Mixture Of Distribution Hypothesis Research Articles

An empirical analysis of the volume-volatility nexus in crude oil markets under structural breaks: Implications for forecasting

The present study investigates how volume impacts volatility in crude oil markets, focusing on the Mixture of Distribution Hypothesis (MDH). Our results reveal a positive and significant volatility-volume association in the case of WTI spot markets. However, we cannot draw similar conclusions for Brent crude oil. Furthermore, we find that the presence of volume leads to only a negligible drop in the volatility persistence, thereby rejecting the claims of the MDH. By incorporating volume as a predictor for volatility and modelling the innovations using heavy-tailed distributions, our estimates of volatility for both Brent and WTI crude oil improve significantly. Our findings reveal that volume contains important information that can impact volatility and prices for crude oil. This has significant implications for market participants, institutional investors, as well as regulators as they can design better strategies and policies based on the volume of trade. Also, from the perspective of risk management and hedging, the results provide interesting insights. Our findings are consistent across different time periods, forecasting horizons and robust to alternative methodologies.

The asymmetric relationships between the Bitcoin futures’ return, volatility, and trading volume

This study investigated the asymmetric contemporaneous and lead-lag relationships between return and trading volume as well as return volatility and volume on the Bitcoin futures by analyzing the threshold and smooth transition effects based on the logistic smooth transition regression (LSTR) model with the logistic smooth transition GJR-GARCH framework. The main findings demonstrated that the asymmetric and smooth transition effects existed in both the contemporaneous and lead-lag return-volume and volatility-volume relationships. There were also a one-trading-day to three-trading-day delayed effects from volume to return and volatility. The implication of the findings suggests that there are arbitrage opportunities for investors in the Bitcoin futures market. Furthermore, we confirm that the Mixture of Distribution Hypothesis (MDH) was a better theory than the Sequential Information Arrival Hypothesis (SIAH) in explaining the volatility-volume relationships on the Bitcoin futures. Our findings also showed that the speed of information transmission increased as it got closer to the current trading day. This evidence supported the MDH in the Bitcoin futures market.

Uniform Nonparametric Inference for Spatially Dependent Panel Data

This article proposes a uniform functional inference method for nonparametric regressions in a panel-data setting that features general unknown forms of spatio-temporal dependence. The method requires a long time span, but does not impose any restriction on the size of the cross section or the strength of spatial correlation. The uniform inference is justified via a new growing-dimensional Gaussian coupling theory for spatio-temporally dependent panels. We apply the method in two empirical settings. One concerns the nonparametric relationship between asset price volatility and trading volume as depicted by the mixture of distribution hypothesis. The other pertains to testing the rationality of survey-based forecasts, in which we document nonparametric evidence for information rigidity among professional forecasters, offering new support for sticky-information and noisy-information models in macroeconomics.

Do bitcoin news information flow and return volatility fit the sequential information arrival hypothesis and the mixture of distribution hypothesis?

The financial asset return volatility and information field have continued to compare both hypotheses: sequential information arrival hypothesis (SIAH) and the mixture of distribution hypothesis (MDH). However, numerous former studies have not found an appropriate information indicator but just used trading volume as an indirect proxy. The study examines the relationship between Bitcoin return volatility and information flow instead of the trading volume. We apply a text and web mining to get all related 24,316 news items for Bitcoin from 64 news websites. Next, we apply a sentiment analysis of natural language processing (NLP) to generate information flow data to replace the traditional trading volume. Finally, we appropriate vector autoregressive (VAR) models to catch the lead-lag relationship and Spearman Correlation to test contemporaneous nexus. The study results show that Bitcoin return volatility is affected by the negative information flow and parallels SIAH; the positive information flow impacts Bitcoin return volatility and matches MDH. The empirical result benefits investors in making proper investment decisions in Bitcoin, and the gist of the paper fills the gap in academic literature because the aspect of information is still absent in academia.

