Nonparametric Statistical Inference of Value At Risk For Financial Time Series
Song Chen and
Cheng Yong Tang
No 88, Research Paper Series from Quantitative Finance Research Centre, University of Technology, Sydney
The paper considers nonparametric estimation of Value at Risk (VaR) and associated standard error estimation for dependent financial return series. The presence of dependence affects the variance of the VaR estimates and has to be taken into consideration in order to obtain adequate assessment on their variation. As estimation procedure of the standard errors is proposed based on a assessment on their variation. As estimation procedure of the standard errors is proposed based on a kernel estimation of the spectral density of a derived series. The performance of the VaR estimators and the proposed standard error estimation procedure are evaluated by theoretical investigation, simulation of commonly used models for financial returns and empirical studies on real financial return series.
Keywords: alphpa-mixing; kernal estimation; sample quantile; spectral density estimation; standard error estimation (search for similar items in EconPapers)
References: Add references at CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:uts:rpaper:88
Access Statistics for this paper
More papers in Research Paper Series from Quantitative Finance Research Centre, University of Technology, Sydney PO Box 123, Broadway, NSW 2007, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Duncan Ford ().