Mixture Of Distribution Hypothesis Research Articles

Liquidity, surprise volume and return premia in the oil market

We investigate oil market price dynamics in the context of the Mixture of Distributions Hypothesis (MDH). Our econometric model addresses autoregressive properties in returns, the impact of surprise volume and conditional oil market return volatility as well as oil market liquidity in the conditional return equation. Surprise volume as a proxy of private information flow is shown to be unrelated to a set of standard market liquidity proxies. Oil return heteroscedasticity is found to be partly explained by surprise volume, a finding that is consistent with the MDH. Our findings further show that both oil market liquidity as well as surprise volume shocks are priced in the oil market. As such, lower levels of lagged market liquidity relate to above average conditional returns. Surprise volume shocks are associated with lower conditional oil market returns jointly with higher contemporaneous conditional return volatility. Lagged market liquidity dominates conditional volatility in predicting conditional oil price returns.

The asymmetric price-volume relation revisited: evidence from Qatar

PurposeThis study aims to revisit the stock price–volume relations, providing new evidence from the emerging market of Qatar. In particular, three main issues are examined using both aggregate market- and sector-level data. First, the return–volume relation and whether or not this relation is asymmetric. Second, the common characteristics of return volatility; and third, the nature of the relation between trading volume and return volatility.Design/methodology/approachThe study uses the OLS and VAR modeling approaches to examine the contemporaneous and dynamic (causal) relations between index returns and trading volume, respectively, while an EGARCH-X(1,1) model is used to analyze the volatility–volume relation. The data set comprises daily index observations and the corresponding trading volumes for the entire market and the individual seven sectors of the Qatar Exchange (i.e. banks and financial services, consumer goods and services, industrials, insurance, real estate, telecommunications and transportation).FindingsThe empirical analysis reports evidence of a positive contemporaneous return–volume relation in all sectors barring transportation and insurance. This relation appears to be asymmetric for all sectors. For the market and almost all sectors, there is no significant causality between returns and volume. By and large, these findings lend support for the implications of the mixture of distributions hypothesis (MDH). Lastly, the information content of lagged volume seems to have an important role in predicting the future dynamics of return volatility in all sectors, with the industrials being the exception.Practical implicationsThe findings provide important implications for portfolio managers and investors, given that the volume of transactions is generally found to be informative about the price movement of sector indices. Specifically, tracking the behavior of trading volume over time can give a broad portrayal of the future direction of market prices and volatility of equity, thereby enriching the information set available to investors for decision-making.Originality/valueBased on both market- and sector-level data from the emerging stock market of Qatar, this study attempts to fill an important void in the literature by examining the return–volume and volatility–volume linkages.

Contemporaneous and Causal Relationship between Returns and Volumes: Evidence from Nifty Futures

AbstractMotivated by the mixture of distribution hypothesis (MDH) and sequential arrival of information hypothesis (SAIH), we investigate the relationship between intraday returns and volumes using derivatives data from the emerging Indian market. Using robust statistical estimation, we document the evidence of both MDH and SAIH in one of the most traded financial markets in India (and the world). We document the presence of strong positive association between returns and volumes. We further report the evidence of strong causality from intraday returns to volumes than vice versa.

The Relationship between Stock Market Volatility and Trading Volume: Evidence from South Africa

The rate of information flow into the market in generating market volatility has been a much debated issue in the finance research. Moreover, since the rate of information cannot be directly observable and quantifiable, trading volume has been considered to be a good proxy for incorporating such information. The mixture of distribution hypothesis (MDH) and the sequential information hypothesis (SIAH) provide the theoretical justification for the relationship between trading volume and market volatility. This paper revisits the relationship between equity trading volume and returns volatility for the Johannesburg Stock Exchange (JSE) of South Africa using daily data over the period of 6th July 2006 to 31st August 2016. Further, it analyzed an after-crisis period, i.e., 1/04/2008 to 8/31/2016, in order to verify the findings. EGARCH and Granger causality models are employed to analyze the volume-volatility relationship. Also the level of volatility persistence has been compared before and after the inclusion of trading volume in the volatility model as an exogenous variable. The analysis shows that the JSE exhibits volatility asymmetry implying that the return volatility responds more to the bad news than to the good news. The relationship between trading volume and market volatility is found to be positive and contemporaneous supporting the MDH. This study also uncover that the volatility persistence remains high even after the inclusion of trading volume as an explanatory variable in the volatility model. The above set of results also holds for the post-crisis sub-sample. Furthermore, the pairwise Granger causality tests indicate a feedback relationship between volume and volatility only in the case of the sub-sample. But for the full sample, a unidirectional causality between volume and volatility, with trading volume Granger causes market volatility was observed. As it was observed that there is a positive and contemporaneous relationship between volatility, and also that the trading volumes cannot attenuate the level of volatility persistence, findings advise the equity market participants to be cautious in their trading behavior; and through messages to the regulators that risk management practice should be strengthened in order to control for market volatility that associated with increasing trading volume.

