Nonparametric Estimate for Conditional Quantiles of Time Series

This paper investigates a nonparametric approach for estimating conditional quantiles of time series for dependent data. The considered estimate is obtained by inverting a kernel estimate of the conditional distribution function. We implement the technique on four simulated samples with light and heavy-tailed distributions and on real financial data, by calculating VaR using the nonparametric procedure. The good performance of the estimator is illustrated with backtesting.

Grantor dc:publisher

Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät

Year dc:date.issued

2012

Author dc:creator

• Balcau, Ioana

• Value at Risk
• Nonparametric
• Backtesting
• Conditional Quantiles
• Kernel Estimation

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

eng

DOI

https://doi.org/10.18452/14167

OAI record oai:identifier

https://edoc.hu-berlin.de/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Aedoc.hu-berlin.de%3A18452%2F14819