THE EMPIRICAL RELATIONSHIP BETWEEN TRADING VOLUME,RETURNS AND VOLATILITY

This paper presents an empirical analysis of the relationship between trading volume, returns and volatility in the Australian stock market. The initial analysis centres upon the volume-price change relationship. The relationship between trading volume and returns, irrespective of the direction of the price change, is significant across three alternative measures of daily trading volume for the aggregate market. This finding also provides basic support for a positive relationship between trading volume and volatility. Furthermore, evidence is found supporting the hypothesis that the volume-price change slope for negative returns is smaller than the slope for non-negative returns, thereby supporting an asymmetric relationship which is hypothesised to exist because of differential costs of taking long and short positions. Analysis at the individual stock level shows weaker support for the relationship. A second related hypothesis is tested in which the formation of returns is conditional upon information arrival which similarly affects trading volume. The hypothesis is tested by using the US overnight return to proxy for expected “news” and trading volume to proxy for news arrival during the day. The results show a reduction in the significance and magnitude of persistence in volatility and hence are consistent with explaining non-normality in returns (and ARCH effects) through the rate of arrival of information. The findings in this paper help explain how returns are generated and have implications for inferring return behaviour from trading volume data.     

Intraday trading volume and international spillover effects

The objective of this paper is to explore whether lagged trading activity in one market contributes to the return and volatility process in other markets, using 5-min concurrent data from German and British equity market. Our results lend support to our initial premise that if international investors have access to the same information set as domestic traders, then after observing foreign trading activity, market makers adjust prices to reflect their expectation of the security value, conditional upon all available information, including prior trades. Our findings clearly indicate that intraday trading volume contains predictive power for cross-border return and volatility processes. Moreover, these volume effects are found to be asymmetric in the sense that the impact of positive volume changes upon foreign stock market volatility is greater than is the impact of negative changes.     

Trading activity and bid–ask spreads of individual equity options

We empirically examine the impact of trading activities on the liquidity of individual equity options measured by the proportional bid–ask spread. There are three main findings. First, the option return volatility, defined as the option price elasticity times the stock return volatility, has a much higher power in explaining the spread variations than the commonly considered liquidity determinants such as the stock return volatility and option trading volume. Second, after controlling for all the liquidity determinants, we find a maturity-substitution effect due to expiration cycles. When medium-term options (60–90 days maturity) are not available, traders use short-term options as substitutes whose higher volume leads to a smaller bid–ask spread or better liquidity. Third, we also find a moneyness-substitution effect induced by the stock return volatility. When the stock return volatility goes up, trading shifts from in-the-money options to out-of-the-money options, causing the latter’s spread to narrow.     

The dynamic relationship of volatility,volume, and market depth in currency futures markets

This study examines the dynamic interactions among return volatilities, volume, and market depth for five currency futures markets. We use vector autoregressive analysis (VAR) to identify not only the nature of these relations but also the direction and speed of the information flow between variables. We find that return volatility is subject to strong reversal effects from trading volume and market depth. The results also indicate that the volatility appears to have predictive power on volume but not on market depth. Furthermore, this study finds that volume and depth are not endogenously determined, as their lead–lag relationship is asymmetrical. We also observe an increasing trend of integration between offshore and domestic information that affects the movement of currency futures prices.     

Intraday volume and volatility relations with and without public news

This paper reexamines the dynamic relation between intraday trading volume and return volatility of large and small NYSE stocks in two partitioned samples, with and without identifiable public news. We argue that the sequential information arrival hypothesis (SIAH) can be tested only in periods containing public news. After partitioning the sample into periods with and without public news, we find bi-directional Granger-causality between volume and volatility in the presence of public information as hypothesized by the SIAH. Our analysis further suggests that return volatility is higher in the periods with public news, while trading volume is significantly higher in the no-news period; perhaps owing to the importance of private information for trading stocks. Using the sample without public news, we find evidence that volume Granger-causes volatility without feedback. These results are broadly consistent with behavioral models like the overconfidence and biased self-attribution model of [Daniel, K., Hirshleifer, D., Subrahmanyam, A., 1998. Investor psychology and security market under- and over-reactions. Journal of Finance 53, 1839–1885]. It appears that overconfident investors overrate the precision of their private news signals and therefore trade too aggressively in the absence of public news; when public news arrives, investors’ biased self-attribution triggers excessive return volatility.     

