The Impact of Off-Exchange Customer Trades on Prices ... - CiteSeerX

January 1999

The Impact of Off-Exchange Customer Trades on Prices: Evidence from the Futures Markets

by Michael F. Ferguson* and Steven C. Mann**

*Department of Finance, Kelley School of Business, Indiana University, 1309 E. 10th Street, Bloomington, Indiana 47405; (812) 855-2698; e-mail: [email protected]. **M.J. Neeley School of Business, Texas Christian University, Box 298530, Fort Worth, Texas 76129; (817) 257-7569; fax: (817) 257-7227; e-mail: [email protected]. We would like to thank Kerry Back, Chris Barry, Hank Bessembinder, Corrine Bronfman, Doug Foster, Norman Mains, and Steve Manaster for their comments and advice on an earlier version of this paper. Mann acknowledges the support of the Charles Tandy American Enterprise Center.

The Impact of Off-Exchange Customer Trades on Prices: Evidence from the Futures Markets Abstract We provide evidence that, in the futures markets, off-exchange customers’ trades with locals have a larger impact on prices than other types of trades. Furthermore, it appears that locals’ trades are at least as strongly related to volatility as customers’ trades. This evidence is derived from a detailed examination of generally proprietary CFTC data that can be partitioned more finely(by trader type and trade direction) than other, publicly available, data sets. We directly estimate depth (price impact per unit net customer order flow), document its intraday properties, and refine previous volume/volatility studies. Although we find that intraday depth is relatively stable, we find that the depth of a market is generally related to its underlying economic fundamentals.

I. Introduction Are all trades equal? That is, do trades by different groups of market participants have differing impacts on market prices? For example, does a trade by a market maker with another market maker have the same effect on prices as a market maker trading with a non-market making customer?

This paper provides evidence that in the futures markets off-exchange customers’

trades with locals have a larger impact on prices than other types of trades. This paper is part of a growing literature that examines the relative influence of market makers’, customers’, and hedgers’ trades on price dynamics. Ito, Lyons, and Melvin (1998) argue that trading by foreign exchange market makers in Tokyo increases volatility because their trades reflect private information about “semi-fundamental” information (information about dealers’ risk aversion, inventory, etc.). Hau (1998) develops a model in which market makers’ trades are a source of volatility. Recent research in the futures markets also suggests that price volatility depends on the identity of the traders. Bessembinder and Seguin (1993) speculate that hedgers’ trades (through their impact on open interest) may affect volatility more than locals’ trades. Daigler and Wiley (1998) argue that the positive volume-volatility relationship documented by Bessembinder and Seguin is driven by the trading of uninformed, off-exchange customers. One interpretation of this result is that customers are destabilizing noise traders in the sense described by DeLong, Shleifer, Summers, and Waldman (1990a, 1990b, 1991). In this paper we use CFTC data that allows us to partition transaction data more finely than previous researchers: we know both the direction of trade (buy or sell) and the identity of the counterparties. This data is used to conduct a detailed examination of the impact of off-exchange customers’ trades on prices in the futures markets. We directly estimate the impact of customers’ 1

trades on prices by regressing price changes on net customer order flow. We find that the depth of a market is generally related to its underlying economic fundamentals. We further refine this analysis to examine the relative impact of customers’ trades with locals versus trades with other market participants. Perhaps surprisingly given their prevalence in other market data, we find little evidence of intraday patterns in the price impact of customers’ trades. We also are able to associate price volatility with volume partitioned by the trade counterparties. This means that we can address questions such as are trades by customers with other customers more strongly related to volatility than trades by locals with locals or than trades by locals with customers, etc. Our analysis consistently reveals that customers’ trades with locals are the most important in the futures markets, both in terms of raw volume and price effects (impact per unit net order flow or volatility). Interestingly, locals’ trades (regardless of counterparty) appear to be at least as strongly related to volatility as customers’ trades. The impact that customer trading volume has on prices is central to numerous models of market microstructure. For example, in models based on Kyle (1985), net customer order flow (buy volume less sell volume) is often the only observable variable. Unfortunately for empirical work, net customer order flow is rarely observable because most publicly available trade data generally identifies neither trade contra parties nor buy and sell trades. More generally, offexchange customers are frequently characterized as uninformed or liquidity traders whose trades force market makers to adjust prices. These price revisions, or price impacts, are a source of price volatility. In order to assess the impact of customer trades on prices, data on transaction prices, trader identity, and trade direction (buy or sell) are required. Previous research into the relationship between price volatility and customer trading has 2

been hampered by a lack of sufficiently detailed data. In order to assess the impact of trading on prices without access to more detailed data, two approaches have been adopted. The first approach employs trade classification schemes (e.g. Lee and Ready (1991)) that compare trade prices to bid and ask quotes to infer order flow. Others, such as Lee, Mucklow, and Ready (1993) use (ex ante) quoted limit order depth to proxy (ex post) price impacts. However, in the futures pits quotes are shouted, rather than posted, and hence most quotes are not around long enough to be collected by researchers (or anyone else). Hasbrouck (1998) circumvents this problem by developing a technique based on Glosten and Harris (1988) that utilizes the Chicago Mercantile Exchange’s “volume and tick” data to estimate depth. His Markov Chain Monte Carlo estimator (the Gibbs sampler) employs transaction level data but does not require bid-ask quotes to infer trade direction. A second approach is to examine the relationship between unsigned volume and price volatility. This approach is necessarily a less precise way to capture the impact of trading on prices because it does not make use of the (noisy) information derived from (inferred) buy and sell orders. A large literature has developed in this area which is summarized in Karpoff (1987). Volume and volatility are strongly, positively related. In the future markets, Bessembinder and Seguin (1993) show that volatility is strongly related to unexpected customer volume. They relate price volatility to market depth by arguing that open interest is a proxy for depth and showing that volatility is in fact inversely related to expected open interest. Bessembinder and Seguin hypothesize that hedgers’ trades will have a greater effect on volatility than locals’ trades because locals’ trades will have a smaller effect on open interest. Daigler and Wiley (1998) further partition volume and report that customer volume–orders submitted from off the exchange 3

floor–drives the positive volume-volatility relationship. They argue that this is consistent with the notion that the public is a major source of short-term volatility and that the public is dominated by uninformed noise traders. In this paper we utilize generally proprietary Commodity Futures Trading Commission data to estimate futures market depth with directly identified customer net order flow for the Chicago Mercantile Exchange, using techniques suggested by Manaster and Mann (1996). Thus, we are able to estimate Kyle’s market depth parameter for a wide spectrum of futures contracts. We provide estimates of the number of contracts, contract notional values, and initial margins required to move prices by given units.1 Notably, our estimates are very similar in relative magnitude to estimates of the same parameters calculated by Bessembinder and Seguin (1993) and Hasbrouck (1998). The consistency of the results obtained using different data sets is encouraging on two fronts. First, it provides some additional confidence in the reasonableness of each methodology. Second, and more importantly, it indicates that for many applications the public domain data required to employ the Hasbrouck or the Bessembinder and Seguin methodology will be sufficient. For instance, the Hasbrouck technique enables researchers to examine intraday phenomena. As in Daigler and Wiley (1998), we examine the relationship between volume and volatility by partitioning the volume data by trader type. We are able to go far beyond simply identifying daily customer, local, or hedger volume. The data can be broken down by time of day

For example, we estimate that an order flow imbalance representing $516 million (contract notional value) would be required to move the Yen contract price by 1.0% while an imbalance of $294 million would be required to move the Yen contract price by $550 (the mean daily price change during our sample period). 1

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into trades by locals with locals, locals with customers, hedgers with customers, etc. Our analysis shows that customers’ trades are positively associated with volatility; but that is the case for locals and hedgers, too. The relationship is strongest for customers’ trades with locals; but, all types of trades by locals are associated with higher volatility. We cannot conclude that trading by offexchange customers is the primary or strongest source of volatility in the futures markets. We use our sample to be the first to examine the intraday properties of the depth parameter. The futures markets provide a particularly interesting setting for analysis of intraday depth, as the commodities have different fundamental information structures. Currency markets decline in activity and volatility after the London close (Harvey and Huang (1991), Anderson and Bollarslev (1998)) which occurs at midmorning Chicago time. The interest rate and currency contracts begin trading at 7:20 am Chicago time, an opening which causes these markets to be active at the time of major economic announcements. We analyze intraday depth, controlling for what Ederington and Lee (1995) label “major” announcements.2 Our intraday analysis yields two important results. First, depth is generally stable throughout the day. The lack of a general intraday pattern in depth is in sharp contrast to volatility patterns (Harvey and Huang, (1993) and spread patterns reported by Laux and Senchak (1992) and Ferguson and Mann (1998)). The lack of consistently detectable general intraday variation in depth contrasts with predictions of Admati and Pfleiderer (1989), but is consistent with models analyzed by Back and Pedersen (1998). Second, while the evidence is weak, depth appears to vary with the underlying market information structure. Depth appears to decrease

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Ederington and Lee (1995) report that PPI, CPI, and employment were the most important announcements for their sample. (In their sample, the merchandise trade deficit announcement was more important than the CPI for the Deutsche mark.)

