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Nonparametric Estimate for Conditional Quantiles of Time Series: An application for VaR
Company: Center for Applied Statistics and Economics, Humboldt Universität zu Berlin
Month Of Publication: January
Pages: 37
Download Count: 1
View Count: 1386
Comment Num: 0
Language: English
Source: thesis
Who Can Read: Free
Date: 12-27-2012
Publisher: Administrator
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.
Balcau, Ioana Sign in to follow this author
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