The Informational Role of Option Trading Volume in Equity Index Options Markets

This paper examines the dynamic relations between future price volatility of the S&P 500 index and trading volume of S&P 500 options to explore the informational role of option volume in predicting the price volatility. The future volatility of the index is approximated alternatively by implied volatility and by EGARCH volatility. Using a simultaneous equation model to capture the volume-volatility relations, the paper finds that strong contemporaneous feedbacks exist between the future price volatility and the trading volume of call and put options. Previous option volumes have a strong predictive ability with respect to the future price volatility. Similarly, lagged changes in volatility have a significant predictive power for option volume. Although the volume-volatility relations for individual volatility and volume terms are somewhat different under the two volatility measures, the results on the predictive ability of volume (volatility) for volatility (volume) are broadly similar between the implied and EGARCH volatilities. These findings support the hypothesis that both the information- and hedge-related trading explain most of the trading volume of equity index options.     

Futures Trading Activity and Commodity Cash Price Volatility

Abstract:   This paper examines the lead‐lag relationship between futures trading activity (volume and open interest) and cash price volatility for major agricultural commodities. Granger causality tests and generalized forecast error variance decompositions show that an unexpected increase in futures trading volume unidirectionally causes an increase in cash price volatility for most commodities. Likewise, there is a weak causal feedback between open interest and cash price volatility. These findings are generally consistent with the destabilizing effect of futures trading on agricultural commodity markets.     

Economic linkages across commodity futures: Hedging and trading implications

We investigate cross-market trading dynamics in futures contracts written on seemingly unrelated commodities that are consumed by a common industry. On the Tokyo Commodity Exchange, we find such evidence in natural rubber (NR), palladium (PA) and gasoline (GA) futures markets. The automobile industry is responsible for more than 50% of global demand for each of these commodities. VAR estimation reveals short-run cross-market interaction between NR and GA, and from NR to PA. Cross-market influence exerted by PA is felt in longer dynamics, with PA volatility (volume) affecting NR (GA) volume (volatility). Our findings are robust to lag-specification, volatility measure, and consistent with full BEKK-GARCH estimation results. Further analysis, which benchmarks against silver futures market, TOCOM index and TOPIX transportation index, confirms that our results are driven by a common industry exposure, and not a commodity market factor. A simple trading rule that incorporates short-run GA and long-run PA dynamics to predict NR return yields positive economic profit. Our study offers new insights into how commodity and equity markets relate at an industry level, and implications for multi-commodity hedging.     

The economic consequences of increased disclosure: Evidence from international cross-listings

We examine market behavior around earnings announcements to understand the consequences of the increased disclosure that non-U.S. firms face when listing shares in the U.S. We find that absolute return and volume reactions to earnings announcements typically increase significantly once a company cross-lists in the U.S. Furthermore, these increases are greatest for firms from developed countries and for firms that pursue over-the-counter listings or private placements, which do not have stringent disclosure requirements. Additional tests support the hypothesis that it is changes in the individual firm's disclosure environment, rather than changes in its market liquidity, ownership, or trading venue, that explain our findings.     

