The impact of transaction duration,volume and direction on price dynamics and volatility

We explore the role of trade volume, trade direction, and the duration between trades in explaining price dynamics and volatility using an Asymmetric Autoregressive Conditional Duration model applied to intraday transactions data. Our results suggest that volume, direction and duration are important determinants of price dynamics, while duration is also an important determinant of volatility. However, the impact of volume and direction on volatility is marginal after controlling for duration, and the impact of volume on volatility appears to be confined to periods of infrequent trading.     

Time and dynamic volume–volatility relation

This paper examines volume and volatility dynamics by accounting for market activity measured by the time duration between two consecutive transactions. A time-consistent vector autoregressive (VAR) model is employed to test the dynamic relationship between return volatility and trades using intraday irregularly spaced transaction data. The model is used to identify the informed and uninformed components of return volatility and to estimate the speed of price adjustment to new information. It is found that volatility and volume are persistent and highly correlated with past volatility and volume. The time duration between trades has a negative effect on the volatility response to trades and correlation between trades. Consistent with microstructure theory, shorter time duration between trades implies higher probability of news arrival and higher volatility. Furthermore, bid–ask spreads are serially dependent and strongly affected by the informed trading and inventory costs.     

Forecasting daily exchange rate volatility using intraday returns

This study investigates whether intraday returns contain important information for forecasting daily volatility. Whereas in the existing literature volatility models for daily returns are improved by including intraday information such as the daily high and low, volume, the number of trades, and intraday returns, here the volatility of intraday returns is explicitly modelled. Daily volatility forecasts are constructed from multiple volatility forecasts for intraday intervals. It is shown for the DEM/USD and the YEN/USD exchange rates that this results in superior forecasts for daily volatility.     

Return Volatility,Trading Imbalance and the Information Content of Volume

In this paper, we examine the relationship between volume and return volatility using the transaction data. We introduce transaction and volume imbalance measures to capture the information content of trades. These two information measures are shown to have a strong explanatory power for return volatility and contain incremental information about the asset values over and above that conveyed by the size and frequency of trades. Also, return volatility is significantly correlated with the percentage of trading volume taking place at NYSE. This result suggests that NYSE trades are more informative and contribute more to price discovery. There is evidence that price discovery concentrates in more heavily traded stocks, particularly the Dow Jones Stocks. Finally, return volatility is found to be persistent at the intraday level. The persistence level is higher for less frequently traded stocks. Return volatility also exhibits temporal variations. In particular, return volatility is significantly higher in the opening half-hour for less frequently traded stocks. Thus, stocks with different frequencies of trades may follow different volatility processes.     

Dynamic Relationship among Intraday Realized Volatility,Volume and Number of Trades

In this paper, the vector autoregressive model is fitted to find out the causal relationship among realized volatility, the number of transactions and volume with the intraday time intervals of 10, 20 and 30 min. To understand the impact of shock to the market on specific variables, a multivariate Impulse Response Function analysis is also introduced to visualize the causal relationship among the variables. From the analysis of a stock listed on the Stock Exchange of Hong Kong, we find that realized volatility reacts positively to the lagged average trade size. However, the realized volatility forms a negative relationship with the first few lagged number of trades. As a result, the intraday causal relationship among realized volatility, volume and the number of trades is quite different from that obtained on a daily basis. The findings of this paper can enhance the understanding of how the number of trades and the average trade size per transaction affect the risk evolution of financial securities and thus improve the risk management of day trading strategies.     

Trade Duration: Information and Trade Disposition

We examine the relation between futures trade duration and profitability, volatility, and volume. The duration of unprofitable trades is longer than that for profitable trades across the day, which is evidence of the disposition effect. Our analysis of profitable and unprofitable trades shows strong intraday volume patterns. Greater proportions of profitable trades are offset at the open and close. During high‐volume periods dealers may use a semi‐fundamental informational advantage, based on their access to order flow signals. Dealers may be able to execute costly inventory‐reducing trades at the end of the day, when their informational advantage is perhaps greatest.     