Baidu news information flow and return volatility: Evidence for the Sequential Information Arrival Hypothesis

This paper employs Baidu News as the proxy for information flow and investigates competing hypotheses on the relationships between information flow and return volatility in Chinese stock market. The empirical results show that: (1) trading volume and return volatility are not driven by the same variable, i.e., the information flow, and thus contradicts the predication of the Mixture of Distribution Hypothesis (MDH); (2) there exist significant lead-lag relationships between information flow and return volatility, which is in accordance with the Sequential Information Arrival Hypothesis (SIAH); (3) these findings are robust to alternative measurement of return volatility and subsample analysis. Generally speaking, these findings contradict the prediction of MDH and support the SIAH.

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.

Mixture of distribution hypothesis: Analyzing daily liquidity frictions and information flows

The mixture of distribution hypothesis (MDH) model offers an appealing explanation for the positive relation between trading volume and volatility of returns. In this specification, the information flow constitutes the only mixing variable responsible for all changes. However, this single static latent mixing variable cannot account for the observed short-run dynamics of volume and volatility. In this paper, we propose a dynamic extension of the MDH that specifies the impact of information arrival on market characteristics in the context of liquidity frictions. We distinguish between short-term and long-term liquidity frictions. Our results highlight the economic value and statistical accuracy of our specification. First, based on some goodness of fit tests, we show that our dynamic two-latent factor model outperforms all competing specifications. Second, the information flow latent variable can be used to propose a new momentum strategy. We show that this signal improves once we allow for a second signal – the liquidity frictions latent variable – as the momentum strategies based on our model present better performance than those based on competing models.

Information arrival and volatility: Evidence from the Saudi Stock Exchange (Tadawul)

This paper investigates the validation of the Mixture of Distributions Hypothesis (MDH) using trading volume and number of trades as contemporaneous proxies for information arrival in 15 sector indices of the Saudi Stock Exchange (Tadawul) using the TGARCH model. Findings provide strong evidence for the validity of the MDH for the Saudi market. Volatility persistence decreases when the trading volume and the number of trades are included in the conditional variance equation. The most striking finding is that contemporaneous number of trades is a better proxy for information arrival than trading volume, interacting with volatility in a manner anticipated under the MDH. This can be attributed to the unique characteristic of the Saudi equity market where only domestic investors are allowed to execute trade transactions. Further, the results reveal that the leverage effect was amplified, indicating a more pronounced asymmetric effect of bad news on volatility.

Information Arrival and Volatility: Evidence from the Saudi Arabian Stock Exchange (Tadawul)

This paper investigates the validation of the Mixture of Distributions Hypothesis (MDH) using trading volume and number of trades as contemporaneous proxies for information arrival in 15 sector indices of the Saudi Stock Exchange (Tadawul) using the TGARCH model. Findings provide strong evidence for the validity of the MDH for the Saudi market. Volatility persistence decreases when the trading volume and the number of trades are included in the conditional variance equation. The most striking finding is that contemporaneous number of trades is a better proxy for information arrival than trading volume, interacting with volatility in a manner anticipated under the MDH. This can be attributed to the unique characteristic of the Saudi equity market where only domestic investors are allowed to execute trade transactions. Further, the results reveal that the leverage effect was amplified, indicating a more pronounced asymmetric effect of bad news on volatility.