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during periods surrounding major announcements, and increase for currencies as volatility declines throughout the day. The paper is structured as follows. Section II describes the CFTC data and methodology employed. The depth estimates are reported in Section III. Section IV provides economic interpretation of the magnitude of the depth estimates. Section V examines the relationship between volatility and volume sorted on counterparty. Section VI contains the intraday analysis. Section VII concludes.

II. Data and Methodology A. Data The data source for this study is the Commodity Futures Trading Commission (CFTC). We utilize two data sets: publicly available Time and Sales (TS) data and a transaction data set based on trade records that are generally not in the public domain. The TS data consist of all recorded price changes, and are used by Harvey and Huang (1991) and Smith and Whaley (1994), among others.3 The proprietary data are Computerized Trade Reconstruction (CTR) transaction data. All data are from the Chicago Mercantile Exchange (CME) for the first six months of 1992. The CTR transaction data are sometimes used to track every trade by individual traders, providing an “audit trail”. The accuracy of the data is such that it is frequently used in enforcement actions. Because the data contains information about traders that could be used to infer inter alia their proprietary trading strategies it is not generally available to the public. However, it is occasionally made available for academic studies, and has been used by Fishman and Longstaff (1992),

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Time and Sales data also includes some non-transaction bid and ask prices.

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Manaster and Mann (1996), Chang and Locke (1996) and Locke and Venkatesh (1997).

For

this study, we use data for the fourteen largest commodity pits of the CME.4 The CFTC requires all contract markets to maintain a complete record of each trade and to submit these records to the Commission monthly. The trading cards submitted by the floor traders, known as “locals”, are the original source of these records. Each trade is recorded twice, once by the buyer and once by the seller. The record details the commodity and delivery month, the quantity, the price, and the date and time.5 Both sides of the trade identify the executing traders and the trade direction (whether the trade was a purchase or a sale). At the end of the day's trading, traders submit the cards to the exchange clearing house for settlement and reconciliation. The CTR data are particularly useful for estimating price response per unit of net customer order flow because in addition to identifying the executing traders, the data indicates trade direction (i.e., whether the trade was a buy or a sell for each floor trader), and whether the trade is for the floor trader’s personal account (CTI1), for the trader’s clearing member (CTI2), for another floor trader (CTI3), or for an outside customer (CTI4).6 For example, if a commercial clearing member’s floor trader executes a trade for the firm, it will be a CTI2 trade, but trades for

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In addition to the fourteen most active pits, we also computed statistics for the Australian Dollar and the S&P Midcap 400 Index contracts. The results for the contracts are similar to those reported here. We omit them due to very low trading volume in 1992 for these markets. 5

Floor traders record only the fifteen-minute time bracket for each trade on the personal trade records, or cards, that they submit at the end of the day. Dual recording of each trade is used in conjunction with the trade sequence on each trader's card and the Time and Sales record by a computer algorithm to define each trade time to the nearest minute. Despite the possibility of some degree of ambiguity about certain trade timing, the exchanges and the CFTC use these records for legal investigations into trading activity. 6

Not all exchange members are clearinghouse members. Floor traders that are not clearinghouse members must maintain margin accounts with clearing members in order to clear trades.

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other member’s accounts will be CTI3 trades. Trades executed on behalf of other members (CTI3) are most often trades done for hedging purposes. For example, locals making markets in futures options will typically delta hedge their option exposure by having a compatriot in the futures pit execute a trade on their behalf (via runner or visual means). CTI2 trades are executed for the account of large commercial or institutional members, and are typically trades executed as part of a larger portfolio strategy. Generally, CTI2 and CTI3 trades are done for hedging purposes. For expositional purposes we label CTI2 and CTI3 trades as trades by hedging contra parties. [Table 1: Volume Decomposed by Counterparty Combination] Table 1 reports the proportion of trades in each pit for each of the 10 possible CTI counterparty combinations. The most frequent CTI combination is a customer order (CTI4) filled by a market maker (CTI1) (ranging from 34-74% of pit volume). Thus, when floor brokers execute trades for customers, the contra party to the trade is most often a local, or market maker, trading for personal account. However, customer trades also execute frequently (8-32% of pit volume) against trades by other customers, as well as against trades by hedging contra parties (CTI 2 and 3). The proportion of trade involving customers on at least one side of the trade is greater than the proportion of trades involving locals in 11 of the 14 markets (S&P 500, Eurodollars, and Pork Bellies are the exceptions). This is primarily due to the fact that customer trades are more likely to cross than locals are to trade with each other (S&P 500 and Eurodollars are the exceptions). Hedger’s trades are about equally likely to be with customers as with locals.

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B. Estimating Depth We estimate simple linear models, expanding on the specification introduced by Manaster and Mann (1996). The models are based on Kyle’s (1985) model of competitive auction markets with Bertrand (price) competition. Futures trading floors probably come as close to meeting that description as any existing market. As modeled by Kyle, depth is simply the inverse of the price response, λ , to customer order flow, where customer order flow, ω, is signed net trade volume (buys less sells):

pt − pt −1 = λ ω t + et

(1)

Estimation of (1) requires observable customer order flow, which can only be inferred with most available databases. Given that typical methods of inferring trade direction for equities (e.g. Lee and Ready (1991)) use price changes to assign trade direction, model (1) is probably not very useful for equity research with currently available data. Fortunately, the futures data provide clear information on net customer trading via the CTI indicator and the buy/sell designation. Therefore we estimate depth with regressions based on (1); larger estimates of λ indicate less depth than do smaller values. We focus our depth estimation on the daily most active contract, which is usually, but not always, the nearby (or nearest delivery month) contract.

C. Calculating Order Flow The CTR transaction data directly identifies customer trades and indicates whether the trades are buys or sells. We compute customer net order flow in a particular contract for a given period (t) by defining ωt as total customer buy volume less total customer sell volume for the

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period. For example, if customers buy 100 contracts and sell 50, then net customer order flow is positive 50 contracts. We compute order flow using the most active contract; price changes are defined using the most active contract as well (see below). We estimate depth using consecutive five-minute intervals throughout the trading day (for example, the first interval for the S&P 500 contract begins at 8:30 a.m., ending at 8:34 a.m.). Not all customer trades execute against locals, or market makers. If customers trade against each other, then they offset and do not impact the order flow calculation. However, some proportion of customer volume (see Table 1) executes against hedging contra parties (CTI2 and CTI3). Bessembinder and Seguin (1993) suggest that trades against hedgers may affect prices more than trades with locals. In order to isolate customer price impacts associated with trading against market makers, we distinguish between customers executing trades against locals (CTI1) and customers executing trades against hedging contra parties (CTI2 and CTI3) by defining an alternative specification of net order flow that divides net customer order flow into two components. We designate: ωlocal t = net customer order flow executed against local (CTI 1) counter parties ; and ωhedger t = net customer order flow executed against CTI 2 and CTI 3 counter parties. With the above order flow specification we are able to compare price impacts of customer trades executed against locals to the price impacts of customer trades executed against hedging contra parties. We report estimated depth using both specifications of order flow (aggregate and disaggregated).

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D. Calculating Price Changes The CTR data records transaction time to the nearest minute, but does not provide the detail needed to sequence prices within a minute. The Time and Sales (TS) data provide a sequenced series of prices that differ from previous trade prices, timed to the second. The TS data also include some non-transaction prices representing quotes, which we eliminate.7 We use transaction prices from the TS data (for the daily most active contract in each commodity) to obtain the last trade price for each interval. We calculate a Time and Sales price change, ∆pTSt , as the last price during interval t, less the last price recorded prior to period t. For the first period of the day we use the first recorded price of the day. The TS price change, ∆pTSt , has some features that may complicate inferences. One problem is that the prices used to calculate the change (the last price in the interval) may not reflect customer trades, as the price could be due to other trader type combinations, such as localto-local trades. It may also be subject to bid-ask bounce. For example, consider a case where customers predominantly sell during interval (t-1) and the last price is at the market maker’s bid. If customers buy during the next interval and the last price is at the market makers’ ask, then the price change is inflated by the bid-ask bounce. This is a particular problem for measuring intraday variations in depth, as depth estimates derived using ∆pTSt could be subject to bias due to intraday variation in spreads, which has been well documented for futures by Laux and Senchak (1992), using TS data, and Ferguson and Mann (1998), using CTR data. Since the TS price change measure suffers from possible bias due to bid-ask bounce, we

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The quotes on the TS records are not paired bids and offers, but rather somewhat sporadically recorded offers or bids that differ from trade prices.

