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The Bickel–Rosenblatt test for continuous time stochastic volatility models

Author

Listed:
  • Liang-Ching Lin

    ()

  • Sangyeol Lee

    ()

  • Meihui Guo

    ()

Abstract

In this paper, we consider the Bickel–Rosenblatt test for continuous time stochastic volatility models. The test is constructed based on discretely observed samples by measuring integrated squared deviations between the nonparametric kernel density estimate from the observations and a parametric fit of the density. It is shown that under the null, the proposed test is asymptotically normal. To evaluate the proposed test, a simulation study is performed for illustration. Copyright Sociedad de Estadística e Investigación Operativa 2014

Suggested Citation

  • Liang-Ching Lin & Sangyeol Lee & Meihui Guo, 2014. "The Bickel–Rosenblatt test for continuous time stochastic volatility models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 195-218, March.
  • Handle: RePEc:spr:testjl:v:23:y:2014:i:1:p:195-218
    DOI: 10.1007/s11749-013-0347-1
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    References listed on IDEAS

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    11. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
    12. Lin, Liang-Ching & Lee, Sangyeol & Guo, Meihui, 2013. "Goodness-of-fit test for stochastic volatility models," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 473-498.
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