- Paper_Handbook_PCTsai_Jan2015
Submitted manuscript, 1.65 MB, PDF document

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed)

Published

**Detecting jumps in high-frequency prices under stochastic volatility : a data-driven approach.** / Tsai, Ping-Chen; Shackleton, Mark.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed)

Tsai, P-C & Shackleton, M 2016, Detecting jumps in high-frequency prices under stochastic volatility: a data-driven approach. in I Florescu, MC Mariani, HE Stanley & FG Viens (eds), *Handbook of high-frequency trading and modeling in finance.* John Wiley, Chichester, pp. 137-165.

Tsai, P-C., & Shackleton, M. (2016). Detecting jumps in high-frequency prices under stochastic volatility: a data-driven approach. In I. Florescu, M. C. Mariani, H. E. Stanley, & F. G. Viens (Eds.), *Handbook of high-frequency trading and modeling in finance *(pp. 137-165). John Wiley.

Tsai P-C, Shackleton M. Detecting jumps in high-frequency prices under stochastic volatility: a data-driven approach. In Florescu I, Mariani MC, Stanley HE, Viens FG, editors, Handbook of high-frequency trading and modeling in finance. Chichester: John Wiley. 2016. p. 137-165

@inbook{5247b89e659e49c68c47b484637a51fc,

title = "Detecting jumps in high-frequency prices under stochastic volatility: a data-driven approach",

abstract = "Detecting jumps in asset prices over a daily interval consists of testing for the significance of the difference between quadratic variation and integrated variance. Detecting jumps in high-frequency prices requires the additional tasks of estimating spot volatility and controlling for over-rejection due to multiple comparisons. We generalize two intraday tests commonly used in the literature and identify the test statistic that has the highest power at a given test level. The daily maximums of such test statistics admit an asymptotic generalized extreme value (GEV) distribution with a strictly positive shape parameter, as opposed to the limiting Gumbel distribution with a shape parameter zero for i.i.d. Gaussian maximums. The shape parameter of GEV distribution can thus be seen as a measure of bias correction for the test under stochastic volatility. We calibrate the shape parameter with a credible volatility model estimated from our data, which are Spyder (SPY) returns during January, 2002 and April, 2010. Empirical results are broadly consistent with those from simulation.",

keywords = "Jumps, High-frequency Data, Bi-power Variation, Realized Variance, Stochastic Volatility, Multiple Tests Correction, Extreme Value Theorem, HAR Regression ",

author = "Ping-Chen Tsai and Mark Shackleton",

year = "2016",

month = may

language = "English",

isbn = "9781118443989",

pages = "137--165",

editor = "Ionut Florescu and Mariani, {Maria C.} and Stanley, {H. Eugene} and Viens, {Frederi G.}",

booktitle = "Handbook of high-frequency trading and modeling in finance",

publisher = "John Wiley",

}

TY - CHAP

T1 - Detecting jumps in high-frequency prices under stochastic volatility

T2 - a data-driven approach

AU - Tsai, Ping-Chen

AU - Shackleton, Mark

PY - 2016/5

Y1 - 2016/5

N2 - Detecting jumps in asset prices over a daily interval consists of testing for the significance of the difference between quadratic variation and integrated variance. Detecting jumps in high-frequency prices requires the additional tasks of estimating spot volatility and controlling for over-rejection due to multiple comparisons. We generalize two intraday tests commonly used in the literature and identify the test statistic that has the highest power at a given test level. The daily maximums of such test statistics admit an asymptotic generalized extreme value (GEV) distribution with a strictly positive shape parameter, as opposed to the limiting Gumbel distribution with a shape parameter zero for i.i.d. Gaussian maximums. The shape parameter of GEV distribution can thus be seen as a measure of bias correction for the test under stochastic volatility. We calibrate the shape parameter with a credible volatility model estimated from our data, which are Spyder (SPY) returns during January, 2002 and April, 2010. Empirical results are broadly consistent with those from simulation.

AB - Detecting jumps in asset prices over a daily interval consists of testing for the significance of the difference between quadratic variation and integrated variance. Detecting jumps in high-frequency prices requires the additional tasks of estimating spot volatility and controlling for over-rejection due to multiple comparisons. We generalize two intraday tests commonly used in the literature and identify the test statistic that has the highest power at a given test level. The daily maximums of such test statistics admit an asymptotic generalized extreme value (GEV) distribution with a strictly positive shape parameter, as opposed to the limiting Gumbel distribution with a shape parameter zero for i.i.d. Gaussian maximums. The shape parameter of GEV distribution can thus be seen as a measure of bias correction for the test under stochastic volatility. We calibrate the shape parameter with a credible volatility model estimated from our data, which are Spyder (SPY) returns during January, 2002 and April, 2010. Empirical results are broadly consistent with those from simulation.

KW - Jumps

KW - High-frequency Data

KW - Bi-power Variation

KW - Realized Variance

KW - Stochastic Volatility

KW - Multiple Tests Correction

KW - Extreme Value Theorem

KW - HAR Regression

UR - http://www.wiley-vch.de/publish/dt/books/forthcomingTitles/EC00/1-118-44398-5/?sID=4deb2lnm48aq6qvcsbuv34epp1

M3 - Chapter (peer-reviewed)

SN - 9781118443989

SP - 137

EP - 165

BT - Handbook of high-frequency trading and modeling in finance

A2 - Florescu, Ionut

A2 - Mariani, Maria C.

A2 - Stanley, H. Eugene

A2 - Viens, Frederi G.

PB - John Wiley

CY - Chichester

ER -