Abstract
Various parametric models have been designed to analyze volatility in time series of financial market data. For maximum likelihood estimation these parametric methods require the assumption of a known conditional distribution. In this paper we examine the conditional distribution of daily DAX returns with the help of nonparametric methods. We use kernel estimators for conditional quantiles resulting from a kernel estimation of conditional distributions.
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This work was financially supported by the Deutsche Forschungsgemeinschaft
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Abberger, K. Quantile smoothing in financial time series. Statistical Papers 38, 125–148 (1997). https://doi.org/10.1007/BF02925220
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DOI: https://doi.org/10.1007/BF02925220