Variation-based tests for volatility misspecification

Papanicolaou, Alex; Giesecke, Kay

doi:10.1016/j.jeconom.2015.10.008

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…These articles study the data without the microstructure noise. Vetter and Dette (2012), Papanicolaou and Giesecke (2016) improved the test under the microstructure noise setting.…”

Section: Introductionmentioning

confidence: 99%

Testing the volatility jumps based on the high frequency data

Liu

Lin

2021

Journal Time Series Analysis

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This article tests volatility jumps based on the high frequency data. Under the null hypothesis that the volatility process is a continuous semimartingale, our test statistic converges to a normal distribution, and under the alternative hypothesis where the volatility has jumps, the statistic diverges to infinity. Compared to the test statistic of Bibinger et al. (Bibinger et al. (2017). Annals of Statistics 45, 1542–1578), our proposed statistic diverges to infinity at a faster rate, and has a better power. Simulation studies confirm the theoretical results, and an empirical analysis shows that some real financial data possess volatility jumps.

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“…These articles study the data without the microstructure noise. Vetter and Dette (2012), Papanicolaou and Giesecke (2016) improved the test under the microstructure noise setting.…”

Section: Introductionmentioning

confidence: 99%

Testing the volatility jumps based on the high frequency data

Liu

Lin

2021

Journal Time Series Analysis

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“…) In the simplest case, where our observations are known to come from a stationary or ergodic diffusion process, a great many authors have described goodness-of-fit tests. We briefly mention some initial work (Aït-Sahalia, 1996;Corradi and White, 1999;Kleinow, 2002) as well as more recent discussion (González-Manteiga and Crujeiras, 2013;Papanicolaou and Giesecke, 2014;Chen et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…In the following, we will be interested in goodness-of-fit tests which not only detect non-stationary alternatives, but also achieve good detection rates. In this setting, Dette and von Lieres und Wilkau (2003) propose a test which can detect misspecification of the volatility at a rate n −1/4 in L 2 norm (see also Dette et al, 2006;Podolskij and Ziggel, 2008;Papanicolaou and Giesecke, 2014).…”

Section: Introductionmentioning

confidence: 99%

Semimartingale detection and goodness-of-fit tests

Bull¹

2017

Ann. Statist.

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In quantitative finance, we often fit a parametric semimartingale model to asset prices. To ensure our model is correct, we must then perform goodness-of-fit tests. In this paper, we give a new goodness-offit test for volatility-like processes, which is easily applied to a variety of semimartingale models. In each case, we reduce the problem to the detection of a semimartingale observed under noise. In this setting, we then describe a wavelet-thresholding test, which obtains adaptive and near-optimal detection rates.

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Consistent estimation of drift parameter in diffusion model with misspecified volatility function

Jeong

2022

Economics Letters

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Variation-based tests for volatility misspecification

Cited by 5 publications

References 34 publications

Testing the volatility jumps based on the high frequency data

Testing the volatility jumps based on the high frequency data

Semimartingale detection and goodness-of-fit tests

Consistent estimation of drift parameter in diffusion model with misspecified volatility function

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