Volatility, Volume, and FX Policy Uncertainty: Evidence from Seoul and Shanghai Chinese-Korean Direct Exchange Rate Market

Purpose – This paper empirically analyzed the effect of the FX policy uncertainty (FX EPU) of Korea and the trading volume in the direct Korean won/Chinese yuan (KRW-RMB) exchange market agreed on in June 2014 on the volatility of the won/yuan exchange rate. Design/Methodology/Approach – In this paper, we first collected monthly data on the trading volume in the direct Korean won/Chinese yuan (KRW-RMB) exchange market from July 2016 to March 2021. We adopted the linear autoregressive distributed lag (ARDL) approach proposed by Pesaran et al. (2001) to study the impact of the FX EPU of Korea and the trading volume on the volatility of the won/yuan exchange rate. This paper uses direct Korean won/Chinese yuan (KRW-RMB) exchange market data for the first time. Findings – The main findings are as follows. First, the empirical results suggest that an increase in trading volume decreases short-run exchange rate volatility, while an increase in FX EPU increases short-run exchange rate volatility. Second, we also find that the long-run effects of trading volume are negative, while the effects of FX EPU are not. Third, the effects on volatility of trading volume and FX EPU are negative or positive in both simultaneous and dynamic relationships, which is consistent with the mixture distribution hypothesis. Research Implications – The estimation result of the ARDL model shows the long- and short-run effects of volume and FX EPU on volatility. We provide new empirical evidence for the way in which financial markets process information.

Does the Base Metal Futures Market of India Support the MDH? An Empirical Evidence

The study examines the mixture of distribution hypothesis (MDH) and the sequential information arrival hypotheses (SIH) in the base metal futures market of India. We use near-month futures daily trading data of price, volume and open interest for 7 years. It is downloaded from the official website of Multi Commodity Exchange (MCX), India. The study supports the MDH as it confirms the existence of a contemporaneous correlation between the return and change in volume of all base metal futures traded at MCX, India. The article exhibits no causality between the return and volume change of metal futures which supports the MDH and contradicts the SIH. This indicates a greater level of market efficiency. The study finds unidirectional causality between the return and daily change of open interest and bidirectional causality between the change in volume and change in open interest. This is found for all base metal futures and this aspect is left for in-depth analysis by the futures studies.

Relationship between Stock Returns and Trading Volume at the Bourse Régionale des Valeurs Mobilières, West Africa

The objective of this paper is to study the contemporaneous relationship and the dynamic relationship between the stock index return and the trading volume on the Bourse Régionale des Valeurs Mobilières using daily data from 5 January 2015 to 31 October 2022. Estimations are made using the generalized method of moments (GMM) and generalized autoregressive conditional heteroscedasticity or GARCH (1,1) specifications for the contemporaneous regressions and the vector autoregressive specification for the dynamic (causal) relationship. The contemporaneous specifications show that there is no significant relationship between stock returns and trading volume. Neither of the two variables significantly influences the other. Furthermore, the dynamic specification shows a causality running from stock returns to trading volume but the reverse is not true. For the period covered by the study, the results invalidate both the mixture of distribution hypothesis and the sequential information arrival hypothesis and open the way for other considerations such as behavioral models.

New volatility evolution model after extreme events

In this paper, we propose a new dynamical model to study the two-stage volatility evolution of stock market index after extreme events, and find that the volatility after extreme events follows a stretched exponential decay in the initial stage and becomes a power law decay at later times by using high frequency minute data. Empirical study of the evolutionary behaviors of volatility after endogenous and exogenous events further demonstrates the descriptive power of our new model. To further explore the underlying mechanisms of volatility evolution, we introduce the sequential arrival of information hypothesis (SAIH) and the mixture of distribution hypothesis (MDH) to test the two-stage assumption, and find that investors transform from the uninformed state to the informed state in the first stage and informed investors subsequently dominate in the second stage. The testing results offer a supporting explanation for the validity of our new model and the fitted values of relevant parameters.

