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on Market Microstructure |
| By: | Kenan Qiao |
| Abstract: | Market liquidity plays a vital role in the field of market micro-structure, because it is the vigor of the financial market. This paper uses a variable called convexity to measure the potential liquidity provided by order-book. Based on the high-frequency data of each stock included in the SSE (Shanghai Stock Exchange) 50 Index for the year 2011, we report several statistical properties of convexity and analyze the association between convexity and some other important variables (bid/ask-depth, spread, volatility, return.) |
| Date: | 2012–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1211.2078&r=mst |
| By: | Michael King (Ivey Business School, University of Western Ontario); Carol Osler (International Business School, Brandeis University); Dagfinn Rime (Norges Bank) |
| Abstract: | Looking back, 30 years of research on foreign exchange (FX) market microstructure reveals that order flow, heterogeneity among agents, and private information are crucial determinants of short-run exchange rate dynamics. Microstructure researchers have produced empirically-driven models that fit the data surprisingly well. But currency markets are evolving rapidly in response to new electronic trading technologies. Transparency has risen, trading costs have tumbled, and transaction speed has accelerated as new players have entered the market and existing players have modified their behavior. These changes will have profound effects on exchange rate dynamics. Looking forward, we highlight fundamental yet unanswered questions on the nature of private information, the impact on market liquidity, and the changing process of price discovery. We also outline potential microstructure explanations for long-standing exchange rate puzzles. |
| Keywords: | Exchange rates, Market microstructure, Information, Liquidity, Electronic trading |
| JEL: | F31 G12 G15 C42 C82 |
| Date: | 2012–10 |
| URL: | http://d.repec.org/n?u=RePEc:brd:wpaper:54&r=mst |
| By: | Katrin Muehlfeld; Utz Weitzel; Arjen van Witteloostuijn |
| Abstract: | We analyze investors' trading behavior, particularly their coping with fundamental shocks in asset value, depending on individual differences in the sensitivity of two basic neurophysiological systems-the Behavioral Approach System (BAS), the 'driving force' of human behavior, and the Behavioral Inhibition System (BIS), its 'braking system'. By analyzing 15 independent experimental asset markets with a total of 171 participants, we find that differences in BAS and BIS sensitivity affect trading in both 'normal' and shocktrading environments: under normal trading conditions, individuals with a more sensitive BAS are more active traders, prefer riskier portfolios, and generate higher individual overall profits. High BIS subjects generate lower scalping and overall profits. Fundamental shocks generally reinforce the preference of high BAS investors for riskier portfolios, while positive shocks 'unfreeze' high BIS investors: they trade more frequently and generate higher profits. At the market level, normal trading in markets with a high BIS median is associated with lower volatility, compared to low BIS median markets, while greater concentration of traders' BAS scores around the mean is associated with better efficiency and liquidity, compared to markets with lower BAS kurtosis. In high BIS median markets, positive shocks lead to improved efficiency, lower bid-ask spread, and lower volatility. We observe no significant differences in market-level reactions to negative shocks. |
| Keywords: | Individual decision making; Investment decisions; Behavioral Approach System/Behavioral Inhibition System; Experimental asset markets; Fundamental shocks. |
| JEL: | C91 G11 D03 D81 |
| Date: | 2012–11 |
| URL: | http://d.repec.org/n?u=RePEc:use:tkiwps:1218&r=mst |
| By: | Austin Gerig |