Intraday periodicity, calendar and announcement effects in Euro exchange rate volatility

This paper provides an analysis of intraday volatility using 5-min returns for Euro-Dollar, Euro-Sterling and Euro-Yen exchange rates, and therefore a new market setting. This includes a comparison of the performance of the Fourier flexible form (FFF) intraday volatility filter with an alternative cubic spline approach in the modelling of high frequency exchange rate volatility. Analysis of various potential calendar effects and seasonal chronological changes reveals that although such effects cause deviations from the average intraday volatility pattern, these intraday timing effects are in many cases only marginally statistically significant and are insignificant in economic terms. Results for the cubic spline approach imply that significant macroeconomic announcement effects are larger and far more quickly absorbed into exchange rates than is suggested by the FFF model, and underscores the advantage of the cubic spline in permitting the periodicity in intraday volatility to be more closely identified. Further analysis of macroeconomic announcement effects on volatility by country of origin (including the US, Eurozone, UK, Germany, France and Japan) reveals that the predominant reactions occur in response to US macroeconomic news, but that Eurozone, German and UK announcements also cause significant volatility reactions. Furthermore, Eurozone announcements are found to impact significantly upon volatility in the pre-announcement period.     

Robust estimation of intraweek periodicity in volatility and jump detection

Opening, lunch and closing of financial markets induce a periodic component in the volatility of high-frequency returns. We show that price jumps cause a large bias in the classical periodicity estimators and propose robust alternatives. We find that accounting for periodicity greatly improves the accuracy of intraday jump detection methods. It increases the power to detect the relatively small jumps occurring at times for which volatility is periodically low and reduces the number of spurious jump detections at times of periodically high volatility. We use the series of detected jumps to estimate robustly the long memory parameter of the squared EUR/USD, GBP/USD and YEN/USD returns.     

Entry, exit and trading profits: A look at the trading strategies of a proprietary trading team

We examine the behavior of a 15 strong proprietary stock trading team and show how consistent intraday trading profits were generated. The team, who worked for a large US direct access trading firm, executed over 96 thousand trades in 3 months in 2000. Profitable intraday trading occurred in an anonymous dealer capacity, on both long and short positions, especially when volume and price volatility were higher. The traders rapidly entered long (short) positions when the number of dealers and size become greater on the bid (offer) side of the spread. Profits were taken early against the trend.     

Information arrivals and intraday exchange rate volatility

This paper investigates the link between information arrivals and intraday DEM/$ volatility. Information arrivals are measured by the numbers of news items that appeared in the Reuters News Service. We separate news stories into different categories and find that total headline news counts, US and German macroeconomic news and German Bundesbank monetary policy news all have a significant impact on intraday DEM/$ volatility. The larger quantitative effects of the German Bundesbank monetary policy news and US macroeconomic news at 15-min intervals are consistent with the findings of a two-stage adjustment process of public information arrivals [Fleming and Remolona, J. Finance (1999) 1901]. Our results suggest that the persistent of intraday exchange rate volatility set off by public information is extended by traders’ private information about 15 min later. The conclusions are obtained from ARCH models that incorporate intraday seasonal volatility terms.     

Weather and intraday patterns in stock returns and trading activity

We examine the relation between weather in New York City and intraday returns and trading patterns of NYSE stocks. While stock returns are found to be generally lower on cloudier days, cloud cover has a significant influence on stock returns only at the market open. There are significantly more seller-initiated trades when there is more cloud cover at the market open, which is consistent with the return results. Cloudy skies are associated with higher volatility and less market depth over the entire trading day. Finally, cloud cover is not significantly correlated with spread measures and turnover ratios. The findings overall suggest that weather has a significant influence on investors’ intraday trading behavior.     

Realized volatility and transactions

This paper re-examines the impact of number of trades, trade size and order imbalance on daily stock returns volatility. In contrast to prior studies, we estimate daily volatility using realized volatility obtained by summing up intraday squared returns. Consistent with the theory of quadratic variation, realized volatility estimates are shown to be less noisy than standard volatility measures such as absolute returns used in previous studies. In general, our results confirm [Jones, C.M., Kaul, G., Lipson, M.L., 1994. Transactions, volume, and volatility. Review of Financial Studies 7, 631–651] that number of trades is the dominant factor behind the volume–volatility relation. Neither trade size nor order imbalance adds significantly more explanatory power to realized volatility beyond number of trades. This finding is robust to different time periods, firm sizes and regression specifications. The implications of our results for microstructure theory are discussed.     

Asymmetric information about volatility: How does it affect implied volatility,option prices and market liquidity?

This paper develops a model of asymmetric information in which an investor has information regarding the future volatility of the price process of an asset and trades an option on the asset. The model relates the level and curvature of the smile in implied volatilities as well as mispricing by the Black-Scholes model to net options order flows (to the market maker). It is found that an increase in net options order flows (to the market maker) increases the level of implied volatilities and results in greater mispricing by the Black-Scholes model, besides impacting the curvature of the smile. The liquidity of the option market is found to be decreasing in the amount of uncertainty about future volatility that is consistent with existing evidence. This revised version was published online in June 2006 with corrections to the Cover Date.     

