Challenging traditional risk models by a non-stationary approach with nonparametric heteroscedasticity
AbstractIn 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. --
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Bibliographic InfoPaper provided by Technische Universität Braunschweig, Institute of Finance in its series Working Papers with number IF41V1.
Date of creation: 2012
Date of revision:
heteroscedastic asset returns; non-stationarity; nonparametric regression; volatility; innovation modelling; forecasting; Value at Risk (VaR); ARCH-models;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
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