| Abstract: | High-speed computerized trading, often called "high-frequency trading" (HFT), has increased dramatically in financial markets over the last decade. In the US and Europe, it now accounts for nearly one-half of all trades. Although evidence suggests that HFT contributes to the efficiency of markets, there are concerns it also adds to market instability, especially during times of stress. Currently, it is unclear how or why HFT produces these outcomes. In this paper, I use data from NASDAQ to show that HFT synchronizes prices in financial markets, making the values of related securities change contemporaneously. With a model, I demonstrate how price synchronization leads to increased efficiency: prices are more accurate and transaction costs are reduced. During times of stress, however, localized errors quickly propagate through the financial system if safeguards are not in place. In addition, there is potential for HFT to enforce incorrect relationships between securities, making prices more (or less) correlated than economic fundamentals warrant. This research highlights an important role that HFT plays in markets and helps answer several puzzling questions that previously seemed difficult to explain: why HFT is so prevalent, why HFT concentrates in certain securities and largely ignores others, and finally, how HFT can lower transaction costs yet still make profits. |
| Date: | 2012–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1211.1919&r=mst |
| By: | Guo, Hong; He, Yinghua; Nielsson, Ulf; Yang, Jiong |
| Abstract: | The paper empirically explores whether more trade transparency improves or deteriorates market liquidity. The analysis takes advantage of a unique setting in which the Shanghai Stock Exchange offered more trade transparency to market participants subscribing to a new software package. First, in contrast to popular policy belief, the paper finds that more transparency need not improve market liquidity. Second, since the effective level of market transparency is bound to depend on how many traders are subscribing to the data, this study is able to empirically estimate the functional form between market-wide transparency and liquidity. The relationship is shown to be non-monotonic, which can explain the lack of consensus in the existing literature where each empirical study is naturally confined to specific parts of the transparency domain. |
| Keywords: | transparency, liquidity, market microstructure, market design |
| JEL: | G14 G28 |
| Date: | 2012–07 |
| URL: | http://d.repec.org/n?u=RePEc:tse:wpaper:26441&r=mst |
| By: | Nikolaus Hautsch; Julia Schuamburg; Melanie Schienle; |
| Abstract: | Multiplicative error models (MEM) became a standard tool for modeling conditional durations of intraday transactions, realized volatilities and trading volumes. The parametric estimation of the corresponding multivariate model, the so-called vector MEM (VMEM), requires a specification of the joint error term distribution, which is due to the lack of multivariate distribution functions on Rd + defined via a copula. Maximum likelihood estimation is based on the assumption of constant copula parameters and therefore, leads to invalid inference, if the dependence exhibits time variations or structural breaks. Hence, we suggest to test for time-varying dependence by calibrating a time-varying copula model and to reestimate the VMEM based on identified intervals of homogenous dependence. This paper summarizes the important aspects of (V)MEM, its estimation and a sequential test for changes in the dependence structure. The techniques are applied in an empirical example. |
| Keywords: | vector multiplicative error model, copula, time-varying copula, highfrequency data |
| JEL: | C32 C51 |
| Date: | 2012–09 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2012-054&r=mst |
| By: | Eduardo Rossi (Department of Economics and Management, University of Pavia); Dean Fantazzini (Moscow School of Economics, M.V. Lomonosov Moscow State University) |
| Abstract: | Intraday return volatilities are characterized by the contemporaneous presence of periodicity and long memory. This paper proposes two new parameterizations of the intraday volatility: the Fractionally Integrated Periodic EGARCH and the Seasonal Fractional Integrated Periodic EGARCH, which provide the required flexibility to account for both features. The periodic kurtosis and periodic autocorrelations of power transformations of the absolute returns are computed for both models. The empirical application shows that volatility of the hourly Emini S&P 500 futures returns are characterized by a periodic leverage effect coupled with a statistically significant long-range dependence. An out-of-sample forecasting comparison with alternative models shows that a constrained version of the FI-PEGARCH provides superior forecasts. A simulation experiment is carried out to investigate the effects that sample frequency has on the fractional differencing parameter estimate. |
| Keywords: | Intraday volatility, Long memory, FI-PEGARCH, SFI-PEGARCH, Periodicmodels. |
| JEL: | C22 C58 G13 |
| Date: | 2012–11 |
| URL: | http://d.repec.org/n?u=RePEc:pav:demwpp:015&r=mst |