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This paper aims to investigate the performance of estimators of the bid-ask spread in a wide range of circumstances and sampling frequencies. The bid-ask spread is important for many reasons. Because spread data are not always available, many methods have been suggested for estimating the spread. Existing papers focus on the performance of the estimators either under ideal conditions or in real data. The gap between ideal conditions and the properties of real data are usually ignored. The consistency of the estimates across various sampling frequencies is also ignored.

The estimators and the possible errors are analysed theoretically. Then we perform simulation experiments, reporting the bias, standard deviation and root mean square estimation error of each estimator. More specifically, we assess the effects of the following factors on the performance of the estimators: the magnitude of the spread relative to returns volatility, randomly varying of spreads, the autocorrelation of mid-price returns and mid-price changes caused by trade directions and feedback trading.

The best estimates come from using the highest frequency of data available. The relative performance of estimators can vary quite markedly with the sampling frequency. In small samples, the standard deviation can be more important to the estimation error than bias; in large samples, the opposite tends to be true.

There is a conspicuous lack of simulation evidence on the comparative performance of different estimators of the spread under the less than ideal conditions that are typical of real-world data. This paper aims to fill this gap.

Cryptocurrency markets are notoriously noisy, but not all markets might behave in the exact same way. Therefore, the aim of this paper is to investigate which one of the cryptocurrency markets contributes the most to the common volatility component inherent in the market.

The paper extracts each of the cryptocurrency's markets' latent volatility using a stochastic volatility model and, subsequently, models their dynamics in a fractionally cointegrated vector autoregressive model. The authors use the refinement of Lien and Shrestha (2009, J. Futures Mark) to come up with unique Hasbrouck (1995, J. Finance) information shares.

The authors’ findings indicate that Bitfinex is the leading market for Bitcoin and Ripple, while Bitstamp dominates for Ethereum and Litecoin. Based on the dominant market for each cryptocurrency, the authors find that the volatility of Bitcoin explains most of the volatility among the different cryptocurrencies.

The authors’ findings are limited by the availability of the cryptocurrency data. Apart from Bitcoin, the data series for the other cryptocurrencies are not long enough to ensure the precision of the authors’ estimates.

To date, only price discovery in cryptocurrencies has been studied and identified. This paper extends the current literature into the realm of volatility discovery. In addition, the authors propose a discrete version for the evolution of a markets fundamental volatility, extending the work of Dias et al. (2018).

We examine the informational roles of trades and time between trades in the domestic and overseas US Treasury markets. A vector autoregressive model is employed to assess the information content of trades and time duration between trades. We find significant impacts of trades and time duration between trades on price changes. Larger trade size induces greater price revision and return volatility, and higher trading intensity is associated with a greater price impact of trades, a faster price adjustment to new information and higher volatility. Higher informed trading and lower liquidity contribute to larger bid–ask spreads off the regular daytime trading period.

This paper aims to examine the impact of a reduction in tick size on the information content of the order book by using data from the Taiwan Stock Exchange (TWSE).

To estimate the information content of the order book, the modified information share proposed by Hasbrouck and extended by Lien and Shrestha is used in this paper.

The empirical results show that the limit order book is informative. Furthermore, the results indicate that a reduction in tick size will decrease the information content of the order book and the decrease in the information content of the order book is positively related to the thinner order book.

This paper suggests that, in order to enhance the information content of the order book, the TWSE should disclose the full limit order book.

In previous research, Friend and Hasbrouck theorized that managerial insiders (officers and directors) have a personal incentive to cause the firm to use less than the optimal amount of debt in its capital structure. They suggested this occurs because officers and directors have a large proportion of their personal wealth invested in the firm in the form of common stock holdings and firm‐specific human capital. This makes managerial insiders reluctant to use the optimal amount of debt financing for the firm because of the additional bankruptcy risk higher levels of debt engender. I test FH’s theory and find evidence that supports it. Specifically, the amount of debt in our sample firms’ capital structures declines as the percentage of the firm’s common stock held by the CEO and other officers and directors increases. A direct relationship is found between blockholder share ownership and our sample firms’ debt/equity ratio. This suggests that monitoring by blockholders is effective in controlling the suboptimal debt usage agency problem. Further, for any given level of blockholder share ownership, the greater the number of blockholders a firm has the less effective blockholders are in raising the amount of debt in the firm’s capital structure. Lastly, some weak evidence was found suggesting that a dual leadership structure was effective in increasing the amount of debt in a firm’s capital structure.

Since illiquidity risk is one of the most important pricing factors of assets, the aim of this paper is to evaluate the suitability of proxies of illiquidity prevalent in the asset pricing literature and their explanatory power in asset pricing tests.

Using the available high‐frequency intra‐day data, the paper constructs some proxies of illiquidity as benchmarks and then evaluates proxies of illiquidity based on inter‐day data.

The empirical results provide convincing evidence that turnover is the most suitable proxy of illiquidity in the Chinese stock market. It is not only hghly related to intra‐day data‐based proxies of illiquidity but also completely superior to other measures of illiquidity in asset pricing tests.

