Intraday analysis of regulation change in microstructure: evidence from an emerging market

PurposeThis study investigates the impact of simultaneously replacing both midday single-price call auction and lunch break with multi-price continuous trading on intraday volatility–volume patterns as well as the intraday volatility–volume nexus.Design/methodology/approachThe analysis utilises 150 m tick-by-tick transaction data related to 333 stocks traded on Borsa Istanbul Equity Market covering a period of 2 months prior to and following the change. In addition to graphic comparisons, the study uses difference in mean tests, panel-fixed generalized least squares (GLS), panel-random GLS and random-effects linear models with AR(1) disturbance regression estimations.FindingsThe results show that intraday volatility and trading volume form an inverse J-shape and are positively correlated. It is observed that the implementation of the regulation change decreased intraday volatility and increased trading volume. Additionally, the results indicate a negative volatility–liquidity and a positive volume–liquidity relationship, supporting the mixture of distribution hypothesis.Research limitations/implicationsEnhanced market efficiency provides greater opportunity for investment and risk management. Investors can benefit from the findings on the intraday volatility–volume nexus, which is an indicator of informed trading, and regulatory authorities can use volume to oversight volatility.Originality/valueThis very rare regulation change of the simultaneous replacement of the lunch break and midday call auction with continuous trading is investigated in the context of intraday volume and volatility. This study also expands upon some important findings on the volume–volatility nexus for the Turkish Stock Market.

Time-Varying Volatility in Bitcoin Market and Information Flow at Minute-Level Frequency

In this article, we analyze the time series of minute price returns on the Bitcoin market through the statistical models of the generalized autoregressive conditional heteroscedasticity (GARCH) family. We combine an approach that uses historical values of returns and their volatilities—GARCH family of models, with a so-called Mixture of Distribution Hypothesis, which states that the dynamics of price returns are governed by the information flow about the market. Using time series of Bitcoin-related tweets, the Bitcoin trade volume, and the Bitcoin bid–ask spread, as external information signals, we test for improvement in volatility prediction of several GARCH model variants on a minute-level Bitcoin price time series. Statistical tests show that GARCH(1,1) and cGARCH(1,1) react the best to the addition of external signals to model the volatility process on out-of-sample data.

Information Distribution and Informed Trading in Mixed and Islamic Capital Markets

The correlation between volume and frequency with return volatility can explicate the information distribution process and informed traders' transaction behavior in a stock market. In this study, the Indonesian stock market represents the mixed market, while the Saudi Arabian stock market represents the Islamic market. We find that 94% and 96% of sharia-compliant stocks in Indonesia and Saudi Arabia follow the Mixture of Distribution Hypothesis (MDH). Consequently, we may conclude that sharia-compliant stocks in both markets are informationally efficient. However, we find that informed traders tend to behave differently in both markets. In the Indonesian market, informed traders exhibit competitive behavior in 95% of shariacompliant stocks and strategic transaction behavior in only 5% of the stocks. In contrast, in the Saudi Arabian market, we find that informed traders exhibit competitive behavior in only 38% of the stocks and strategic behavior in 62% of the stocks. The findings suggest that social and religious contexts may affect market participants' behavior.

Did the Covid-19 Pandemic Affect the Relationship Between Trading Volume and Return Volatility in the Cryptocurrencies?

In this study, it was investigated whether the Covid-19 pandemic, which started to affect the world in early 2020, influenced the relationship between return volatility and trading volume in the cryptocurrency market. In the empirical part of the study, 40 cryptocurrencies were included in the analysis. The data were divided into two separate periods as before and during the pandemic. Two alternative estimators developed by Garman and Klass (1980) and by Rogers and Satchell (1991) were used to measure the return volatility of cryptocurrencies. With causality and simultaneous correlation analyses, it was determined that the sequential information arrival hypothesis was valid in the cryptocurrency market in the pre-pandemic period. In the pandemic period, the sequential information arrival hypothesis lost its effect and left its place to the mixture of distribution hypothesis.

The Arrival of Information and Price Adjustment Across Extreme Quantiles: Global Evidence

This study investigates the impact of information arrival on prices for 21 major global market indices for the period 1998–2018, employing quantile regression methodology. The results show that there is a contemporaneous and causal effect of volume on returns. This return-volume relation is a manifestation of systematic market-wide information that is released in an autocorrelated manner to market participants. This information is absorbed by the market participants over short horizons, within a day. This leads to uniform expectations and, in turn, lower volatility levels. The effect of volume on return is heterogeneous across the conditional quantiles, reflecting the contrasting patterns in the transmission of positive and negative news. This evidence is more pronounced when the intensity of information arrival is high (the tails of return distribution), which is consistent with the mixture of distribution hypothesis and information asymmetry hypothesis.