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supplement the TS price change with an alternative price change measure designed to eliminate possible bid-ask bias. We calculate a customer one-sided price change, ∆pCt , that is defined as either the change in customer buy prices or the change in customer sell prices, conditional on the sign of customer net order flow. If customer order flow is positive (customers are net buyers for the five-minute period) then ∆pCt is the highest customer purchase price in the last minute less the lowest customer purchase price in the first minute. If customer order flow is negative, then ∆pCt is the lowest customer sale price in the last minute, less the maximum customer sale price in the first minute. Since the one-sided customer price change measure uses changes in buy prices for periods of net buying, and changes in sell prices for periods of net selling, any bias introduced by bid-ask bounce is eliminated by using the price change based on changes in price for only one trade direction. Table 2 lists correlations between the two price change measures, which range from roughly 0.4 to 0.8.

E. Summary Statistics for Order Flow and Price Changes [Table 2: Characteristics of customer trade activity ] For the empirical analysis, we use each commodity pit’s most active contract for each day. Table 2 reports mean and median total customer volume (buy volume plus sell volume) per 5minute interval as well as mean and median net customer order flow (buy volume minus sell volume for CTI4s) per 5-minute interval, all for the most active contract. These statistics provide a sense of typical net order flow imbalances compared to typical volumes. Typically, net order flow is on the magnitude of 10 to 30% of total volume. For example, median S&P 500 index contract volume is 317 contracts per 5-minute period, while the corresponding median absolute 12

net order flow is 31 contracts. Thus the median imbalance is on the order of 10% of volume. Table 2 also reports price volatility measures based on one-sided customer prices: for each five-minute period, we compute the price range and the price standard deviation for customer buy trades and for customer sell trades, then select the maximum of the buy and sell statistics for each period. As an illustration, if the range of customer sell prices is $100 and the customer buy price range is $125, the one-sided price range for the interval is $125. The table reports mean and median price ranges and standard deviations for the one-sided maximum volatility measures, in both unadjusted dollar terms and percentage terms (in basis points). Dollar volatility is highest for the S&P 500 index contract, with a median price range of $175. On the other hand, the S&P contract’s percentage volatility, at an 8.5 basis point median range, is much lower than some of the agricultural contracts, in particular the pork belly contract, with a 28.6 basis point median range. By far the lowest volatility (both dollar and percentage) is exhibited in the interest rate contracts, as the CME contracts are all short-term interest rate contracts.

III. Price Response Associated with Customer Order Flow The previous section details two alternative specifications of order flow and two specifications of price changes. Table 3 reports our estimates of four linear models, alternatively pairing one of the two order flow specifications with one of the two price change definitions. [Table 3: Depth Estimates--Price Impact of Customer Order Flow] The first model use TS price changes and aggregate customer net order flow: Model 1:

∆pTSt = α + λ1 ω t + εt.

Table 3 shows that λ1 is positive and significant for all commodities; customer order flow is 13

associated with positively with price changes. When customers are net buyers, prices are increasing. For example, net customer buying of 100 S&P 500 contracts moves the price by $27.00, slightly more than 1 tick. We provide more extensive commentary regarding economic significance and relative magnitude of the parameter estimates below, in section IV. The second model uses TS price changes and disaggregated customer order flow: Model 2:

∆pTSt = α + λ2 ωlocalt + λ3 ωhedgerτ + εt.

As in model 1, λ2 is positive and significant for all commodities. However, λ3 is significantly positive for only five of fourteen commodities, usually much smaller in magnitude, and even negative for some pits (significantly negative for four of the fourteen: Lumber, Hogs, Bellies, and Eurodollars). Clearly, customer trades against market makers move prices more than customer trades against hedgers. The third and fourth models use the one-sided customer price change specification: Model 3:

∆pCt = α + λ1 ω t + εt.

Model 4:

∆pCt = α + λ2 ω localt + λ3 ωhedgert + εt.

Models 3 and 4 are designed to more clearly associate price changes with order flow by selecting the price change (i.e. the change in customer buy or sell prices) conditional on whether customers are buying or selling. Thus if customers are net buyers, we associate that period of buying with the change in customer buy prices. The latter two models provide additional precision by first restricting the price changes to customer trades only, and then eliminating bid-ask bounces by using one-sided price changes. The results of our estimations of models 3 and 4 are notable in the overall much larger estimates of price impacts associated with customer order flow, and thus less estimated depth. 14

As in models 3 and 4, customer trades against market makers move prices more than trades with hedgers. In 13 of 14 markets (Feeder Cattle is the exception), the parameter estimates are greater for locals than for hedgers. Of the former, 13 are significantly positive while only 9 of the latter are. Bessembinder and Seguin (1993) suggest that trades with hedgers will have a greater impact on price than trades with locals. They base this conjecture on the likely effects of these trades on their proxy for depth, open interest. The results of the four regressions presented in this section clearly indicate that customer trades are associated with significant price changes; but, these impacts are greatest when the trades are executed against locals trading for their own accounts. The difference is likely due to the difference between an intermediate proxy for depth-open interest–and direct estimates of price impacts.

IV. Economic Interpretation of Depth Estimates Given the depth estimates presented in the previous section we can estimate the order flow imbalances required to move prices by given amounts. We will use the estimates to compare our depth estimates to those of Bessembinder and Seguin (1993) and Hasbrouck (1998).

IV.A Order Flows Required to Move Prices We use the estimated depth coefficients (lambdas) from models 1 and 3 (reported in Table 3), where order flow is measured as net customer CTI 4 order flow; model 1 uses ∆pTS ; model 3 uses ∆pC. To facilitate comparisons with results provided by Hasbrouck (1998), and those provided by Bessembinder and Seguin (1993), we estimate the order flow required to move prices 15

by a basis point, as well as the order flow required to move prices by the mean daily absolute change, which we designate δµ. [Table 4: Economic Interpretation of Depth Estimates] The fundamental relationships between price changes and order flow that we estimate are based on the Kyle (1985) model: ∆p = λω. In order to provide economic intuition for the depth estimates, we invert the Kyle model to solve for ω(λ,φ) , the order flow imbalance required to move prices a given amount, φ, where we choose φ to be either 0.01% ( a basis point) or δµ. For example, for the S&P 500 Index contract, λ1 = 0.270 (estimated with TS price changes) and λ1 = 1.055 (estimated with customer price changes). The mean price of the most active daily contract during the sample period was $205,654; therefore one basis point of the sample mean price is $20.56 (a little less than the $25 minimum tick). Our estimate, based on TS price changes, of the customer contract imbalance required to move prices by one basis point is the solution ω(λ1,0.01%) to : $20.56 = 0.270 ω , so that ω(λ1,0.01%) = 76 contracts. Alternatively, our estimate based on one-sided customer price changes is the solution ω(λ4,0.01%) to : $20.56 = 1.055 ω , so that ω(λ4,0.01%) = 19 contracts. To aid intuition, we also convert estimated contract imbalances into dollar figures representing the notional value of the contracts required to move prices a given amount, which we designate as $N(λ1,φ)). We define $N(λ1,φ) = ω(λ1,φ) φ. For example, we estimate the S&P 500 index notional dollar depth measure $N(λ1, 0.01%)) to be $15.7 million (76 x $205,654); one interpretation is that net customer purchases representing $15.7 million would move the index by one basis point.

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IV.B Comparisons with other techniques Hasbrouck (1998) estimates depth for three CME contracts (the S&P 500 index, the mark, and pork bellies). Bessembinder and Seguin (1993) also provide depth estimates for three CME contracts (mark, yen and T-bills). The only contract common to both samples is the mark. Therefore, while we do compare estimated depths for all three techniques for the mark (albeit for different time periods), our comparisons are useful primarily for purposes of relative magnitudes, rather than absolute differences in contract depth estimates. First we examine depth estimates for the one common contract, the mark. Bessembinder and Seguin estimate that a net order flow imbalance of 173 contracts would be required to move prices by one basis point.8 In marked contrast, Hasbrouck estimates that ten contracts would move mark prices by 0.7 basis points, so that roughly 14 contracts would move mark prices by a basis point. As Table 3 shows, our results are in the middle. We estimate that a 67 contract order imbalance would move the mark price by one basis point, using the TS price change, or that 36 contracts move prices a basis point, using the customer price change. Thus, for the mark, our estimates indicate less depth than Bessembinder and Seguin, but more than Hasbrouck. Some of the difference is likely due to different sample time periods (1982-1990 for BS, 1992 for this paper, and 1998 for Hasbrouck), and some variation should be expected with techniques that use different data. While the mark is the only contract common to all studies, it may not be a serendipitous

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Bessembinder and Seguin report that capital of $988.38 million is required to move the nearby futures price by 1.0%; dividing estimated capital by the average value of the mark contract for their sample period ($57,000) yields the contracts required to move the price by 1% (17,340). We then divide by 100 to determine the contracts required to move prices one basis point.