Does Trading Volume Contain Information to Predict Stock Returns? Evidence from China's Stock Markets

This paper examines empirical contemporaneous and causal relationships between trading volume, stock returns and return volatility in China's four stock exchanges and across these markets. We find that trading volume does not Granger-cause stock market returns on each of the markets. As for the cross-market causal relationship in China's stock markets, there is evidence of a feedback relationship in returns between Shanghai A and Shenzhen B stocks, and between Shanghai B and Shenzhen B stocks. Shanghai B return helps predict the return of Shenzhen A stocks. Shanghai A volume Granger-causes return of Shenzhen B. Shenzhen B volume helps predict the return of Shanghai B stocks. This paper also investigates the causal relationship among these three variables between China's stock markets and the US stock market and between China and Hong Kong. We find that US return helps predict returns of Shanghai A and Shanghai B stocks. US and Hong Kong volumes do not Granger-cause either return or volatility in China's stock markets. In short, information contained in returns, volatility, and volume from financial markets in the US and Hong Kong has very weak predictive power for Chinese financial market variables.     

Samuelson Hypothesis & Indian Commodity Derivatives Market

Samuelson (1965) devised that futures price volatility increases as the futures contract approaches its expiration. The relation amid the volatility and time to maturity has significant inference for hedging strategies. Interestingly, so far the empirical evidence in favor of the Samuelson Hypothesis (maturity effect) is mixed in various markets. Considering no significant work to examine the relationship is so far carried out in commodity derivative markets of India, this paper ordeal the Samuelson Hypothesis on 8 commodities traded on Multi-Commodity Exchange (MCX), India. We have examined the issue by applying different regression techniques to test the hypothesis for 8 commodities (Aluminium, Nickel, Copper, Gold, Silver, Natural Gas, Crude Oil and Wheat) using inter-day data on MCX India. In order to test the Samuelson’s hypothesis, tests have been conducted using a series of GARCH, EGARCH and TGARCH models by including trading volume, open interest and time-to-maturity in the conditional variance equation. From our results, it is concluded that Samuelson’s hypothesis does not hold true for majority of commodity contracts considered. Our results also find that volatility series depend on the trading volume, compared to the time-to-maturity or open interest. As Samuelson hypothesis does not hold true for majority of commodity contracts, traders in Indian commodity derivative markets should not bias their decisions solely based on the time-to-maturity, but should also consider trading volume and open interest as they are an important determinant of price volatility. They should also consider the possibility of leverage effect while predicting future price volatilities, and the associated margin requirements.     

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.     

MACROECONOMIC NEWS,STOCK TURNOVER,AND VOLATILITY CLUSTERING IN DAILY STOCK RETURNS

We study volatility clustering in daily stock returns at both the index and firm levels from 1985 to 2000. We find that the relation between today's index return shock and the next period's volatility decreases when important macroeconomic news is released today and increases with the shock in today's stock market turnover. Collectively, our results suggest that volatility clustering tends to be stronger when there is more uncertainty and disperse beliefs about the market's information signal. Our findings also contribute to a better understanding of the joint dynamics of stock returns and trading volume.     

Information and volatility links in the foreign exchange market

We apply the trading model of Fleming et al (1998 ). to a number of currency markets. The model posits that two markets can have common volatility structures as a result of receiving common information and from cross‐hedging activity where a position in one currency is used to hedge risk in a position taken in another. Our results imply that the model is effective in identifying common information flows and volatility spillovers in the currency markets and that some of these effects are lost when simply examining raw correlations. A series of specification tests of the 21 bivariate systems that are examined provides support for the trading model in the foreign exchange context.     

The Determinants of Conditional Autocorrelation in Stock Returns

We investigate whether return volatility, trading volume, return asymmetry, business cycles, and day‐of‐the‐week are potential determinants of conditional autocorrelation in stock returns. Our primary focus is on the role of feedback trading and the interplay of return volatility. We present empirical evidence using conditional autocorrelation estimates generated from multivariate generalized autoregressive conditional heteroskedasticity (M‐GARCH) models for individual U.S. stock and index data. In addition to return volatility, we find that trading volume and market returns are important in explaining the time‐varying patterns of return autocorrelation.     