MODIFIKASI MODEL KONTEMPORER DAN KAUSALITAS ANTARA VOLUME PERDAGANGAN, BID-ASK SPREAD, RETURN SAHAM DAN VOLATILITAS RETURN (STUDI KASUS: INDEKS LQ-45)

Suatu variabel perdagangan saham mungkin dipengaruhi oleh variabel perdagangan saham lain pada periode yang sama, variabel perdagangan saham itu sendiri maupun variabel lainnya pada periode yang berbeda. Penelitian ini menyajikan modifikasi dari model Valentika N, Nugrahani E and Lesmana D (2017) pada data saham Indonesia dengan studi kasus LQ-45. Penelitian ini secara empiris menguji hubungan antara volume perdagangan, bid-ask spread dan return saham terhadap volatilitas return. Hasil regresi menunjukkan bahwa tidak cukup bukti untuk mendukung teori mixture of distribution hypothesis (MDH) pada pasar. Berdasarkan uji kausalitas Granger, terdapat dugaan bahwa perdagangan intraday sampel saham LQ-45 cenderung mengikuti teori MDH.Keywords: volume perdagangan, bid-ask spread, volatilitas dan return saham

The relationship between trading volume and exchange rate volatility: linear or nonlinear?

PurposeThe purpose of this paper is to examine the linear and nonlinear relations between returns volatility and trading volume for the Indian currency futures market.Design/methodology/approachTo examine the contemporaneous relation between returns volatility and volume, the author uses the generalized method of moment estimator. For the linear causal relation, the author makes use of Granger (1969) bivariate vector autoregression model. The author tests for nonlinear Granger causality between returns volatility and trading volume based on a modified version of the Baek and Brock (1992) nonparametric technique developed by Hiemstra and Jones (1994).FindingsThe results indicate a negative contemporaneous relation between returns volatility and trading volume; therefore, the mixture of distribution hypothesis is not supported. The results of both linear and nonlinear Granger causality between futures returns volatility and trading volume indicate a significant bidirectional relation between the two variables lending support to the sequential arrival of information hypothesis. The results are robust to divergence of opinions as proxied by open interest.Practical implicationsThe findings of this paper are important for the participants in the market and regulators. The participants in the market require alternatives to diversify their risk. The significant causal relation between returns volatility and trading volume implies that trading volume helps predict the futures prices and should lead to creation of more reliable hedging strategies for investment purposes. Furthermore, it may interest the regulators who need to decide upon the appropriateness of their policies in the currency futures market.Originality/valueTo the best of the author’s knowledge, there is no study that investigates the forecast ability of trading volume to futures returns volatility in an emerging currency futures market. Given that currency futures market is one of the largest markets in the world, and Indian rupee has seen wide fluctuations in the recent years, it seems exciting to explore the price–volume relation in the Indian currency futures market.

Contemporaneous Relationship between Trading Volume and Stock Returns Volatility : Evidence from Nepalese Stock Market

This paper examines the contemporaneous relation between trading volume and stock returns volatility for Nepalese stock market using monthly data for the period 2005 mid-July to 2017 mid-April. The study uses ordinary least square method and analyzes whether rising price leads to higher volume or vice versa. The study also investigates the association between trading volume and stock returns volatility based on monthly data of NEPSE index and examines the effects of trading volume on stock returns volatility using GARCH (1, 1) model. The study finds positive contemporaneous relationship between trading volume and stock return volatility. The study result indicates that the relationship between trading volume and return volatility is asymmetric. The findings strongly support the hypothesis that higher trading volume is associated with an increase in stock return volatility, but offers little support to the sequential arrival hypothesis and the mixture of distribution hypothesis. Finally, the findings support the weak-form efficient market hypothesis in Nepalese stock market.

Revisiting the price-volume relationship: a cross-currency evidence

Purpose The purpose of this paper is to examine the contemporaneous and causal relationship between returns (volatility) and trading volume in the Indian currency futures market for selected currency pairs; USD-INR, EUR-INR, GBP-INR and JPY-INR, from August 2008 to December 2014. Design/methodology/approach The data for all the currency futures series has been taken from National Stock Exchange of India Limited which represents the daily settlement prices along with trading volume. The contemporaneous returns-volume relation is tested using the generalized method of moments, and Granger-causality framework impulse response function is used to test the predictive ability of returns (volatility) and volume for each other. Findings The author reports a positive contemporaneous relationship between futures returns and trading volume which persists even after controlling for heteroskedasticity providing support to mixture of distribution hypothesis. The results show a unidirectional Granger causality from futures returns to volume. However, there is a significant bidirectional Granger causality between returns volatility and volume lending support to sequential arrival of information hypothesis. Next, the results for cross-currencies show significant influence of US dollar on the volume and returns of all other currencies. Overall, the author suggests that the short- to medium-term movements in the currency markets are dominated by market microstructure and not by fundamentals. Practical implications The findings of this paper are very important for the participants in the market and regulators. The participants in the market require alternatives to diversify their risk. The significant relationship between futures returns (volatility) and trading volume implies that the current trading volume help predict the futures prices and should lead to creation of more reliable hedging strategies for investment purposes. Further, it may interest the regulators who need to decide upon the appropriateness of their policies in the currency futures market. Based on returns-volume relation, they need to set forth market restrictions such as daily price movement and position limits. Originality/value To the best of the knowledge, no study has yet investigated the forecast ability of trading volume to price changes and their volatility in the Indian currency futures market. Given that currency futures market is one of the largest markets in the world, and Indian rupee has seen wide fluctuations in the recent years, it seems exciting to explore the price-volume relationship in the Indian currency futures market.