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The Volume-Synchronized Probability of Informed Trading (VPIN) metric is proposed by Easley et al. (2011, 2012) (Journal of Portfolio Management, 37:118–128; Review of Financial Studies, 25:1457–1493) as a real-time measure of order flow toxicity in an electronic trading market. This study examines the performance of VPIN around inventory announcements and price jumps in crude oil and natural gas futures markets with a sample period from January 2009 to May 2015. We obtain several interesting results: (i) VPIN increases significantly around inventory announcements with price jumps as well as at jumps not associated with any scheduled announcements. (ii) VPIN does not peak prior to the events but shortly after. (iii) A minor variation of VPIN based on exponential smoothing significantly improves the early warning signal property of VPIN, and this estimate of toxicity returns faster to the pre-event level. © 2017 Wiley Periodicals, Inc. Jrl Fut Mark

This paper applies nonparametric methods to identify jumps in daily futures prices and intraday jumps surrounding inventory announcements of crude oil, heating oil and natural gas contracts traded on the New York Mercantile Exchange. The sample period of our intraday data covers January 1990 to January 2008. We have obtained several interesting empirical results. (1) Large volatility days are often associated with large jump components, and large jump components are often associated with the Energy Information Administration's inventory announcement dates. (2) The volatility jump component is less persistent than the continuous sample path component. (3) Volatility and trading volume are higher on days with a jump at the inventory announcement than on days without a jump at the announcement. Furthermore, the intraday volatility returns to normal faster following inventory announcements with jumps than after announcements without jumps.

We apply nonparametric statistical procedures to extract jumps around scheduled macroeconomic news in U.S. Treasury bond, U.S. Treasury note and Eurodollar futures prices from 2001 to 2004. Volatility and trading activity during announcement days with jumps versus no jumps are also analyzed with computerized trade reconstruction (CTR) and time and sales high frequency data. Several interesting results are obtained. First, while jumps often occur during announcement periods, many jumps cannot be associated with macroeconomic news releases. Second, volatility and trading volume are higher during announcement days with jumps than announcement days without jumps. Furthermore, volatility returns to the pre-announcement level faster following scheduled news releases with jumps than after announcements without jumps. Third, we find that price and trading volume are adjusting simultaneously in the first one-minute interval following the announcement. Thus our results do not confirm that there exists a twostage adjustment process for prices and trading volume in interest rate futures following scheduled public news releases.

This paper employs a unique data set to investigate the total price, liquidity and information effects of large institutional trades versus individual trades on three futures contracts traded on the Taiwan Futures Exchange. Several interesting results are obtained. We find that, for the entire sample period, most buyer-initiated large trades have larger permanent price effects than seller-initiated large trades and vice versa for liquidity effects. However, we find that the permanent price effects of large sells are larger than the effects of large purchases in bearish markets and the reverse pattern is found for bullish markets. These results are consistent with the current economic condition hypothesis which is used to explain the asymmetry between total price impacts, information and liquidity effects of large buys and sells. Our new empirical results demonstrate that the asymmetric patterns between price impacts of large buys and sells hold for individual traders as well as for institutional traders.

This study examines the effects of large trades executed by outside customer on the prices of futures contracts traded on the Chicago Mercantile Exchange. We find that, on average, large buyer-initiated trades have a larger permanent price impact (information effect) than large seller-initiated trades, whereas the opposite is found for the temporary price impact (liquidity effects) of large trades. These results are consistent with previous findings for block and institutional trades in equity markets. However, we also find that the information effects of large sells are larger than large buys in bearish markets, whereas the results are the reverse in bullish markets. The liquidity price effects of buys are larger than the liquidity price effects of sells in bearish markets whereas the reverse results hold in bullish markets. Our results are consistent with the hypothesis that the current economic condition is a key determinant of asymmetric price effects between large buys and large sells. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:1147–1181, 2008

For over a hundred years, diffusion differential equations have been used to model the changes in asset prices. Despite obvious fundamental problems with these equations, such as the requirement of continuity, they often provide adequate local fits to the observed asset price process. There are, however, several aspects of the empirical process that are not fit by simple diffusion equations.

This paper applies a nonparametric method based on realized and bipower variations calculated from intraday data to identify jumps in daily futures prices of crude oil, heating oil and natural gas contracts traded on the New York Mercantile Exchange. The sample period of our intraday data covers January 1990 to January 2008. We have obtained several interesting empirical results. (1) Large volatility days are often associated with large jump components and large jump components are often associated with the Energy Information Administration’s inventory announcement dates and other important news related to energy markets. (2) The volatility jump component is less persistent than the continuous sample path component. (3) Volatility and trading volume are higher on days with a jump at the inventory announcement than on days without a jump at the announcement. Furthermore, the intraday volatility returns to normal faster following inventory announcements with jumps than after announcements without jumps.

This paper examines the relationship between daily price volatility and trading activity one year before and after a change in the size of selected futures contracts. The following three contracts are included in this study: the Stock Price Index traded on the Sydney Futures Exchange (SFE), which had a contract split on October 11, 1993; the FTSE-100 index traded on the London International Financial Futures Exchange (LIFFE), which had a contract split on March 23, 1998; and the 90-Day Bank Acceptance Bill (BAB) traded on the SFE, which had a reverse split on May 1, 1995. We obtain several interesting empirical results. We observe that there is a positive relationship between daily price volatility and the number of trades (trading frequency) before and after a change in the size of the examined futures contracts. We find that the increase (decrease) in total trading frequency has the power to explain the increase (decrease) of daily price volatility after a contract split (reverse split). Most of the average trade size variable has an immaterial impact on price volatility. Decomposing the total trading frequencies into four trade size classes, we find that the trading frequencies for small and large trade size categories are highly significant in explaining changes in daily price volatility after the index futures contracts' splits. These results are consistent with the noise trading hypothesis (Black (1986)) and the hypothesis on less informed trading in index futures markets. For the BAB case, we find that the trading frequencies for small, medium and large sizes impact price volatility before and after the reverse contract split.

A rapid growth of available geospatial data requires development of systems capable of autonomous data retrieval, integration and validation. Mobile agent technology may provide a suitable framework for developing such systems since this technology can deal, in a natural way, with the distributed heterogeneous nature of the data. We evaluate our novel multi-agent architecture for geospatial data integration and compare its performance with a client/server and a single-agent architecture. We analyze the performance alteration for various numbers of participating nodes, amount of database accesses, processing loads, and network loads.