Modelling the relationship between future energy intraday volatility and trading volume with wavelet

ABSTRACTAlthough the energy and stock markets are both characterized by volatility and liquidity, and there has been substantial research to explore the relationship between volatility and trading volume (TV) in stock markets, few researchers have investigated this relationship in energy markets. Moreover, studies that have explored this association within energy markets did not describe its nature or impetus. To redress this oversight, we investigate this relationship using intraday data from the oil and gas markets – the most liquid energy markets in the world. In this way, the current article extends the previous studies through the use of a frequency approach to propose an original analysis of the relationship between volume and volatility. More specifically, we employ a continuous wavelet transform to identify the lead–lag phase between volatility and volume. This framework supplants usual time series modelling, as it uses a measure of coherence for different frequencies and time-scales to capture further changes and time variation in the volume–volatility relationship. Our results provide supportive evidence for the well-known positive relationship between realized volatility and TV, thereby supporting the mixture distribution hypothesis. In particular, our results show that volume causes volatility only during ‘turbulent times’, while volatility causes volume during ‘good times’. Furthermore, there is no relationship between volume and volatility in the long term, due to the absence of noise traders and liquidity traders in the long run. These findings are helpful for investors and policymakers as they contribute to better forecast the TV and price volatility during turbulent and calm periods and over several investment horizons.

Investor structure and the price–volume relationship in a continuous double auction market: An agent-based modeling perspective

Abstract This paper investigates the impact of investor structure on the price–volume relationship by simulating a continuous double auction market. Connected with the underlying mechanisms of the price–volume relationship, i.e., the Mixture of Distribution Hypothesis (MDH) and the Sequential Information Arrival Hypothesis (SIAH), the simulation results show that: (1) there exists a strong lead-lag relationship between the return volatility and trading volume when the number of informed investors is close to the number of uninformed investors in the market; (2) as more and more informed investors entering the market, the lead-lag relationship becomes weaker and weaker, while the contemporaneous relationship between the return volatility and trading volume becomes more prominent; (3) when the informed investors are in absolute majority, the market can achieve the new equilibrium immediately. Therefore, we can conclude that the investor structure is a key factor in affecting the price–volume relationship.

The Empirical Investigation of Causal Relationship between Intraday Return and Volume in Indian Stock Market

This article examines the causal relationship between intraday return and volume by using 1-minute intraday data of 35 stocks of S&P CNX Nifty index during the period from April 2007 to March 2011. The empirical analysis provides evidence to the mixture of distribution hypothesis (MDH), as a majority of stocks of S&P CNX Nifty index show no causal relationship between the intraday return–volume relationships. However, this study finds evidence of significant causal and lead–lag relations between the intraday return–volume associations for some stocks. These findings reveal strong indication of uni- and bi-directional causality, thus supporting the sequential information arrival hypothesis (SIAH) which suggests that lagged values of volume provide the predictability component of current return (vice versa).

The relationship between trading volumes, number of transactions, and stock volatility in GARCH models

We examine the relationship between trading volumes, number of transactions, and volatility using daily stock data of the Tokyo Stock Exchange. Following the mixture of distributions hypothesis, we use trading volumes and the number of transactions as proxy for the rate of information arrivals affecting stock volatility. The impact of trading volumes or number of transactions on volatility is measured using the generalized autoregressive conditional heteroscedasticity (GARCH) model. We find that the GARCH effects, that is, persistence of volatility, is not always removed by adding trading volumes or number of transactions, indicating that trading volumes and number of transactions do not adequately represent the rate of information arrivals.

Trading and non-trading period Internet information flow and intraday return volatility

This paper employs the news appeared in Baidu News as the proxy for Internet information flow, separates them into trading period and non-trading period information and provides alternative evidence for the Mixture of Distribution Hypothesis (MDH). The empirical results show that the contemporary information can effectively reduce the volatility persistence; meanwhile, the lead information and the aggregate information also show some explanatory power. Some future directions are pointed out in the concluding remarks.

Bid-Ask Spreads, Trading Volume and Return Volatility: Intraday Evidence from Indian Stock Market

This paper empirically examines the relationship between stock return volatility, trading volume and bid-ask spread within the scope of mixture of distribution hypothesis (MDH) and sequential information arrival hypothesis (SIAH) in the Indian stock market using high frequency 5-minute data set over the period of 2 July 2012 to 31 December 2012. This is the first kind of study in India using bid-ask spread as an additional information variable along with trading volume to investigate the relationship with stock return volatility. Our empirical findings provide evidence of a positive contemporaneous relationship between return volatility and trading volume, and also between return volatility and bid-ask spread. Moreover, the results of Granger causality test show that the information content of trading volume and bid-ask spread are useful for predicting stock return volatility. Our results indicate that information arrival to investors tends to follow a sequential rather than a simultaneous process. This finding is consistent with the sequential information arrival hypothesis and contradicts the mixture of distribution hypothesis.

