Challenging traditional risk models by a non-stationary approach with nonparametric heteroscedasticity

Arbeitspapier

In this paper we analyze an econometric model for non-stationary asset returns. Volatility dynamics are modelled by nonparametric regression; consistency and asymptotic normality of a symmetric and of a one-sided kernel estimator are outlined with remarks on the bandwidth decision. Further attention is paid to asymmetry and heavy tails of the return distribution, involved by the framework for innovations. We survey the practicability and automatization of the implementation. For simulated price processes and a multitude of financial time series we observe a satisfying model approximation and good short-term forecasting abilities of the univariate approach. The non-stationary regression model outperforms parametric risk models and famous ARCH-type implementations.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper Series ; No. IF41V1

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General
• Subject: heteroscedastic asset returns ; non-stationarity ; nonparametric regression ; volatility ; innovation modelling ; forecasting ; Value at Risk (VaR) ; ARCH-models

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Gürtler, Marc ; Rauh, Ronald
• Event: Veröffentlichung
• (who): Technische Universität Braunschweig, Institut für Finanzwirtschaft
• (where): Braunschweig
• (when): 2012

Further information

• Handle: http://hdl.handle.net/10419/67963
• Last update: 10.03.2025, 11:44 AM CET