Shortcomings of a parametric VaR approach and nonparametric improvements based on a non-stationary return series model
AbstractA non-stationary regression model for financial returns is examined theoretically in this paper. Volatility dynamics are modelled both exogenously and deterministic, captured by a nonparametric curve estimation on equidistant centered returns. We prove consistency and asymptotic normality of a symmetric variance estimator and of a one-sided variance estimator analytically, and derive remarks on the bandwidth decision. Further attention is paid to asymmetry and heavy tails of the return distribution, implemented by an asymmetric version of the Pearson type VII distribution for random innovations. By providing a method of moments for its parameter estimation and a connection to the Student-t distribution we offer the framework for a factor-based VaR approach. The approximation quality of the non-stationary model is supported by simulation studies. --
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Bibliographic InfoPaper provided by Technische Universität Braunschweig, Institute of Finance in its series Working Papers with number IF32V2.
Date of creation: 2009
Date of revision:
heteroscedastic asset returns; non-stationarity; nonparametric regression; volatility; innovation modelling; asymmetric heavy-tails; distributional forecast; Value at Risk (VaR);
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
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