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candidate for mutual comparisons. Comparing other mutual contracts (Yen and T-bills for BS, pork bellies and the S&P 500 contract for Hasbrouck), the techniques provide depth estimates that are very similar in relative magnitude. Bessembinder and Seguin estimate that 181 contracts for the Yen, and 3400 contracts for T-bills, would be required to move contract prices by a basis point, so that the yen is about 19 times more responsive than the T-bill contract to order flow. On the other hand, our estimates (using TS price changes) are 104 contracts for the Yen and 2153 contracts for T-bills, suggesting that the yen is about 21 times as responsive. Both techniques estimate similar relative magnitude. Hasbrouck estimates that 33 S&P 500 contracts and 0.9 pork belly contracts would move prices by a basis point, so that pork bellies are about 37 times more responsive than the S&P contract to order flow. Our estimates (with TS price changes) are 76 S&P contracts and 2.8 pork belly contracts, so that bellies are about 27 times as responsive. Again, relative magnitudes are comparable. The similarity in relative magnitudes of the three techniques suggests that the Bessembinder and Seguin method and the Hasbrouck method provide relatively good estimates of market depth. Our estimates utilize precise, but proprietary, CFTC data while their techniques require only easily obtained, publicly available data. Thus, one implication of our results is that publicly available data may be used confidently by those seeking to compare relative market depths. To aid intuition in comparing estimated depths across commodities, we also provide estimates of initial margin requirements for the number of contracts required to move prices a given amount, φ, which we label $M( ω(λ,φ)). This number simply takes an estimated number of 18

contracts and multiplies by the initial margin requirements for the contract. The cross-sectional variation in ω(λ,φ), $N(λ1,φ), and $M( ω(λ,φ)) across commodities varies with the economic fundamentals of the commodities. The mean absolute daily price change for the most active Eurodollar contract is $211, which corresponds to about an 8 basis point change in the London Interbank Offered Rate. One interpretation of Table 4 is that net customer purchases representing $9.9 billion notional principal (using λ3), or $70 billion notional (using λ1) would drive short term rates down by about 8 basis points. Alternatively, the margin requirement for the contracts required to move short term rates by 8 basis points is estimated at $7 million (using λ3), or $47.5 million (using λ1). At the extreme, Table 4 shows that between $4.65 billion to $32.6 billion notional principal is needed to move the Eurodollar contract by one basis point (which translates to approximately a 4 basis point change in short-term interest rates). In contrast to the interest rate contracts, Table 4 shows that commodity price changes are associated with much smaller levels of order flow. For example, we estimate that $2 million to $4 million notional contract value would be required to move pork belly prices by 1%, and the margin required would be between $100,000 to $200,000. As a final example, depth for the S&P 500 contract is somewhere between the extremes. The model suggests that it takes between $400 million to $1.5 billion notional contract value to move the S&P by 1%; the margin requirements are between $40 to $160 million.

V.

Volume-Volatility Relationship by Trader Type In this section we consider the impact of different counterparty combinations on the well19

known, positive relationship between volume and volatility. The results in Section III strongly suggest that customer trades will have a greater association with volatility than customer trades with hedgers. Volume is defined for each possible CTI counterparty combination as the sum of buy and sell volume within a 5-minute interval for the daily most active contract and price volatility is defined as the price range for the five-minute interval for all trades of the most active daily contract.9 [Table 5: Regressions of Volatility on Volume Partitioned by Counterparty Combination] Table 5 presents the regression of volatility on each possible counterparty combination for each pit. The last two rows of the table report the number of positive and significantly positive relationships (out of 14 markets) for each trader combination. Note that there are several extremely large parameter estimates (e.g., CTI3 with CTI3 in Lumber) in the table. These estimates are for counterparty combinations that the proportions in Table 1 indicate are very rare. In general, volume and volatility are strongly, positively related for each type of trader combination. Nonetheless, some patterns do emerge from the data. Consistent with our earlier results, customer trades with locals are most strongly associated with volatility. In every pit these trades are significantly, positively related to volatility. In fact, for each type (CTI2, CTI3, and CTI4) the strongest relationship between volume and volatility exists for their trades with locals (CTI1s). For each type there are more positive and more significantly positive coefficient estimates than for their trades with locals than for trades with any other type of customer. Also consistent with our earlier results, the relationship is

9

The regressions in this section have also been run using all volume, rather than the most active contract only, and using the standard deviation as the dependent variable. The results are very similar to those presented here; therefore, we do not report them here. They are available from the authors upon request.

20

weaker for each type for trades with hedgers (CTI2 and CTI3) than for trades with either customers or locals. Further evidence that market maker trading is significantly related to volatility is that trades by locals with other locals are significantly positive more often than for any combination not involving locals. In particular, customer trades with other customers have a somewhat weaker relationship with volatility. Volume and volatility are significantly, positively related. When we consider volume by trader type, we see that customer trades with locals is the type of volume most strongly associated with volatility. However, in general it appears that trades by locals are more strongly related to volatility than customer trades. At the very least, we cannot conclude that trading by customers is the primary or strongest source of volatility in the futures markets. A particularly interesting example is the S&P 500 pit where customer trades that cross with other customer trades account for 13% of volume; yet, these trades have an insignificant effect on volatility. This finding is inconsistent with the notion advanced in Daigler and Wiley (1999) that customers represent uninformed traders that induce price volatility through their trading on relatively dispersed estimates of intrinsic value. We find that customers are no greater source of volatility than locals.

VI.

Intraday Variation in Depth It is widely documented that there are systematic intraday patterns in the volatility of

security prices and the volume of trading. Numerous papers [e.g., Admati and Pfleiderer (1988), Foster and Viswanathan (1990, 1996), Holden and Subramanyam (1992), Back and Pedersen 21

(1998), and Back, Cao, and Willard (1997)] offer explanations for these patterns that are derived from extensions of Kyle (1985). Thus, it is natural that these papers relate intraday patterns in price volatility and volume to intraday patterns in market depth (1/8, where 8 is the price response per unit of net customer order flow in equation (1)). Numerous papers have documented intraday patterns in volume and volatility. However, we are the first to directly estimate intraday patterns in market depth. We control for three types of economic announcements identified by Ederington and Lee (1995) as having a major impact on interest rate and currency futures markets: the Producer Price Index (PPI), the Consumer Price Index (CPI), and the Employment report. These announcements are made once each month for each statistic. The announcements are never on the same day; therefore, we have 18 announcement dates in the sample. To investigate patterns in price impact of customer order flow we estimate the price impact by time of day with the following model: )p

t

' " % 81*1Tt % 82*2Tt % 83*3Tt % 84*4Tt % ,t

where *i, i={1, 2, 3, 4}, are dummy variables for the time of day: *1=1 if the interval is in the first 15 minutes of the day on a day when there is an announcement and 0 otherwise; *2=1if the interval is in the first 15 minutes on a non-announcement day and 0 otherwise; *3=1 if the interval is not in the first or last 15 minutes of the day and 0 otherwise; and, *4=1 if the interval is in the last 15 minutes of the day; Tt, is net customer order flow per 5-minute interval as defined in Section II.C; and 8i, i={1, 2, 3, 4}, are the price impact coefficients. The left hand side variables are the global price change, ∆pGt, and CTI4 price change series, ∆pCt, described in Section II.D. 22

