Neyman smooth goodness-of-fit tests for the marginal distribution of dependent data
2011 | journal article; research paper. A publication with affiliation to the University of Göttingen.
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- Authors
- Munk, Axel ; Stockis, Jean-Pierre; Valeinis, Janis; Giese, Goetz
- Abstract
- We establish a data-driven version of Neyman's smooth goodness-of-fit test for the marginal distribution of observations generated by an alpha-mixing discrete time stochastic process (X-t)(t is an element of Z). This is a simple extension of the test for independent data introduced by Ledwina (J Am Stat Assoc 89:1000-1005, 1994). Our method only requires additional estimation of the cumulative autocovariance. Consistency of the test will be shown at essentially any alternative. A brief simulation study shows that the test performs reasonable especially for the case of positive dependence. Finally, we illustrate our approach by analyzing the validity of a forecasting method ("historical simulation") for the implied volatilities of traded options.
- Issue Date
- 2011
- Journal
- Annals of the Institute of Statistical Mathematics
- Organization
- Fakultät für Mathematik und Informatik
- ISSN
- 0020-3157
- Language
- English