Volume-volatility Relationship Research Articles

Asymmetric information, volatility components and the volume–volatility relationship for the CAC40 stocks

This paper investigates the relationship between trading volume components and various realized volatility measures for the CAC40 index constituents. A mixture-of-distribution model is used to decompose trading volume into informed and liquidity components. Realized volatility is broken down into continuous volatility and jumps. Our findings confirm the strong positive contemporaneous relationship between total trading volume and volatility when realized volatility and its continuous component are considered. A limited evidence of the effect of total trading volume on discontinuous volatility is found. The positive volume–volatility relationship is mainly driven by the informed component of trading volume. Conversely, liquidity volume is negatively related to realized volatility lending some support to the view that liquidity trading dampens the volatility of stock returns. A stronger negative relationship between liquidity volume and volatility jump is uncovered.

Measuring the liquidity part of volume

Based on the concept that the presence of liquidity frictions can increase the daily traded volume, we develop an extended version of the mixture of distribution hypothesis model (MDH) along the lines of Tauchen and Pitts (1983) to measure the liquidity portion of volume. Our approach relies on a structural definition of liquidity frictions arising from the theoretical framework of Grossman and Miller (1988), which explains how liquidity shocks affect the way in which information is incorporated into daily trading characteristics. In addition, we propose an econometric setup exploiting the volatility–volume relationship to filter the liquidity portion of volume and infer the presence of liquidity frictions using daily data. Finally, based on FTSE 100 stocks, we show that the extended MDH model proposed here outperforms that of Andersen (1996) and that the liquidity frictions are priced in the cross-section of stock returns.

Exchange-traded funds, liquidity and volatility

Given the exponential growth in exchange-traded fund (ETF) trading, ETFs have become a significant factor in the volatility generating process of their largest component stocks. A simple model of trading is developed for securities that are included in ETFs, and empirical support is provided for the model hypotheses. Volatility spillovers from ETFs to their largest component stocks are economically significant. These spillovers are increasing in liquidity, the proportion of each stock held by the fund, deviations from net asset value, ETF flow of funds and ETF market capitalization. The results are consistent with a positive volume–volatility relation and trading-based explanations of volatility, and are generally stronger for smaller stocks.

The Information Content of Stock Market Flows: Evidence from Thailand

This paper studies the impact of Thai stock market (SET) by examining the return-volume relation and the weekly volatility-volume relation, started 2003–2014. The results show that there is a positive relation in return-volume relation; although, there is a negative relation of volatility-volume. In addition, we differentiate the impact of fund inflow and outflow for each individual groups, including foreign, local and institution investors for finding the relations between fund flow and market return, between fund flow and market volatility. The analysis shows that only foreign investor impacts to stock market in both of trading-return relation and trading-volatility relation. There is a positive relation with market return, but has a negative relation with market volatility. However, it depends on direction of fund flow. Fund inflow has positive relation in market return while outflow has negatively. On the other hand, in part of trading-market volatility, the larger of cash outflow, the more volatility is the market.

Trading volume, realized volatility and jumps in the Australian stock market

We study the volume–volatility relation by splitting volume into the number of trades and the average trade size at individual and institutional level, and realized volatility into its continuous and jump components. We find that the number of trades is the most important variable driving realized volatility. The number of trades by the individual investors carries more explanatory power in explaining volatility compared to the number of trades by institutional investors. The average trade size cannot be entirely disregarded, though; its importance compared to the number of trades is limited. Order imbalance plays a minimal role in driving volatility.

A microstructure analysis of the carbon finance market

The European Union Emissions Trading Scheme is the key policy instrument of the European Commission's Climate Change Program aimed at reducing greenhouse gas emissions to 8% below 1990 levels by 2012. The key asset traded under the scheme is the European Union allowance (EUA). This article examines ultra high frequency data to assess the extent of the development in the futures market of the EU Emissions Trading Scheme. Our results indicate significant developments consistent with sequential information arrival. They also indicate a negative contemporaneous relationship between volume and volatility for all contracts. The implication is that liquidity traders dominate any role played by informed traders. Incorporating the duration between trades in our analysis has significant impact suggesting that any empirical investigation of the intra-day volume–volatility relationship needs to actively account for the impact of time elapse between trades.

An Empirical Investigation of Asymmetric Volatility, Trading Volume and Risk-Return Relationship in the Indian Stock Market

The objective of this article is to investigate the volatility asymmetry, volatility-volume relationship by considering trading volume as a mixing variable, and the risk-return relationship in the Indian stock market. Daily data from January 2, 1997 to May 30, 2013 for SP provide supports to MDH but the volatility shocks are found to be highly persistent even after incorporating trading volume. The study also finds evidence of no significant relationship between risk and return. The implication of the findings may be applicable to traders, speculator as well as the financial decision makers and practitioners as the trading volume reflects the information about market expectation.

