Shortcomings of a parametric VaR approach and nonparametric improvements based on a non-stationary return series model

Marc Gürtler and Ronald Rauh

No IF32V2, Working Papers from Technische Universität Braunschweig, Institute of Finance

Abstract: A 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.

Keywords: heteroscedastic asset returns; non-stationarity; nonparametric regression; volatility; innovation modelling; asymmetric heavy-tails; distributional forecast; Value at Risk (VaR) (search for similar items in EconPapers)
JEL-codes: C12 C14 C15 C5 (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/55232/1/684931540.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:tbsifw:if32v2

Access Statistics for this paper

More papers in Working Papers from Technische Universität Braunschweig, Institute of Finance Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().