The Bickel-Rosenblatt test for continuous time stochastic volatility models

Liang-Ching Lin, Sangyeol Lee, Meihui Guo

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)195-218
Number of pages24
JournalTest
Volume23
Issue number1
DOIs
Publication statusPublished - 2014 Jan 1

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Stochastic Volatility Model
Continuous Time
Kernel Density Estimate
Null
Deviation
Simulation Study
Continuous time
Stochastic volatility model
Evaluate

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Lin, Liang-Ching ; Lee, Sangyeol ; Guo, Meihui. / The Bickel-Rosenblatt test for continuous time stochastic volatility models. In: Test. 2014 ; Vol. 23, No. 1. pp. 195-218.
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The Bickel-Rosenblatt test for continuous time stochastic volatility models. / Lin, Liang-Ching; Lee, Sangyeol; Guo, Meihui.

In: Test, Vol. 23, No. 1, 01.01.2014, p. 195-218.

Research output: Contribution to journalArticle

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