Long memory and tail dependence in trading volume and volatility

During the last decades a wide literature has focused on the relationship volumevolatility on financial markets. This paper investigates the temporal dynamics of volatility and volumes, supposing, as in Bollerslev and Jubinski (1999), that the link has to be found in their long-run dependencies, that are supposed to be driven by the same informative process. We analyze the volume-volatility relationship using IBM stocks data. In particular, we rely on the realized volatility based on five minutes stock prices. Tail dependence analysis is carried out with two alternative estimators of the continuous part of the volatility process. The analysis shows that log-realized volatility and logvolumes are characterized by upper and lower tail dependence, where the positive tail dependence is mainly due to the jump component. We also investigate the possibility that volumes and volatility are driven by a common fractionally integrated stochastic trend, i.e. they have the same degree of long memory and are fractionally cointegrated as the Mixture Distribution Hypotesis prescribes. Moreover, we estimate a bivariate FIVAR specification that explicitly considers the long run relationship between the two series and the tail dependence in the shocks, by parameterizing the joint density by means of different copula functions. The evidence from the model estimates, the simulation results and the forecasts comparison with HAR model highlight the ability of the bivariate FIVAR with copula density specification to account for the common long memory pattern and tail dependence.

Impact of Foreign and Domestic Order Imbalances on Return and Volatility-Volume Relation in the Indonesian Stock Market

Impact of Foreign and Domestic Order Imbalances on Return and Volatility-Volume Relation in the Indonesian Stock Market

Exchange Traded Funds, Liquidity, and Market Volatility

We propose a simple model of trading based on the Kyle (1985) framework for securities that are included in Exchange Traded Funds (ETFs). The model postulates that trading in ETFs will increase volatility in their component stocks, and that volatility spillovers will be increasing in liquidity and the relative proportion of each stock held by the fund. An empirical analysis of trading in the S&P 500 SPDR and three heavily traded industry ETFs confirms these hypotheses, using both Amihud’s (2002) measure of illiquidity as well as stock turnover as proxies for liquidity. The results are consistent with a positive volume-volatility relation as well as tradingbased explanations of volatility. The findings are relevant to market practitioners, regulators and investors in these increasingly popular products, since ETFs may in fact contribute to volatility in their underlying component stocks, and thus to the stock market in general.

On the volatility–volume relationship in energy futures markets using intraday data

This paper investigates the relationship between trading volume and price volatility in the crude oil and natural gas futures markets when using high-frequency data. By regressing various realized volatility measures (with/without jumps) on trading volume and trading frequency, our results feature a contemporaneous and largely positive relationship. Furthermore, we test whether the volatility–volume relationship is symmetric for energy futures by considering positive and negative realized semivariances. We show that (i) an asymm etric volatility–volume relationship indeed exists, (ii) trading volume and trading frequency significantly affect negative and positive realized semivariance, and (iii) the information content of negative realized semivariance is higher than for positive realized semivariance.

No news is not good news: evidence from the intra-day return volatility–volume relationship in Shanghai Stock Exchange

Through this research, we find that the asymmetric volatility phenomenon is reversed in the Shanghai Stock Exchange during bull markets. That is, volatility increases more with good news than with bad news. This evidence is inconsistent with the US markets. Further examination of this phenomenon reveals that the positive impact of good news on volatility is driven by the return-chasing behaviour of investors during bull markets. We also find that volatility increases after stock price declines in bear markets. After controlling for liquidity shifts, we observe similar patterns in volatility in both bull and bear markets. We posit that institutional and behavioural factors are the major driving forces of observed volatility patterns in the Chinese stock market.

Trading Volume, Realized Volatility and Jumps in the Australian Stock Market

We study the volume-volatility relation by splitting volume into the number of trades and the average trade size at individual and institutional level, and realized volatility into its continuous and jump components. We find that the number of trades is the most important variable driving realized volatility. The number of trades by the individual investors carries more explanatory power in explaining volatility compared to the number of trades by institutional investors. The average trade size cannot be entirely disregarded, though; its importance compared to the number of trades is limited. Order imbalance plays a minimal role in driving volatility.