First, the paper applies illiquidity measurements from microstructure theory and the available high‐frequency data, and examines the suitability of illiquidity proxies in asset pricing literature in the Chinese stock market. Rational basics are provided to test the applicability of illiquidity measures in the Chinese stock market. Second, the paper introduces illiquidity proxies into asset pricing models to extend their explanatory power. The paper's results may help researchers to select illiquidity proxies more cautiously.

This study confirms and extends previous research by providing a detailed longitudinal examination of the strategic group and performance relationship in the hospital industry from 1983 to 1993. Based on a deductive approach using Porter's (1980) typology, we find that matching strategy to environment affects hospital performance, that the appropriate match between strategy and environment changed over the 1983 to 1993 time period, and that hospitals combining a low cost and differentiation strategy (i.e., a best‐cost approach) performed well during most of the time period examined. We also find significant movement between strategic groups, thus calling into question the degree to which mobility barriers affect between group performance differences. Finally, our research suggests the existence of multiple groups following the same strategic approach, a result that calls into question the view that groups within an industry are monolithic.

This study empirically explores the intraday price discovery mechanism and volatility transmission effect between the dual-listed Indian Nifty index futures traded simultaneously on the onshore Indian exchange, National Stock Exchange (NSE) and offshore Singapore Exchange (SGX) and its spot market by using high-frequency data.

This study applies the vector error correction model to analyze the lead-lag relationship in price discovery among three markets. The contributions of individual markets in assimilating new information into prices are measured using various measures, Hasbrouck's (1995) information share, Lien and Shrestha's (2009) modified information share and Gonzalo and Granger's (1995) component share. Additionally, the Granger causality test is conducted to determine the causal relationship. Lastly, the BEKK-GARCH specification is employed to analyze the volatility transmission.

This study provides robust evidence that Nifty futures lead the spot in price discovery. The offshore SGX Nifty futures consistently ranked first in contributing to price discovery, followed by onshore NSE Nifty futures and finally by the spot. Empirical results also show unidirectional causality and volatility transmission from Nifty futures to spot, as well as bidirectional causal relationship and volatility spillovers between NSE and SGX Nifty futures. These novel findings provide fresh insights into the informational efficiency of the dual-listed Indian Nifty futures, which is distinct from previous literature.

These findings can potentially help market participants, policymakers, stock exchanges and regulators.

Unlike previous studies in this area, this is the first study that empirically examines the intraday price discovery mechanism and volatility spillover between the dual-listed futures markets and its spot market using 5-min overlapping price data and trivariate econometric models.

The purpose of this paper is to study price discovery and price convergence in securities trading within a fragmented market environment where stocks are traded on multiple venues. The results provide novel empirical insights questioning the generalizability of the current literature and aim to expand the understanding of price determination in a fragmented market microstructure.

This paper provides an empirical data analysis based on an event study methodology. The authors applied Thomson Reuters Tick History data covering German blue chip stocks listed on multiple venues in 2009 and 2013. Different time aggregations up to one second are applied to provide an in-depth analysis.

The paper empirically discovers a persistent price leader-follower relationship not only during intraday auctions but also in subsequent continuous trading. The authors found that trading on alternative venues instantly dries out in case the dominant market switches to a call auction. In these situations, alternative markets await and adopt the official price signal of the dominant market although prices on alternative venues still indicate a certain extent of price discovery. This phenomenon remains persistent at different levels of market fragmentation, indicating that alternative trading venues fully accept the price leadership role of the dominant market, no matter their own market share.

This paper provides an innovative empirical setup to analyze price co-movement and convergence based on high-frequent data. Further, the results provide novel and robust insights into the price determination process in fragmented markets that clarify the role of price follower and price leader.

The purpose of this paper is to investigate price discovery for cross-listed stocks on the New Zealand Exchange (NZX) and the Australian Stock Exchange (ASX) and find out the determinants of price discovery between the two markets.

Gonzalo Granger Component Shares and Hasbrouck Information Shares were estimated annually for a sample of 19 cross-listed stocks between 1998 and 2012. Then dynamic panel regressions were used to investigate the driving factors behind price discovery between the NZX and ASX.

Strong downward trends were observed in the contribution to price discovery of the NZX, both for New Zealand firms cross-listing on the ASX and Australian firms cross-listing on the NZX. While in the early years in our sample period, price discovery is dominated by the home market, by 2012, 50 per cent of price discovery for New Zealand firms takes place on the ASX, and the NZX acts as a satellite market for Australian firms. It was also observed that the NZX share of trading activity has a strong positive effect on the NZX level of price discovery, while there is a negative relationship with relative bid–ask spreads.

Results suggest that the importance of the NZX relative to the ASX with regards to price discovery is decreasing over time. Given the importance of price discovery for exchanges, such a finding is concerning for the NZX. The determinants of price discovery found in the paper, such as relative volume and spreads, do, however, offer some guidance on how the NZX could regain price discovery.

This paper offers a longer and broader analysis of price discovery between the NZX and ASX, two highly integrated markets, and extends previous work by exploring the drivers of price discovery in a panel setting.