The 8 estimates represent price responses due to net customer order flow at different times of the day. For example, 81 is the estimated price impact per unit of net order flow for the opening 15 minutes of trading on announcement days, and 82 is the estimated price impact for the opening 15 minutes on days without major economic announcements. This approach allows for the following estimation technique suggested by Harvey and Huang (1991). Let the price changes be written in vector notation as y (nx1), the dummy matrix as δ (nx4), and the coefficients as λ (4x1). We estimate the coefficient vector λ by minimizing the quadratic form, bNNwb, where b=δ δN(y-δλ δλ) (4x1) are the moment conditions, and w is a weighting matrix designed to provide consistent estimation under conditions of serial correlation and heteroskedasticity (see Hansen (1982)). In this case, the minimized quadratic form is not identically zero for the unrestricted model because the estimated equation has an intercept term and non-dummy explanatory terms. The second step in the test is a re-estimation of the coefficients by minimizing a quadratic form using the same weighting matrix as in the first step and imposing a restriction that the coefficients are equal. The resulting value for the restricted minimized quadratic form has an asymptotic chi-square distribution with degrees of freedom equal to the number of restrictions. [Table 6: Intraday Patterns in the Price Impact of Customer Order Flow] Table 6 presents the estimates of the price impact per unit of net customer order flow. Recall that depth is the inverse of the price impact, so a smaller 8 estimate implies a greater market depth estimate. The most remarkable thing about the 8 estimates is how similar they are throughout the day. We cannot reject the hypothesis that the estimates are identical across the three time periods in 13 of 14 cases when using the global price changes and in 9 of 14 cases 23

when using the CTI4 price change series. The data in Table 6 suggest that in the futures markets price impact, or market depth, is relatively stable over the course of the day. As noted above, models that attempt to explain patterns in volume or volatility frequently link these patterns to patterns in market liquidity--the price impact estimated here. For example, Admati and Pfleiderer (1988) suggest that trading volume and price volatility will be greatest during periods in which market depth is also greatest because informed traders will strategically concentrate their activity in periods in which the price impact of their trades is low. Back and Pederson (1997) show that price impact will be stable throughout the day if there is a single informed trader who strategically trades throughout the day. However, Back, Cao, and Willard (1997) show that this result is sensitive to the monopolistic informed trader assumption. Our data are more consistent with the Back and Pederson (1997) prediction, but the model structure they assume may not conform as closely with trading conditions in the futures markets as models which assume several competing informed traders. [Figure 1: Intraday Price Response: 8C] Price response parameters estimated using the customer price series for the S&P 500, Mark, Eurodollar, and Live Cattle are presented in Figure 1. The estimates in the first bin are the price responses in during announcement periods. The Mark and Eurodollar estimates are significantly higher during these periods. Several other currency and interest rate contracts display similar, although less statistically significant, patterns. Recall that we have only 18 announcements in our sample. This is weak evidence for the proposition that there is less depth in these markets during announcement periods. There is also weak evidence that the currency markets are deeper at the close. The price response for the Mark contract declines significantly at 24

the close. Harvey and Huang (1991) and Ferguson, Mann, and Schneck (1998) report that foreign exchange futures volatility declines through out the U.S. trading day. Thus, it appears that the currency markets are deeper during a period of decreasing activity.

VII. Conclusions In this paper we have addressed the question: do different types of traders have different impacts on prices? In the futures markets the answer appears to be yes: trades by off-exchange customers with locals have a greater impact on prices than do other types of trades. We arrive at this conclusion after examining a data set that allows us to partition trading volume more finely (by trader type and by direction) than other researchers have been able to do. This enables us to estimate depth (price impact per unit net customer order flow) directly and refine previous volume/volatility studies. Market microstructure models based on Kyle (1985) relate changes in market prices to net customer order flow. We are the first to document the general properties of this parameter. We estimate the absolute level of market depth for 14 CME contract markets, note their relative depth, and document that there are no strong intraday patterns in the depth parameter. We find that the depth of a market is generally related to its underlying economic fundamentals. Interest rate contract markets are the deepest , followed by the currency markets, then the S&P 500, and finally the agricultural contracts. The Eurodollar market is remarkably deep. We estimate that a $3.2 Trillion dollar (notional value) order flow imbalance would be required to move the price by 1%. Of course, 1% daily moves in this market are extremely rare. At the other end of the spectrum, we estimate that a $4 million order flow imbalance would move the Pork Bellies price 25

by 1%. In this market, 1% moves are commonplace. We find that market depth is generally stable during the day. This contrasts strikingly with the intraday U-shaped patterns in execution costs (spreads) documented by Ferguson and Mann (1998) for futures markets and the general finding that market liquidity varies during the day in most markets. Strikingly, we find that the estimated price impacts are greater for customer trades against locals than for trades against hedgers. Similarly, we find that volatility is most strongly associated with trades between customers and locals. Thus, the analysis of the impact of customer trading on prices consistently tells us the same thing: Namely, customer trades with locals are the most important both in raw volume (Table 1) and price impact (depth estimate or volatility [Tables 3 and 5]). Interestingly, locals’ trades appear (regardless of counterparty) to be at least as strongly related to volatility than customers’ trades. What accounts for these price dynamics? One possible explanation is that locals are simply charging other traders for the service of providing liquidity. Many types of traders–customers, hedgers, and speculating locals--primarily demand liquidity while locals primarily provide liquidity. Trades between locals and these groups require compensation–price impacts–that we measure directly for customers (Table 3) and indirectly for all groups (Table 5). Another possible explanation is consistent with Ito, Lyons, and Melvin (1998) hypothesis that market makers have access to private information about “semi-fundamental” information regarding order flow, locals’ risk aversion, locals’ inventory, etc. It may be the case that customers (through crossing trades) and hedgers also provide liquidity while locals may do more

26

than simply provide liquidity, they may trade on their informational advantage.10 In this more complex view of the pits, the price impacts and volatility associated with locals’ trades may reflect their information being impounded in prices.

10

Manaster and Mann (1998) provide evidence that locals pursue more complex trading strategies than simply providing liquidity. Ferguson and Mann (1998) show that off-exchange customers are frequent providers of liquidity in the futures markets.

27

References Admati, Anat R., and Paul Pfleiderer, 1988, A Theory of Intraday Patterns: Volume and Price Variability, Review of Financial Studies 1, 3-40. Andersen, Torben G., and Tim Bollarslev, 1988, Deutsche Mark-Dollar Volatility: Intraday Activity Patterns, Macroeconomic Announcements, and Longer Run Dependencies, Journal of Finance 53, 219-265. Back, Kerry, and Hal Pedersen, 1998, Long-lived Information and Intraday Patterns, Journal of Financial Markets 1, 385-402. Back, Kerry, Henry Cao, and Gregory A. Willard, 1997, Imperfect Competition Among Informed Traders, working paper. Bessembinder, Hendrik, and Paul J. Seguin, 1993, Price Volatility, Trading Volume, and Market Depth: Evidence from Futures Markets, Journal of Financial and Quantitative Analysis 28, 21-39. Chang, Eric, C., and Peter R. Locke, 1996, The Performance and Market Impact of Dual Trading: CME Rule 552, Journal of Financial Intermediation 5, 23-48. Daigler, Robert and Marilyn Wiley, 1998, The Impact of trader Type on the Futures VolatilityVolume Relation, Journal of Finance, forthcoming. DeLong, J. Bradford, Andrei Shleifer, Lawrence H. Summers, and Robert J. Waldman, 1990a, Noise Trader Risk in Financial Markets, Journal of Political Economy 98, 703-739. DeLong, J. Bradford, Andrei Shleifer, Lawrence H. Summers, and Robert J. Waldman, 1990b, Positive Feedback Investment Strategies and Destabilizing Rational Speculation, Journal of Finance 45, 379-395. DeLong, J. Bradford, Andrei Shleifer, Lawrence H. Summers, and Robert J. Waldman, 1991, The Survival of Noise Traders in Financial Markets, Journal of Business 64, 1-19. Ederington, Louis H., and Jae Ha Lee, 1995, The Short-Run Dynamics of the Price Adjustment to New Information, Journal of Quantitative and Financial Analysis 30, 117-134. Ferguson, Michael F., and Steven C. Mann, 1998, Execution Costs and their Intraday Behavior in Futures Markets, working paper. Ferguson, Michael F., Steven C. Mann, and Leonard J. Schneck, 1998, Concentrated Trading in the Foreign Exchange Futures Markets: Discretionary Liquidity Trading or Market Closure? Journal of Futures Markets 18, 343-362. 28

Fishman, Michael J., and Francis A. Longstaff, 1992, Dual Trading in Futures Markets, Journal of Finance 47, 643-671. Foster, F. Douglas, and S. Viswanathan, 1990, A Theory of the Intraday Variations in Volume, Variance and Trading Costs in Securities Markets, Review of Financial Studies 3, 593624. Foster, F. Douglas, and S. Viswanathan, 1996, Strategic Trading When Agents Forecast the Forecasts of Others, Journal of Finance 51, 1437-78. Glosten, L. R., and L. E. Harris, 1988, Estimating the Components of the Bid/Ask Spread, Journal of Financial Economics 21, 123-42. Hansen, Lars, 1982, Large Sample Properties of Generalized Method of Moment Estimators, Econometrica 50, 1029-54. Harvey, Campbell R., and Roger D. Huang, 1991, Volatility in the Foreign Currency Futures Market, Review of Financial Studies 4, 53-569. Hasbrouck, Joel, 1998, Liquidity in the Futures Pits: Inferring Market Dynamics from Incomplete Data, working paper. Hau, Harald, 1998, Competitive Entry and Endogenous Risk in Foreign Exchange Market, Review of Financial Studies 11, 757-87. Holden, Craig W., and A. Subrahmanyam, 1992, Long-Lived Private Information and Imperfect Competition, Journal of Finance 47, 247-70. Ito, Takatoshi, Richard K. Lyons, and Michael T. Melvin, 1998, Is There Private Information in the FX Market? The Tokyo Experiment, Journal of Finance 53, 1111-30. Karpoff, Jonathan M., 1987, The Relation Between Price Changes and Trading Volume: A Survey, Journal of Financial and Quantitative Analysis 22, 109-126. Kyle, Albert S., 1985, Continuous Auctions and Insider Trading, Econometrica 53, 1315-1336. Laux, Paul, and A. Senchak, 1992, Bid-Ask Spreads in Financial Futures, The Journal of Futures Markets 12, 621-634. Lee, Charles M. C., Belinda Mucklow, and Mark J. Ready, 1993, Spreads, Depths, and the Impact of Earnings Information: An Intraday Analysis, Review of Financial Studies 6, 345-374.