Intraday risk management in International stock markets: A conditional EVT approach

The study compares the predictive ability of various models in estimating intraday Value-at-Risk (VaR) and Expected Shortfall (ES) using high frequency share price index data from sixteen different countries across the world for a period of seven and half months from September 20, 2013 to May 07, 2014. The main emphasis of the study has been given to Extreme Value Theory (EVT) and to evaluate how well Conditional EVT model performs in modeling tails of distributions and in estimating and forecasting intraday VaR and ES measures. We have followed McNeil and Frey's (2000) two stage approach called Conditional EVT to estimate dynamic intraday VaR and ES. We have compared the accuracy of Conditional EVT approach to intraday VaR and ES estimation with other competing models. The best performing model is found to be the Conditional EVT in estimating both the quantiles for the entire sample. The study is useful for market participants (such as intraday traders and market makers) involved in frequent intraday trading in such equity markets.     

INTRADAY STEALTH TRADING: WHICH TRADES MOVE PRICES DURING PERIODS OF HIGH VOLUME?

Research documents a U-shaped intraday pattern of returns. We examine which trade sizes drive the U-shaped pattern and find that intraday price changes from larger trades exhibit a U-shaped pattern whereas price changes from smaller trades show a reverse U-shaped pattern. We argue that price changes from smaller trades are higher during the middle of the day because informed investors break up their trades to disguise their information when intraday volume is low. Price changes from larger trades are likely higher at the beginning and end of the day because high volume allows informed investors to increase their trade size without revealing their information to the market.     

Circuit Breakers,Trading Collars,and Volatility Transmission Across Markets: Evidence from NYSE Rule 80A

The NYSE's Rule 80A attempted to delink the futures and equity markets by limiting index arbitrage trades in the same direction as the last trade to reduce stock market volatility. Rule 80A leads to a small but statistically significant decline in intraday U.S. equity market volatility. In addition, the results are asymmetric: volatility is dampened more in a rising market than in a declining one. These results suggest that, to a limited extent, rule restrictions on trading can sufficiently delink the futures and equity markets enough to reduce the transmission of volatility.     

On the intraday periodicity duration adjustment of high-frequency data

In the last decade, intensive studies on modeling high frequency financial data at the transaction level have been conducted. In the analysis of high-frequency duration data, it is often the first step to remove the intraday periodicity. Currently the most popular adjustment procedure is the cubic spline procedure proposed by Engle and Russell (1998). In this article, we first carry out a simulation study and show that the performance of the cubic spline procedure is not entirely satisfactory. Then we define periodicity point processes rigorously and prove a time change theorem. A new intraday periodic adjustment procedure is then proposed and its effectiveness is demonstrated in the simulation example. The new approach is easy to implement and well supported by the point process theory. It provides an attractive alternative to the cubic spline procedure.     

Long-lived information and intraday patterns

This paper studies the effect of clustering of liquidity trades on intraday patterns of volatility and market depth when private information is long-lived. The assumption of long-lived information allows us to distinguish between the patterns of information arrival and information use. Our results are: (i) volatility follows the same pattern as liquidity trading, (ii) there are no systematic patterns in the price impacts of orders, and (iii) the timing of information arrival is unimportant. Result (i) is the same as that obtained by Admati and Pfleiderer (1988) in a model of short-lived private information, but (ii) and (iii) are different.     

Trading Patterns and Prices in the Interbank Foreign Exchange Market

The behavior of quote arrivals and bid-ask spreads is examined for continuously recorded deutsche mark-dollar exchange rate data over time, across locations, and by market participants. A pattern in the intraday spread and intensity of market activity over time is uncovered and related to theories of trading patterns. Models for the conditional mean and variance of returns and bid-ask spreads indicate volatility clustering at high frequencies. The proposition that trading intensity has an independent effect on returns volatility is rejected, but holds for spread volatility. Conditional returns volatility is increasing in the size of the spread.     

Exchange rate volatility,macroeconomic announcements and the choice of intraday periodicity filtering method

Filtering out the intraday periodicity of volatility is crucial for using high frequency data in econometric analysis. This paper studies the effects of filtering on statistical inference as regards the impact of news on exchange rate volatility. The properties of different methods are studied using a five-minute frequency EUR/USD data set and simulated returns. The simulation results suggest that all the methods tend to produce downward-biased estimates of news coefficients, some more biased than others. The study supports the Flexible Fourier Form method as the best for seasonality filtering.