Nonparametric estimates for conditional quantiles of time series

Arbeitspapier

We consider the problem of estimating the conditional quantile of a time series fYtg at time t given covariates Xt, where Xt can ei- ther exogenous variables or lagged variables of Yt . The conditional quantile is estimated by inverting a kernel estimate of the conditional distribution function, and we prove its asymptotic normality and uni- form strong consistency. The performance of the estimate for light and heavy-tailed distributions of the innovations are evaluated by a simulation study. Finally, the technique is applied to estimate VaR of stocks in DAX, and its performance is compared with the existing standard methods using backtesting.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SFB 649 Discussion Paper ; No. 2014-012

Classification and Topics

• Classification: Wirtschaft ; Mathematical and Quantitative Methods: General ; Semiparametric and Nonparametric Methods: General ; Econometric Modeling: General ; Financial Econometrics
• Subject: Conditional quantile ; kernel estimate ; quantile autoregression ; time series ; uniform consistency ; value-at-risk

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Franke, Jürgen ; Mwita, Peter ; Wang, Weining
• Event: Veröffentlichung
• (who): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
• (where): Berlin
• (when): 2014

Further information

• Handle: http://hdl.handle.net/10419/93220
• Last update: 10.03.2025, 11:41 AM CET