29

Lee, Charles M. C., and Mark J. Ready, 1991, Inferring Trade Direction from Intradaily Data, Journal of Finance 46, 733-746. Locke, Peter R., and P. C. Venkatesh, 1997, Futures Market Transaction Costs, Journal of Futures Markets 17, 229-245. Manaster, Steven, and Steven C. Mann, 1996, Life in the Pits: Competitive Market Making and Inventory Control, Review of Financial Studies 9, 953-975. Manaster, Steven, and Steven C. Mann, 1998, Sources of Market making Profits: Man Does Not Live by Spread Alone, working paper. Smith, Tom, and Robert E. Whaley, 1994, Assessing the Costs of Regulation: The Case of Dual Trading, Journal of Law and Economics 37, 215-246.

30

Figure 1: Intraday Price response: λ C

1.40

1.20

1.00

0.80

0.60

0.40 0.20 0.00 ent em c n ou ann

n ope ar l u reg

y dda mi

se clo

Liv eC attl e

Eu rod oll ar

Du ets che (x1 0)

S& P5 00 Ind ex ma rk

Table 1. Volume partitioned by counterparty combination (most active daily contract) Customer (CTI 4) Volume against: CTI 1:

Commodity S&P 500

CTI 2:

CTI 3:

locals/ market makers

clearing members

floor hedgers

mean volume percent of total

541.5

35.3

24.4


349.2


190.0


115.0


168.7


31.9


730.9


33.2


32.3


143.9


47.3


66.5


21.2


15.6

53%

3%

CTI 4:

Clearing members (CTI 2) volume against: CTI 2: CTI 3: CTI 1:

other customers


clearing members

132.8

73.0

2.7

2%

13%

7%

Local hedger (CTI3) volume against: CTI 1: CTI 3:

floor hedgers


other floor hedgers

CTI 1 to CTI 1: local to 1ocal

3.4

69.2

2.0

137.8

0%

0%

7%

0%

Mean aggregate Volume (x2) 1,022.1

13%

Currency Deutsche mark Swiss Franc Pound Yen Canadian dollar

39% 47% 46% 42% 35%

137.2

23.5

15%

47.6

131.3 3%

6.5

12%

28.6

49.5 2%

6.3

11%

58.4 12.8

3.8

28.7

2.7

2.5

40.2 5.1 6%

1.2

19.7

0.5

3.8

248.9 8%

27.6 0%

0.2 4%

404.6

10%

0%

4%

1%

42.3

0.3

17.0

897.9

10%

0%

3%

1%

1%

0.5

8.0

4.5

85.5 0%

4%

1%

2%

0.8

32%

15.1

2.0

6.6

2.1 6%

1%

1%

10%

51.6 2%

1%

9%

17%

4%

4.6

22.3

67.1

14.1 2%

11%

18%

2%

14%

45.8

44.4

8.2

20.8 9%

12%

3%

15%

82.7

15%

398.8 7%

2.7

91.1

0%

3%

Interest rate Eurodollar T-Bill LIBOR

178.0

35% 34% 34%

136.8

9%

12.3

8.7

13%

17.9

173.1

7%

11.7 9%

6.8

19%

345.7

8%

14.6

12%

20.6 7%

48.1

17%

3.1

15%

10.8

22%

63.8 2%

5.2 3%

2.7

11%

187.0 3%

3.2 5%

2.4 3%

30.6

9%

0.3 3%

0.9 2%

195.4 1%

5.3

97.6

0%

0.1 1%

2,089.3 9% 5%

1.2

95.6

0%

1%

Agriculturals Live Cattle Pork Bellies Hogs Feeder Cattle Lumber

9.2

52%

1.6

60%

2.5

3.7

10.9

7.6

0.3

22.7

1.6

0.1

5.3

0.2 0%

0.0

4.6

0.2

0.1

0.4

8.0

0.1

122.6 7%

1.4 0%

0.0 2%

78.3

13%

0%

6%

0.4 0%

10.4

0.5

2.0

276.2 9%

0%

7%

0%

0.0 0%

0.1

8.4

0.0

23.9 0%

4%

0%

0%

0.0 2%

3.4

0.5

0.0

0.8 6%

0%

0%

2%

16.5 0%

0%

4%

0.7

13%

0.9 0%

2%

16%

2.7 1%

1.9

19%

5%

0.3 3%

14%

6%

1%

9.3

21%

3%

3%

65%

57.4 5%

2%

54%

74%

14.1 3%

32.7 4%

1.7 0%

21.1 8%

The table reports mean volume, by counter party combination, per five-minute period. Below each mean volume is the percent of total volume represented by the counterparty combination. All volume is for the daily most active contract only.

Table 1

Table 2. Characteristics of the variables. Statistics based on five-minute brackets for the first six months of 1992, Chicago Mercantile Exchange. Price volatility Total customer Absolute customer measures based on onevolume (contracts net volume sided customer prices, for bought and sold) (buys less sells) five-minute brackets Commodity during five-minute during five-minute range std. dev. (trading hours) statistic brackets brackets

Equity Index S&P 500 8:30 - 3:15 Currency Deutsche mark 7:20 - 2:00 *

mean median

445 317

46 31

$196 175

$99 83

mean median

396 256

72 42

58 50

45 35

Swiss Franc 7:20 - 2:00 *

mean median

176 109

41 22

68 50

43 34

Pound 7:20 - 2:00 *

mean median

123 66

29 15

64 50

42 31

Yen 7:20 - 2:00 *

mean median

190 107

45 21

46 37.5

35 27

Canadian dollar 7:20 - 2:00 * Interest rate Eurodollar 7:20 - 2:00 *

mean median

56 19

14 6

13 10

10 7

mean median

736 345

184 95

18 25

32 13

T-Bill 7:20 - 2:00 * LIBOR 7:20 - 2:00 * Agriculturals Live Cattle 9:05 - 1:00 * Pork Bellies 9:10 - 1:00 * Hogs 9:10 - 1:00 * Feeder Cattle 9:05 - 1:00 *

mean median

46 17 62 30

22 6 26 10

6 0 1.75 0

6 0 3 0

mean median

148 85 38 18 65 31 18 9

30 17 9 5 18 9 7 3

30 30 45 40 27 20 19 10

17 15 22 18 15 13 11 7

mean median

12 5

4 2

41 16

23 11

Lumber 9:00 - 1:05 *

mean median mean median mean median mean median

Customer volume for a five-minute period is the sum of total customer (Customer Type indicator 4) buy contract volume and sell contract volume (for the five-minute period) for the most actively traded contract (I.e. delivery month) for the given trading day. Customer net order flow is the difference between customer buy volume and sell volume (for the most active contract of the day) for a given five-minute period. Volatility measures based on "onesided" customer prices use the maximum, for each five-minute bracket, of the statistic based on customer buy prices and the statistic based on customer sell prices. For example, if the range of customer buy prices for a bracket exceed the range of customer sell prices for a given bracket, the price range is based on the range of sell prices. The volatility measuires are based on the most active daily contract. * currency, interest rate, and agriculturals closed at 12:00 noon (central time) on the following 1992 holidays: January 20, February 14, April 6, and May 22.

