parameter estimation, while it retains a comparable performance in goodness-of-fit.
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Collections - College of Political Science & Economics > Department of Statistics > 1. Journal Articles
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