VOLATILITY FORECASTS,TRADING VOLUME,AND THE ARCH VERSUS OPTION‐IMPLIED VOLATILITY TRADE‐OFF

We investigate empirically the role of trading volume (1) in predicting the relative informativeness of volatility forecasts produced by autoregressive conditional heteroskedasticity (ARCH) models versus the volatility forecasts derived from option prices, and (2) in improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are explored. We find that if trading volume was low during period t?1 relative to the recent past, ARCH is at least as important as options for forecasting future stock market volatility. Conversely, if volume was high during period t?1 relative to the recent past, option‐implied volatility is much more important than ARCH for forecasting future volatility. Considering relative trading volume as a proxy for changes in the set of information available to investors, our findings reveal an important switching role for trading volume between a volatility forecast that reflects relatively stale information (the historical ARCH estimate) and the option‐implied forward‐looking estimate.     

Long memory in volatility and trading volume

We use fractionally-integrated time-series models to investigate the joint dynamics of equity trading volume and volatility. Bollerslev and Jubinski (1999) show that volume and volatility have a similar degree of fractional integration, and they argue that this evidence supports a long-run view of the mixture-of-distributions hypothesis. We examine this issue using more precise volatility estimates obtained using high-frequency returns (i.e., realized volatilities). Our results indicate that volume and volatility both display long memory, but we can reject the hypothesis that the two series share a common order of fractional integration for a fifth of the firms in our sample. Moreover, we find a strong correlation between the innovations to volume and volatility, which suggests that trading volume can be used to obtain more precise estimates of daily volatility for cases in which high-frequency returns are unavailable.     

Volume and Volatility in Foreign Currency Futures Markets

In this paper we propose and test several hypotheses concerning time series properties of trading volume, price, short and long-term relationships between price and volume and the determinants of trading volume in forcign currency futures. The nearby contracts for British Pound, Canadian Dollar, Japanese Yen, German Mark and Swiss Franc are analyzed in three frequencies i.e. daily, weekly and monthly.We find supportive evidence for all the five currencies that the price volatility is a determinant of the trading volume changes. Furthermore, the volatility of the price process is a determinant of the unexpected component of the changes in trading volume. Also, there is a significant relationship between the volatility of price and the volatility of trading volume changes for three of the five currencies in the daily frequency and for one currency in the monthly frequency.     

An Empirical Examination of the Return Volatility-Volume Relation in Related Markets: The Case of Stock and Options

This paper empirically examines the impact of option trading on the relation between daily stock return volatility and stock trading volume. For a sample of firms for which options were newly listed on the CBOE from 1982 to 1985, the empirical evidence indicates that there is a structural shift in the relation after option trading is introduced. Also, the findings show that daily stock return volatility is significantly and positively correlated with contemporaneous option volume, but not one-day lagged option volume. These results suggest that contemporaneous option volume may be an important variable in modelling daily stock return volatility and heteroskedasticity.     

Asymmetric Volume‐Return Relation and Concentrated Trading in LIFFE Futures

This study demonstrates that intraday volume and return on LIFFE interest rate and currency futures exhibit an asymmetric volume‐return relationship characterised by significantly larger volume associated with negative returns than with non‐negative returns. This finding is unlike the stylised asymmetric relation often observed in equity markets, where the volume on price rise is larger than the volume on price decline. The asymmetric relationship in LIFFE futures is also found to be dynamic as the direction of asymmetry can reverse during the day. It has been argued in the past that a costly short sale restriction that requires a higher transaction cost on a short position than on a long position is responsible for the asymmetric effect in equity markets. Since such a restriction is absent in futures markets, they should not exhibit any asymmetric volume behaviour. Based on the results of this research, the costly short sale hypothesis is rejected. An alternative explanation of the asymmetric relation observed in futures is presented based on recent information models that take into consideration asymmetrically‐informed traders, their dispersion of beliefs, quality and quantity of the information signal, and how the traders process it. The paper also confirms a strong U‐shape trading pattern in 15‐minute volume, but no such pattern is identified in intraday returns.