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.

Does Trading Volume explain the Information Flow of Crude Palm Oil Futures Returns?

This study examines the role of trading volume in the crude palm oil (CPO)futures market as a proxy for information áow from the perspective of the mixture-of-distributions hypothesis (MDH). Using the data from January 2000 to April 2017, a sym-metric GARCH model has been estimated, in which the residuals follow alternatively thenormal Student-t and generalised error distribution. An alternative augmented model thatconsists of trading volume as an exogenous variable is estimated with the same error dis-tributions. Our results suggest several conclusions: First, the trading volume could not actas a true proxy for information áow. This indicates that volume of futures trading containsrelatively less price-sensitive information. Secondly, the inclusion of trading volume into theconditional variance equation with Student-t distributed errors is important for modellingpurposes when the returns are leptokurtic and positively skewed. Hence, it can be concludedthat the use of return and trading volume will enhance the current information set usedby practitioners and analysts in pricing the CPO futures contract when there exists a highdegree of leptokurtosis in the returns. This is the Örst study that validates the MDH in thecontext of the CPO futures market

The Impact of COVID-19, Day-of-the-Week Effect, and Information Flows on Bitcoin’s Return and Volatility

Past literatures have not studied the impact of real-world events or information on the return and volatility of virtual currencies, particularly on the COVID-19 event, day-of-the-week effect, daily high-low price spreads and information flow rate. The study uses the ARMA-GARCH model to capture Bitcoin's return and conditional volatility, and explores the impact of information flow rate on conditional volatility in the Bitcoin market based on the Mixture Distribution Hypothesis (Clark, 1973). There were 3,064 samples collected during the period from 1^st of January 2012 to 20^th April, 2020. Empirical results show that in the Bitcoin market, a daily high-low price spread has a significant inverse relationship for daily return, and information flow rate has a significant positive relationship for condition volatility. The study supports a significant negative relationship between information asymmetry and daily return, and there is a significant positive relationship between daily trading volume and condition volatility. When Bitcoin trades on Saturday & Sunday, there is a significant reverse relationship for conditional volatility and there exists a day-of-the-week volatility effect. Under the impact of COVID-19 event, Bitcoin's condition volatility has increased significantly, indicating the risk of price changes. Finally, the Bitcoin's return has no impact on COVID-19 events and holidays (Saturday & Sunday).

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.

A Test of Sufficient Condition for Infinite-Step Granger Noncausality in Infinite Order Vector Autoregressive Process

This paper derives a sufficient condition for noncausality at all forecast horizons (infinitestep noncausality). We propose a test procedure for this sufficient condition. Our procedure presents two main advantages. First, our infinite-step Granger causality analysis is conducted in a more general framework with respect to the procedures proposed in literature. Second, it involves only linear restrictions under the null, that can be tested by using standard F statistics. A simulation study shows that the proposed procedure has reasonable size and good power. Typically, one thousand or more observations are required to ensure that the test procedures perform reasonably well. These are typical sample sizes for financial time series applications. Here, we give a first example of possible applications by considering the Mixture Distribution Hypothesis in the Foreign Exchange Market.

Does volume really matter? A risk management perspective using cross‐country evidence

AbstractThis paper examines the impact of volume on conditional volatility and value at risk (VaR) in the context of mixture of distribution hypothesis (MDH). We test whether the support for or against the hypothesis is unconditional and holds true universally irrespective of the time period under study, the stock market under study, and the distributional assumptions so made on the residuals of the returns. We find that the persistence in volatility shows negligible reduction in all the indices across subperiods, thus refuting the claims of the MDH: that volume can explain the heteroscedasticity of returns. However, we do find that volume can act as a proxy for information post the sub‐prime financial crisis, and it does impact VaR as the estimates improve significantly for some of these indices, which exhibit a strong correlation between volume and volatility.

