The effect of infrequent trading on detecting price jumps

Schulz, Frowin C.; Mosler, Karl

doi:10.1007/s10182-010-0137-y

Cited by 3 publications

(7 citation statements)

References 28 publications

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“…See Zhang and Mueller (2005) for further details. 3 The reader is referred to Schulz and Mosler (2010) for further details.…”

Section: Concept Of Realized Variancementioning

confidence: 99%

“…The high-frequency dataset we are working with is the same as employed by Schulz and Mosler (2010), covering the period from May 1 st 2002 to June 30 th 2008. It consists of initially season forward contracts and later on quarter forward contracts, differing in length of the delivery period.…”

Section: Datamentioning

confidence: 99%

“…9 Generally, we simulate one setting without jumps, one with small and rare jumps (σ jmp = 0.0134), and another with large and rare jumps (σ jmp = 0.1). Moreover, we compute 5 (15) minute interval returns and process the series with the zero-return algorithm of Schulz and Mosler (2010) in order to proceed with the analysis of how robust SIVQE works in case of flat prices and no trading. The sampling frequency of 5 minutes is chosen as this is a very common sampling length in many empirical studies.…”

Section: Simulation Studymentioning

confidence: 99%

“…Important for the computation of the return series: if at least one grid is without an assigned price, the corresponding interval return is set to zero. According to Schulz and Mosler (2010), we set the sampling interval length to 15 minutes, producing 30 interval returns per day. For 15 minute sampling intervals, the time series consist of 48% zero-returns, whereof 13% (35%) are due to flat prices (no trading).…”

Section: Datamentioning

confidence: 99%

“…Schulz and Mosler (2010) illustratively show that the existence of even a small percentage amount of zero-returns greatly distort the statistical conclusions by employing the method of Barndorff-Nielsen and Shephard (2004aShephard ( , 2006a. Schulz and Mosler (2010) introduce a first ad-hoc approach to reduce the distortion. Alternatively, a price data manipulation method is employed by Barndorff-Nielsen and Shephard (2004b) in advance.…”

Section: Introductionmentioning

confidence: 99%

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Robust estimation of integrated variance and quarticity under flat price and no trading bias

Schulz¹

2011

JEM

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract This paper investigates a selection of methods disentangling contributions from price jumps to realized variance. Flat prices (consecutively sampled prices in calendar time with the same value) and no trading (no price observation at sampling points), both frequently occurring stylized facts in financial high-frequency datasets, can cause a considerable bias in each considered method. Hence, we propose an approach to robustify those methods so that they can provide undistorted statistical results based on intraday intervals not influenced by flat prices and no trading. The new approach is tested in realistic Monte Carlo experiments and shows to be extraordinary robust against varying levels of flat price and no trading bias. Additionally, we examine the new approach empirically with a dataset of electricity forward contracts traded on the Nord Pool Energy Exchange. We obtain coherent conclusions with respect to predefined qualitative indicators. Terms of use: Documents in

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“…See Zhang and Mueller (2005) for further details. 3 The reader is referred to Schulz and Mosler (2010) for further details.…”

Section: Concept Of Realized Variancementioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust estimation of integrated variance and quarticity under flat price and no trading bias

Schulz¹

2011

JEM

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Jump volatility spillover network based measurement of systemic importance of Chinese financial institutions

et al. 2021

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The identification of systemically important financial institutions (SIFIs) is an important measure to deal with systemic risks. To achieve this goal, we first use generalized variance decomposition method and granger causality test to construct jump volatility spillover networks of Chinese financial institutions based on the 5-min high-frequency data. Then, out-strength and in-strength are adopted to analyze the SIFI. Finally, we use panel data regression model to investigate the determinant of the SIFIs. The empirical results show that: (a) The network density reaches a peak when the financial system under pressure during the China's stock market disaster of 2015. (b) Large banks and insurances usually display systemic importance, while some small financial institutions are also SIFIs due to their high value of out-strength and in-strength. (c) There are obvious differences in the factors that affect the out-strength and in-strength based on panel data regression model, but turnover rate, jump volatility, firm size and growth rate of total assets are the common driving factors.

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Detecting jumps amidst prevalent zero returns: Evidence from the U.S. Treasury securities

Han

Huh

Park

2023

Journal of Empirical Finance

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The effect of infrequent trading on detecting price jumps

Abstract: Realized variance, Bipower variation, Zero-returns, Infrequent trading, High frequency data, Electricity forward contract,

Cited by 3 publications

References 28 publications

Robust estimation of integrated variance and quarticity under flat price and no trading bias

Robust estimation of integrated variance and quarticity under flat price and no trading bias

Jump volatility spillover network based measurement of systemic importance of Chinese financial institutions

Detecting jumps amidst prevalent zero returns: Evidence from the U.S. Treasury securities

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