Stock market microstructure and return volatility

Amihud, Yakov; Mendelson, Haim; Murgia, Maurizio

doi:10.1016/0378-4266(90)90057-9

Cited by 78 publications

(32 citation statements)

References 10 publications

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“…Amihud and Mendelson (1987) attribute the result to differences in trading mechanisms between the opening and closing transactions and Stoll and Whaley (1990) to-open volatility, and conclude that it is caused by the preceding long period of non-trading rather than the trading mechanism. Similarly, Amihud et al (1990) found that on the Milan Stock Exchange, where the market opens with continuous trading and moves to a call market, the call trading actually exhibits lower volatility. It is also consistent with Choe and Shin's (1993) …”

Section: Literature Reviewmentioning

confidence: 99%

Interday and intraday volatility: Additional evidence from the Shanghai Stock Exchange

Tian

Guo

2007

Rev Quant Finan Acc

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After examining both the interday and intraday return volatility of the Shanghai Composite Stock Index, it was found that the open-to-open return variance is consistently greater than the close-to-close variance. Examining the volatility of interday returns and variance ratio tests with five-minute intervals reveals an Lshaped pattern, or more precisely, two L-shaped patterns, starting with a small hump during both the morning and the afternoon sessions, with the morning session having a much higher interday volatility than the afternoon session. This L-shaped interday volatility is supported by the similarly shaped intraday volatility pattern. This result suggests that the high volatility of intraday returns for the market open is not entirely due to the trading mechanisms (call auction in the market opening) but also due to both the accumulated overnight information and the trading halt effect. The five-minute breaks after the auction and blind auction procedures are the two major driving forces which exaggerate the high intraday volatility observed at the market open. KeywordsInterday and intraday volatility, order driven market, Shanghai Stock Exchange Disciplines Business | Social and Behavioral Sciences Publication DetailsThis article was originally published as Tian, G, Guo, M, Interday and intraday volatility: additional evidence from the Shanghai Stock Exchange, Review of Quantitative Finance and Accounting, 28(3), [287][288][289][290][291][292][293][294][295][296][297][298][299][300][301][302][303][304][305][306] 2007 This paper is organized as follows: Section 2 presents the literature review on interday and intraday return volatility; Section 3 discusses the institutional background for the SHSE and the data; Section 4 reports and analyses the

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Section: Literature Reviewmentioning

confidence: 99%

Interday and intraday volatility: Additional evidence from the Shanghai Stock Exchange

Tian

Guo

2007

Rev Quant Finan Acc

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“…For example, StratBox from PuppetMaster Trading stores its data as HDF5 files at the backend since 2008 3 ; and professor Henning Bunzel of Aarhus University mentioned that HDF5 is a part of the set of software packages available at his organization 4 . However, the only peer-reviewed documentation of the effectiveness of HDF5 in handling financial data is in a 2012 article by Bethel et al [8].…”

Section: Test Data and Data File Formatsmentioning

confidence: 99%

“…To measure the volatility, we believe any number of well-known measures could in principle be fine [35,4,5,6]. For this work, we opt for an instantaneous volatility measure called the Maximum Intermediate Return or MIR for short.…”

Section: Definitionsmentioning

confidence: 99%

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A Big Data Approach to Analyzing Market Volatility

Bethel

et al. 2013

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Understanding the microstructure of the financial market requires the processing of a vast amount of data related to individual trades, and sometimes even multiple levels of quotes. Analyzing such a large volume of data requires tremendous computing power that is not easily available to financial academics and regulators. Fortunately, public funded High Performance Computing (HPC) power is widely available at the National Laboratories in the US. In this paper we demonstrate that the HPC resource and the techniques for data-intensive sciences can be used to greatly accelerate the computation of an early warning indicator called Volume-synchronized Probability of Informed trading (VPIN). The test data used in this study contains five and a half year's worth of trading data for about 100 most liquid futures contracts, includes about 3 billion trades, and takes 140GB as text files. By using (1) a more efficient file format for storing the trading records, (2) more effective data structures and algorithms, and (3) parallelizing the computations, we are able to explore 16,000 different ways of computing VPIN in less than 20 hours on a 32-core IBM DataPlex machine. Our test demonstrates that a modest computer is sufficient to monitor a vast number of trading activities in real-time -an ability that could be valuable to regulators.Our test results also confirm that VPIN is a strong predictor of liquidity-induced volatility. With appropriate parameter choices, the false positive rates are about 7% averaged over all the futures contracts in the test data set. More specifically, when VPIN values rise above a threshold (CDF > 0.99), the volatility in the subsequent time windows is higher than the average in 93% of the cases.

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“…There have been numerous studies on the effectiveness of microstructure reforms in the more established stock markets, focusing in particular on trading and information systems; and most of the studies suggest that the reforms have had a positive impact, creating efficiency gains in the price discovery process, increased liquidity and lower volatility. Examples include studies of Milan (Amihud et al, 1990;Majnoni & Massa, 2001), Tokyo (Amihud & Mendelson, 1991), Tel Aviv (Amihud et al, 1997;Kalay et al, 2002), London (Chelley-Steeley, 2008), Dublin (Chelley-Steeley & Lucey, 2008), Paris (Pagano & Schwartz, 2003), and New Zealand (Blennerhassett & Bowman, 1998).…”

Section: Introductionmentioning

confidence: 99%

Share Liquidity and Market Microstructure Reform: The Case of Screen‐based Trading in Mumbai*

Green

Manos²,

Murinde

et al. 2010

Asia-Pac J of Fin Stud

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We investigate the impact of the March 1995 move to screen-based trading on the Mumbai Stock Exchange, using separate samples of more liquid (A) and less liquid (B) shares. Following the move, the average cumulative abnormal return for A shares was 4.5%, whereas that for B shares was over 12%; market liquidity and efficiency increased but the effect on volatility was more ambiguous. We identify a significant cross-sectional relationship between the size of cumulative abnormal returns and firm-specific improvements in liquidity, efficiency, and volatility, with differences in the effects of reform on A and B shares.

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Stock market microstructure and return volatility

Cited by 78 publications

References 10 publications

Interday and intraday volatility: Additional evidence from the Shanghai Stock Exchange

Interday and intraday volatility: Additional evidence from the Shanghai Stock Exchange

A Big Data Approach to Analyzing Market Volatility

Share Liquidity and Market Microstructure Reform: The Case of Screen‐based Trading in Mumbai*

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