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Volatility model selection for extremes of financial time series

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Volatility model selection for extremes of financial time series. / Liu, Ye; Tawn, Jonathan Angus.

In: Journal of Statistical Planning and Inference, Vol. 143, No. 3, 03.2013, p. 520-530.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Liu, Y & Tawn, JA 2013, 'Volatility model selection for extremes of financial time series', Journal of Statistical Planning and Inference, vol. 143, no. 3, pp. 520-530. https://doi.org/10.1016/j.jspi.2012.08.009

APA

Liu, Y., & Tawn, J. A. (2013). Volatility model selection for extremes of financial time series. Journal of Statistical Planning and Inference, 143(3), 520-530. https://doi.org/10.1016/j.jspi.2012.08.009

Vancouver

Liu Y, Tawn JA. Volatility model selection for extremes of financial time series. Journal of Statistical Planning and Inference. 2013 Mar;143(3):520-530. Epub 2012 Sep 3. doi: 10.1016/j.jspi.2012.08.009

Author

Liu, Ye ; Tawn, Jonathan Angus. / Volatility model selection for extremes of financial time series. In: Journal of Statistical Planning and Inference. 2013 ; Vol. 143, No. 3. pp. 520-530.

Bibtex

@article{7bf3f45bccc8412980b4f3bee62bb224,
title = "Volatility model selection for extremes of financial time series",
abstract = "Although both widely used in the financial industry, there is quite often very little justification why GARCH or stochastic volatility is preferred over the other in practice. Most of the relevant literature focuses on the comparison of the fit of various volatility models to a particular data set, which sometimes may be inconclusive due to the statistical similarities of both processes. With an ever growing interest among the financial industry in the risk of extreme price movements, it is natural to consider the selection between both models from an extreme value perspective. By studying the dependence structure of the extreme values of a given series, we are able to clearly distinguish GARCH and stochastic volatility models and to test statistically which one better captures the observed tail behaviour. We illustrate the performance of the method using some stock market returns and find that different volatility models may give a better fit to the upper or lower tails.",
keywords = "Coefficient of tail dependence, Conditional tail probability, GARCH, Stochastic volatility, Extremal dependence",
author = "Ye Liu and Tawn, {Jonathan Angus}",
year = "2013",
month = mar,
doi = "10.1016/j.jspi.2012.08.009",
language = "English",
volume = "143",
pages = "520--530",
journal = "Journal of Statistical Planning and Inference",
issn = "0378-3758",
publisher = "Elsevier",
number = "3",

}

RIS

TY - JOUR

T1 - Volatility model selection for extremes of financial time series

AU - Liu, Ye

AU - Tawn, Jonathan Angus

PY - 2013/3

Y1 - 2013/3

N2 - Although both widely used in the financial industry, there is quite often very little justification why GARCH or stochastic volatility is preferred over the other in practice. Most of the relevant literature focuses on the comparison of the fit of various volatility models to a particular data set, which sometimes may be inconclusive due to the statistical similarities of both processes. With an ever growing interest among the financial industry in the risk of extreme price movements, it is natural to consider the selection between both models from an extreme value perspective. By studying the dependence structure of the extreme values of a given series, we are able to clearly distinguish GARCH and stochastic volatility models and to test statistically which one better captures the observed tail behaviour. We illustrate the performance of the method using some stock market returns and find that different volatility models may give a better fit to the upper or lower tails.

AB - Although both widely used in the financial industry, there is quite often very little justification why GARCH or stochastic volatility is preferred over the other in practice. Most of the relevant literature focuses on the comparison of the fit of various volatility models to a particular data set, which sometimes may be inconclusive due to the statistical similarities of both processes. With an ever growing interest among the financial industry in the risk of extreme price movements, it is natural to consider the selection between both models from an extreme value perspective. By studying the dependence structure of the extreme values of a given series, we are able to clearly distinguish GARCH and stochastic volatility models and to test statistically which one better captures the observed tail behaviour. We illustrate the performance of the method using some stock market returns and find that different volatility models may give a better fit to the upper or lower tails.

KW - Coefficient of tail dependence

KW - Conditional tail probability

KW - GARCH

KW - Stochastic volatility

KW - Extremal dependence

U2 - 10.1016/j.jspi.2012.08.009

DO - 10.1016/j.jspi.2012.08.009

M3 - Journal article

VL - 143

SP - 520

EP - 530

JO - Journal of Statistical Planning and Inference

JF - Journal of Statistical Planning and Inference

SN - 0378-3758

IS - 3

ER -