Evolution of Volatility, Trade Price Location, Correlations, and Speed of Trading in the Limit Order Book

We investigate the information content of the limit order book (LOB) on the Tokyo Stock Exchange, the world’s second largest order-driven market. We find that high frequency microstructure parameters, such as the current cost-to-trade 1% of average daily volume and LOB slope, contain information about future trade price location, price volatility, speed of trading, return autocorrelation and cross-correlation. We also document that the average trade size in LOB is the driving force in the standard volume–volatility relationship, which is contrary to previous findings for other markets. These results are helpful in understanding the price discovery process in a purely order driven market and have potential applications in academics and industry for optimizing the order submission strategies.

Intraday trading activities and volatility in round-the-clock futures markets

In this paper we examine the relationship between intraday return volatility and volume of trading for Japanese yen futures, euro FX futures, and E-mini S&P 500 futures traded on a 24-hour GLOBEX trading system in six time zones. The results support the mixture-of-distribution hypothesis (MDH), which endorses a significant contemporaneous relationship between volume and volatility, and the sequential-arrival-of-information hypothesis (SAIH), which advocates significant lagged volatility–volume relations. The net effect of trading number is positive, supporting the dispersed belief hypothesis, while the net effect of trading imbalance is negative, supporting the asymmetrical information hypothesis. Our results suggest that the four theories of volume–volatility relations are complementary, not competing. In addition, the largest effect of the trading imbalance on volatility is found during American regular trading hours, rather than the home asset market of the futures contracts, thus supporting the trading place bias.

Order size, order imbalance and the volatility–volume relation in a bull versus a bear market

AbstractThis paper revisits the volume–volatility relationship documented in Chan and Fong (2000) in a bull versus a bear market by using data from the period surrounding the subprime crisis in 2008. The relationship is predicted to be asymmetric because of the greater cost of short positions, the lack of liquidity and the differing trading strategies adopted in a bearish market. Our analysis shows the trading patterns in the bull and bear markets are different. Particularly, in a falling market characterised by higher volatility and lower liquidity, the frequency of order placement has doubled but orders are smaller in size. This provides evidence of investors’ shift of trading strategy in relation to the status change of the market. Furthermore, we find evidence to support the proposition that volume is more informative in a bear market than in a bull market.

What drives the volume–volatility relationship on Euronext Paris?

The goal of this paper is to shed light on the relationship between volume and volatility. More specifically, it aims to determine which component of trading volume (trade size or number of transactions) drives this relation. Our intraday analysis reveals several results. Firstly, we confirm the strong positive relationship between volume and volatility. Secondly, including volume in the conditional variance of stock returns significantly reduces the persistence of volatility. Thirdly, we show that the well-known positive relationship between volatility and volume is generated by the number of trades. These results are robust, even after controlling for the impact of the intraday patterns. Finally, our findings are available for the CAC40 Index as well as for individual stocks.

The Predictive Power of Limit Order Book for Future Volatility, Trade Price, and Speed of Trading

We investigate the information content of the limit order book (LOB) on the Tokyo Stock Exchange, the world’s second largest order-driven exchange1. Microstructure parameters, such as the current cost-to-trade 1% of average daily volume and order book slope, consistently and significantly predict future price volatility, trade prices, and speed of trading. The shape of the LOB on the bid side carries more predictive power than that on the ask side. We also document that the average trade size is the driving force in the standard volume-volatility relationship.

Do regulatory policies affect the flow of information in emerging markets?

In a previous paper we established that volatility is best explained by contemporaneous rather than lagged trading volume in the Egyptian stock exchange (EGX). The main objective of this paper is to investigate the effects of regulatory policies – namely the switch from price limit to circuit breaker – on the dynamic relationship between trading volume and stock returns volatility in the EGX. Using daily returns data for 20 actively traded companies as well as the EGX30 market index, the Generalised Method of Moments (GMM), results show that the volume–volatility relationship is not only endogenous but is also structurally altered by the switch.

A Microstructure Analysis of the Carbon Finance Market

The European Union Emissions Trading Scheme is the key policy instrument of the European Commission's Climate Change Program aimed at reducing greenhouse gas emissions to eight percent below 1990 levels by 2012. The key asset traded under the scheme is the European Union allowance (EUA). This article examines ultra high frequency data to assess the extent of the development in the futures market of the EU Emissions Trading Scheme. Our results indicate significant developments consistent with sequential information arrival. They also indicate a negative contemporaneous relationship between volume and volatility for all contracts. The implication is that liquidity traders dominate any role played by informed traders. Incorporating the duration between trades in our analysis has significant impact suggesting that any empirical investigation of the intra-day volume-volatility relationship needs to actively account for the impact of time elapse between trades.