Table 2

Table 3. Regression results: Estimated Price impact (λ λ ) of customer order flow (ω ω). TS

model 1: ∆ p

C t

model 3: ∆ p

TS

= α + λ 1 ωt + ε t .

model 2: ∆ p

= α + λ 4 ωt + ε t .

model 4: ∆ p

t

C t

Global price change ∆ p

= α + λ 2 ωlocalt + λ 3 ω hedgert + ε t .

t

= α + λ 5 ωlocalt + λ 6 ω hedgert + ε t . Customer one-sided price change ∆ pCt

TS

Commodity S&P 500

t

model 1: λ1

λ2

λ3

model 3: λ4

λ5

(std. error)

(std. error)

(std. error)

(std. error)

(std. error)

model 2:

model 4: λ6 (std. error)

0.270

0.274

0.267

1.055

1.086

0.934

(0.022)*

(0.026)*

(0.061)*

(0.020)*

(0.025)*

(0.057)*

correlation of

∆p

TS

t,

∆p

C

t

0.790

Currency Deutsche mark Swiss Franc Pound Yen Canadian dollar

0.114

0.205

0.037

0.215

0.307

0.138

(0.004)*

(0.007)*

(0.006)*

(0.004)*

(0.007)*

(0.006)*

0.173

0.334

-0.013

0.347

0.506

0.162

(0.009)*

(0.013)*

(0.015)

(0.008)*

(0.013)*

(0.014)*

0.231

0.338

0.059

0.457

0.572

0.280

(0.014)*

(0.020)*

(0.026)

(0.014)*

(0.019)*

(0.024)*

0.093

0.131

0.065

0.181

0.250

0.130

(0.005)*

(0.010)*

(0.008)*

(0.005)*

(0.010)*

(0.008)*

0.078

0.132

0.033

0.123

0.172

0.083

(0.008)*

(0.014)*

(0.013)*

(0.006)*

(0.010)*

(0.009)*

0.817 0.777 0.789 0.782 0.699

Interest rate Eurodollar T-Bill LIBOR

0.003

0.012

-0.012

0.021

0.028

0.007

(0.001)*

(0.001)*

(0.002)*

(0.001)*

(0.001)*

(0.001)*

0.046

0.080

0.015

0.034

0.056

0.011

(0.006)*

(0.010)*

(0.011)

(0.004)*

(0.006)*

(0.006)

0.027

0.075

-0.015

0.010

0.023

0.001

(0.008)*

(0.015)*

(0.013)

(0.003)*

(0.005)*

(0.004)

0.606 0.506 0.393


0.043

0.103

-0.129

0.148

0.203

-0.010

(0.007)*

(0.008)*

(0.016)*

(0.006)*

(0.008)*

(0.014)

0.506

0.633

-0.149

0.858

0.946

0.300

(0.037)*

(0.043)*

(0.145)

(0.032)*

(0.037)*

(0.125)

0.103

0.195

-0.128

0.230

0.292

0.072

(0.010)*

(0.014)*

(0.025)*

(0.009)*

(0.011)*

(0.020)*

0.244

0.219

0.616

0.359

0.338

0.662

(0.037)*

(0.039)*

(0.203)*

(0.024)*

(0.026)

(0.135)*

0.621

0.831

-7.095

1.690

1.770

-2.130

(0.621)*

(0.167)*

(1.622)*

(0.100)*

(0.102)*

(0.990)

0.795 0.732 0.723 0.642 0.622

Net order flow (ω ωt) is net customer buy volume (buy volume less sell volume) in 5-minute bracket t. Models 2 and 4 estimate the impact of decomposed order flow; where order flow is split into two components: ω

1ocal

trading for personal account (CTI 1); ω

hedger t

t is net customer volume executed against local floor traders

is net customer volume executed against hedging contra parties (CTI 2 and 3). The dependent

variable in models 1 and 2 is the Time and Salesprice change series, ∆ pTSt ; models 3 and 4 use cutomer one-sided price changes.

* p-value less than 1%.

Table 3

Table 4. Economic interpretation of depth estimates. Price impact estimates based on Table 3:

Commodity (margin*) S&P 500 $22,000

mean price of most mean active absolute contract daily price 1/92-6/92 change $205,654 $1,861

price impact estimate (prices used)

notional principal order flow imbalance represented ($mm) by initial margin ($mm) for estimated (contracts)needed to move contract imbalance needed contract imbalance needed price by: to move price: to move price by: price impact mean daily mean daily mean daily (λ λ) change 1% change 1% change 1% (from Tab.2)

λ 1 (TS) λ 4 (customer)

0.270 1.055

6,893 1,764

7,617 1,949

$1,417 363

$1,566 401

$151.6 $38.8

$167.6 $42.9

Currency Deutsche mark $1,755

76,789

621


0.114 0.215

5,447 2,888

6,736 3,572

418 222

517 274

9.6 5.1

11.8 6.3

Swiss Franc $2,025

84,829

783


0.173 0.347

4,526 2,256

4,903 2,445

384 191

416 207

9.2 4.6

9.9 5.0

Pound $2,295

110,817

846


0.231 0.457

3,662 1,851

4,797 2,425

406 205

532 269

8.4 4.2

11.0 5.6

Yen $1,688

96,656

550


0.093 0.181

5,914 3,039

10,393 5,340

572 294

1,005 516

10.0 5.1

17.5 9.0

Canadian dollar $810

84,080

246


0.078 0.123

3,154 2,000

10,779 6,836

265 168

906 575

2.6 1.6

8.7 5.5

Eurodollar $675

988,952

211


0.003 0.021

69,556 3,260,087 9,937 465,727

47.5 6.8

2,225.1 317.9

T-Bill $675

990,278

156


0.046 0.034

213,185 288,427

2.3 3.1

145.3 196.6

LIBOR $540

2,989,756

102


0.027 0.010

11,295 3,310,608 30,496 8,938,641

2.0 5.5

598.0 1,614.5

Interest rate

70,333 3,296,507 10,048 470,930 3,391 4,588

215,278 291,258

3,778 1,107,317 10,200 2,989,756

3,358 4,544

Agriculturals Live Cattle $700

30,079

258


0.043 0.148

6,000 1,743

6,995 2,032

180 52

210 61

4.2 1.2

4.9 1.4

Pork Bellies $840

13,985

414


0.506 0.858

818 483

276 163

11 7

4 2

0.7 0.4

0.2 0.1

Hogs $560

17,540

252


0.103 0.230

2,447 1,096

1,703 763

43 19

30 13

1.4 0.6

1.0 0.4

Feeder Cattle $700

34,068

259


0.244 0.359

1,061 721

1,396 949

36 25

48 32

0.7 0.5

1.0 0.7

Lumber $1,200

37,911

602


0.621 1.690

969 356

610 224

37 14

23 9

1.2 0.4

0.7 0.3

The table reports the net order flow required to move prices given amounts (by the mean daily price change and by 1%). The net order flow required to move prices given amounts is represented in three forms. First, the order flow imbalance is represented as the number of contracts. Second, the notional principal represented by the contracts is shown, and finally, the table reports the initial margin requirements for the contract imbalance required to move prices the given amount. For each contract, the table reports two price impact estimates, one for the Time and sales (TS) price change series, and one for the customer price change series. Contract mean sample period prices and mean absolute daily price changes are based on the most actively traded contract for each day. * Margin is initial exchange performance bond requirements for speculative trades as of May 12, 1992. Margin requirements change regularly. For the sample period prior to 5/12/92, marigins differed for the following commodities (prior margin in parentheses): Mark ($1350), Swiss franc ($1755), Pound ($2340), Yen ($1350), Canadian dollar ($540), Eurodollar ($540), T-bills ($540), and Pork Bellies ($1120). Margins for the other contracts were unchanged. Exchange members, designated hedgers, and spread trades have lower margin requirements. Cattle, Hogs, and Bellies have higher margin during delivery months.