Trading volume and return volatility of Bitcoin market: evidence for the sequential information arrival hypothesis

This paper gives the first empirical evidence on the relationships between trading volume and return volatility of the Bitcoin denominated in fifteen foreign currencies by investigating two competing hypotheses, i.e., mixture of distribution hypothesis (MDH) and sequential information arrival hypothesis (SIAH). Allowing for both linear and nonlinear correlation and causality tests, the empirical results mainly show that: first, trading volume and return volatility are negatively correlated, implying a lack of support for the MDH; second, we document significant lead–lag relationships between trading volume and return volatility, which support the SIAH; third, the results are robust to alternative measurements of trading volume, data source and sub-period analysis. Generally speaking, these findings have practical implications for investors, who are interested in investing in Bitcoin market.

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.

News and return volatility of Chinese bank stocks

Using the comprehensive RavenPack Dow Jones News Analytics (DJNA) database that captures firm-specific news releases and their sentiment scores at high frequencies, we examine the contemporaneous correlation as well as the lead-lag relation between news and return volatility of major commercial banks listed on the Chinese stock market. Contrary to the Sequential Information Arrival Hypothesis (SIAH), most of the Chinese bank stocks do not exhibit significant lead-lag relations between news and volatility. However, there is substantial evidence that news is strongly correlated with return volatility in all the stocks, consistent with the Mixture of Distributions Hypothesis (MDH). Further analysis based on news sentiment scores suggests that positive news arrivals influence return volatility more strongly, compared with negative news. In addition, there is some evidence indicating that news arrivals contribute to the persistence in return volatility.

Do Trading Volume and Volatility Relationship Differs in Conventional and Islamic Stocks Evidence from Boursa Kuwait

In this paper, we present the results of a study on the effect of daily trading volume on the persistence of time-varying conditional volatility for Boursa Kuwait (BK). The test results were mixed. As the mixture-of-distribution hypothesis (MDH) implies, the inclusion of a contemporaneous volume in the equation of conditional variance reduces the persistent volatility of most stock series. However, the trading volume did not contribute to the removal of the ARCH or GARCH effects. The negative contemporaneous trading volume coefficient in a third of the cases provided evidence running counter to MDH predictions. Furthermore, lagged trading volumes appeared to display the predictable power of asset variability, which further buttresses and bolsters the sequential information arrival hypothesis. Moreover, we found that in more than a third of the series, the sign of the coefficients of contemporaneous and lagged trading volumes was negative, which contradicts the extensive evidence. However, the results do not lend support to the hypothesis of the effect of national cultural dimensions, market development and the level of transparency on the strength of the volume–volatility relation. The findings also show no significant difference between conventional and Islamic stocks on the relation between volume and volatility.

Cryptocurrencies from the perspective of euro investors: a re-examination of diversification benefits and a new day-of-the-week effect

We examine diversification benefits of several cryptocurrencies between 11 August 2015 and 7 August 2018 using mean–variance spanning tests. Among the eight cryptocurrencies under study, we find that all single cryptocurrencies besides one as well as a combination thereof yield significant diversification benefits when being added to a well-diversified benchmark portfolio. However, the improvement solely stems from an increase in portfolio returns, not a reduction of risk. Along with that distinction, we find that the overall beneficial impact of cryptocurrencies only holds for bullish market phases throughout our observation period, but vanishes completely throughout the price collapse of cryptocurrencies in 2018. Furthermore, we model daily differences in the returns and volatility of cryptocurrencies with an EGARCH model. Throughout the observation period considered, returns of all eight cryptocurrencies on Sundays are significantly lower than those on other days. A similar but less distinctive pattern is also observable for their conditional variance. One possible reason for the negative Sunday effect is the lower trading volume observed on Sundays, in connection with the assumption of a causal relationship between trading volume and asset returns and volatility as suggested by the mixture of distributions hypothesis.

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.