Do Trading Volume and Bid-Ask Spread Contain Information to Predict Stock Returns? Intraday Evidence from India

Relying on the mixture of distribution hypothesis (MDH) and the sequential information arrival hypothesis (SIAH), this paper empirically examines the relationship between stock returns, trading volume and bid-ask spread for 50 Indian stocks using high frequency 5-minute data set for the period July 2, 2012 to December 31, 2012. This is the first study in India using bid-ask spread as yet another measure of information flow variable along with trading volume. Our empirical findings provide evidence of a positive contemporaneous relationship between absolute returns and trading volume as well as between absolute returns and bid-ask spread. The Granger causality test results show that the information content of trading volume and bid-ask spread are useful for predicting stock returns in Indian stock market. Overall results seem to indicate that information arrival to investors tends to follow a sequential rather than simultaneous process as suggested by SIAH. In summary, both trading volume and bid-ask spread serve as a good measure of information variable in India.

MIXTURE DISTRIBUTION HYPOTHESIS AND THE IMPACT OF A TOBIN TAX ON EXCHANGE RATE VOLATILITY: A REASSESSMENT

This paper applies smooth transition regressions to incorporate nonlinearity into the impact of trading volume on exchange rate volatility, the so-called mixture distribution hypothesis (MDH). Linking this analysis to the Tobin tax debate, we provide the first empirical corroboration that such a tax may be effective in limiting speculation and reducing exchange rate volatility, especially in turbulent times. Our study points to two main results. First, we show that nonlinearities should be taken into account to explain the MDH. When volatility, spreads, and volume are simultaneously high, the relationship between trading volume and volatility tends to grow stronger and thus the MDH holds in turbulent periods. Second, on the assumption of constant trading volume elasticity, a Tobin tax would have been stabilizing and effective in the 2008 crisis.

Financial Market Liquidity: Who is Acting Strategically?

In a new environment where liquidity providers as well as liquidity consumers act strategically, understanding how liquidity flows and dries-up is key. In this paper, we propose a dynamic extension of the seminal model of Tauchen and Pitts (1983)’ Mixture of Distributions Hypothesis (MDH) that specifies the impact of information arrival on market characteristics in the context of liquidity frictions. In our model, the daily price change and volume processes are represented by a bivariate mixture of distributions, conditioned by two latent time-persistent variables It and Lt. Since the price change and volume equations are nonlinear functions of the first latent variable It, we use an Extended Kalman Filter (EKF) to filter the two latent variables and estimate the model parameters, simultaneously. This procedure enables us to: (i) capture the impact of long-lasting liquidity frictions on the daily price change and volatility dynamics; (ii) separate out the impact of both long and short-lasting liquidity frictions, on the serial correlation of the daily volume. Our results show that, 48% (44/92) of the stocks of the FTSE100 are actually facing liquidity problems. Amongst these stocks, 28% (26/92) of them are also facing a slow-down in the information propagation in prices due to long-term investors’ strategic behavior.

Factors Effecting Trading Volume: A Test of Mixed Distribution Hypothesis

This paper investigates the empirical relationship between trading volume and conditional volatility using data from Amman Stock Exchange (ASE) within the framework of Mixed Distribution Hypothesis (MDH). Our sample covered 27 securities, which is most active stocks traded for the period span from 2002 to 2012. Generalized Autoregressive Conditional Heteroskedasticity (GARCH) k Exchange model employed in order to test the persistence in the volatility of stock returns. Our results confirm positive and strong relationship between trading volume for individual stocks and conditional volatility of returns. Moreover, the degree of volatility persistence reduced through the process of adding the contemporaneous volume into the conditional variance equation of GARCH model, and this is according to the predictions of the Mixture of Distributions Hypothesis (MDH).

Testing the mixture of distributions hypothesis on target stocks

We test the mixture of distributions hypothesis (MDH) in which equity trading volumes and return volatilities are derived from an unobservable mixing variable, the speed of information flow to the market. Interpreting the public announcement of a takeover offer as a regime-changing firm-specific informational event, we study the daily trading volumes and price volatilities of 190 US targets from four years before the takeover announcement until the conclusion of the bid. We find strong evidence for MDH-consistent positive volume–volatility relationships before and after takeover announcements that are supportive of the applicability of the MDH in the market for corporate control.