Table 4

Table 5. Regressions of volatility (five-minute price range) on volume decomposed by counterparty combination Customer (CTI 4) Volume against: CTI 1:

Commodity S&P 500

CTI 2:

CTI 3:

int


clearing members

floor hedgers

102.600

13.354

23.709

5.499

68.29*

27.30*

10.48*

1.50

CTI 4:

Clearing members (CTI 2) volume against: CTI 1: CTI 2: CTI 3:

other customers


clearing members

-0.525

8.473

-4.854

-0.60

4.39*

-0.44

Local hedger (CTI3) volume against: CTI 1: CTI 3:

floor hedgers


other floor hedgers

CTI 1 to CTI 1: local to 1ocal

31.432

20.333

-28.310

0.616

2.60*

8.60*

-1.43

R

2

0.459

n 10,268

0.385

10,003

0.352

9,984

0.335

9,942

0.324

9,950

0.342

9,141

0.450

9,680

0.231

6,045

0.069

2,776

0.428

5,918

0.472

5,677

0.410

5,748

0.334

4,985

0.411

4,624

0.48

Currency Deutsche mark

30.458 49.95*

Swiss Franc

38.547 56.67*

Pound

37.951 48.68*

Yen

30.456 63.90*

Canadian dollar

8.403 44.55*

6.668 34.26*

11.116 29.96*

16.375 27.59*

6.525 23.37*

13.797 36.70*

2.649 8.94*

1.687 2.51

7.396 6.31*

2.308 5.71*

2.114 4.43*

3.407 3.25*

29.524 7.83*

21.851 6.69*

-2.202 -1.23

2.728 2.21

0.344 1.18

0.599 0.82

13.642 16.26*

1.007 2.44

-0.072 -0.33

0.699 1.28

5.091 5.34*

6.904 3.88*

0.140 0.19

10.468 8.96*

4.377 5.03*

13.154 4.86*

16.990 3.36*

7.204 4.54*

7.361 3.04*

3.966 3.08*

10.261 2.14

2.141 0.36

9.123 4.35*

8.998 4.01*

3.124 3.58*

14.090 5.58*

23.565 5.95*

20.888 14.06*

27.172 16.33*

27.467

-0.851

4.69*

37.696

-1.35

10.953

1.84

-49.298

8.63*

5.831

-1.53

-32.111

2.35

11.895

-2.29

52.845

10.20*

38.498

4.36*

17.26*

Interest rate Eurodollar

9.640 36.94*

T-Bill

2.357 10.46*

LIBOR

0.593 2.68*

0.415 11.86*

7.446 23.68*

2.523 8.13*

0.544 5.97*

1.114 2.53

0.154 0.53

0.566 4.75*

4.524 6.96*

1.030 1.86

0.668 7.46*

3.141 5.80*

0.957 3.53*

0.453 7.86*

4.654 11.29*

3.284 5.96*

0.382 2.15

2.168 3.11*

0.702 0.74

2.052 11.15*

3.066 4.38*

0.401 0.26

0.641 6.20*

9.483 6.12*

14.404 5.00*

0.106

0.956

0.32

2.987

9.77*

12.931

0.86

22.132

10.11*

8.608

1.86

2.65*

Agriculturals Live Cattle

18.731 62.50*

Pork Bellies

27.626 52.34*

Hogs

18.240 62.67*

Feeder Cattle

10.692 28.42*

Lumber number pos number pos & sig

22.326 26.29* 14 14

4.065 19.82*

27.283 26.12*

7.364 21.02*

31.182 28.76*

128.402 35.71* 14 14

3.429 4.80*

-16.248 -2.06

6.379 5.47*

32.946 2.34

320.545 3.56* 13 9

4.475 4.74*

-1.131 -0.18

8.809 6.76*

64.352 8.69*

-9.512 -0.17 11 8

2.423 7.51*

21.913 9.86*

8.772 14.77*

17.534 6.82*

57.153 6.05* 12 9

5.869 6.16*

29.574 3.88*

4.160 3.81*

39.469 5.27*

209.725 6.22* 14 12

12.029 2.09

-46.376 -0.67

35.466 3.57*

-634.158 -2.35

248.926 0.24 11 7

6.738 1.49

-20.326 -0.78

14.404 2.96*

153.019 2.75*

401.571 1.47 13 8

11.055 11.40*

59.901 10.49*

13.310 9.57*

38.743 7.15*

84.645 2.25 14 13

19.079

7.928

3.51*

-151.529

10.05*

43.067

-4.02*

15.117

12.49*

14.463

2.53

183.438

8.89*

59.072

4.73*

2004.129

8.25*

188.670

1.92 10 4

9.02* 13 11

The table reports regressions wherein the overall price range (all trades) for a five-minute period is the dependent variable. The explanatory variables are the contract volumes (in units of 100 contracts) for the ten possible counter party combinations. Asterisks represent two-sided p-values below 1.0%.

Table 5

Table 6. Intraday Patterns in the price impact of Customer order flow. Time and sales price change model: ∆p ∆ t = α + λ 1 δ1 ω t + λ 2 δ2 ω t + λ 3 δ3 ω t + λ 4 δ4 ω t + εt . Customer price change model: ∆ pCt = α + λ 1 δ 1 ω t + λ 2 δ 2 ω t + λ 3 δ 3 ω t + λ 4 δ 4 ω t + ε t . where δ 1 = { 1 for announcement date opening 15 minutes, 0 else} ; δ 2 = {1 for non-announcement opening 15 minutes, 0 else}; δ 3 = { 1 for mid-day times; 0 else}; δ 4 = { 1 for closing 15 minutes, 0 else}. TS

Estimated price response during:

GMM (Harvey and Huang) tests for equality:

announce open

regular open

mid-day

close

dependent price change

λ1=λ2

λ2=λ3

λ3=λ4

λ2=λ3=λ4

λ1=λ2=λ3=λ4

λ1

λ2

λ3

λ4

χ2(1)

χ2(1)

χ2(1)

χ2(2)

χ2(3)

TS Customer

0.066 0.944*

0.475* 1.220*

0.247* 1.057*

0.312* 0.857*

1.11 0.81

3.33 2.06

0.58 # 5.25

3.73 # 7.83

4.16 # 7.95

Deutsche mark

TS Customer

0.342* 0.465*

0.142* 0.223*

0.111* 0.212*

0.060* 0.134*

2.38 # 4.21

0.38 0.08

6.22 14.97*

#

7.90 16.59*

#

12.61* 23.41*\

Swiss Franc

TS Customer

-0.055 0.155*

0.141* 0.289*

0.189* 0.361*

0.171* 0.309*

5.56# 4.42#

1.69 2.59

0.19 1.71

1.69 3.15

10.18# 13.43*

Pound

TS Customer

0.926# 1.028*

0.203* 0.417*

0.206* 0.442*

0.279* 0.431*

2.69 2.33

0.00 0.09

1.07 0.03

1.24 0.89

3.62 2.39

Yen

TS Customer

0.199 0.391*

0.092* 0.172*

0.096* 0.187*

0.056* 0.118*

0.75 2.62

0.03 0.18

3.95# 10.93*

4.53 11.01*

5.36 13.78*

Canadian dollar

TS Customer

-0.019 0.080

0.086* 0.153*

0.079* 0.113*

0.079 0.186*

0.92 0.79

0.05 2.25

0.00 2.76

0.06 4.56

0.94 4.66

Eurodollar

TS Customer

0.018 0.063*

-0.003 0.020*

0.004 0.018*

0.008# 0.025*

1.40 8.37*

0.72 0. 38

0.89 4.12#

2.08 4.17

2.80 13.18*

T-Bill

TS Customer

0.030 0.090

0.058 0.078*

0.043* 0.020*

0.053# 0.067*

0.05 0.01

0.12 4.04#

0.18 4.96#

0.26 8.37#

0.27 9.98#

TS Customer

0.154 0.252#

0.004 0.008

0.031# 0.009#

-0.020 -0.013

0.66 5.91#

1.21 0.00

2.06 2.96

2.59 2.99

3.12 9.65#

TS Customer TS Customer TS Customer TS Customer

0.012 0.143*

0.084# 0.200*

0.041* 0.141*

0.001 0.104*

0.99 1.12

0.95 2.49

1.10 0.95

2.37 3.54

2.69 3.55

0.582 1.359*

0.437* 0.759*

0.500* 0.815*

0.703* 1.225*

0.10 1.35

0.12 0.08

1.48 4.69#

1.83 4.97

1.84 6.90

0.248 0.551*

0.019 0.181*

0.111* 0.207*

0.147# 0.306*

1.75 1.35

3.28 0.33

0.30 3.03

3.81 3.60

3.67 6.53

1.118* 1.353*

0.356* 0.581*

0.180* 0.209*

-0.032 0.229#

3.21 2.98

2.29 11.90*

1.38 0.03

5.21 11.92*

9.17# 18.36*

TS Customer

-0.795 0.834

0.519 1.912#

0.506 1.474*

2.613# 3.570*

0.64 0.94

0.00 0.30

2.55 2.77

2.56 3.01

3.68 3.79

Commodity S&P 500

Currency

Interest rate

LIBOR


Asterisks represent Net order flow (ω ω t) is customer buy volume less sell volume in 5-minute bracket t . Time and Sales price changes ∆ pTSt are based on the last price for each five-minute bracket. Customer price changes ∆p ∆ Ct are the change in either customer buy prices (for ω t > 0) or the change in customer sell prices (for ω t < 0). Customer buy price changes are the highest buy price in the last minute of the five-minute period less the lowest buy price in the first minute. Customer sell prices are the lowest customer sell price in the last minute of the five-minute period less the highest sell price in the first minute.

Table 6