Derivatives pricing and liquidity dominance in alternative trading venues

For example, the Singapore Stock Exchange in 1989, the Vancouver Stock Exchange in 1989, the Marche a Terme International de France (MATIF) in 1999 and the Sydney Futures Exchange in 1999.

Market participants use electronic trading systems in three different ways: side-by-side trading (parallel trading to open outcry), off-hours trading and exclusive electronic trading (for example, E-mini futures/options that only trade electronically). In side-by-side trading a trader chooses between open outcry trading and electronic trading systems during regular trading hours.

Pagano and Röell,4 Massimb and Phelps5 and Pirrong6 point out that floor traders’ observations of the bids and asks and trading activity can be used to infer valuable information about the motives for trade, and therefore help determine the size of volume, inventory control and the reduction of adverse selection risk.

Pagano, M. and Roell, A. (1992) Auction and dealership markets: What is the difference? European Economic Review 36: 623.

Massimb, M.N. and Phelps, B.D. (1994) Electronic trading, market structure and liquidity. Financial Analyst Journal 50: 39–50.

Pirrong, C. (1996) Market liquidity and depth on computerized and open outcry trading systems: A comparison of DTB and LIFFE bund contracts. Journal of Futures Markets 16: 519–543.

Front-running is the practice of executing a floor trader's order ahead of a customer order in order to profit from the expected price impacts of the order. Customer orders experiencing front-running often receive inferior prices related to the floor trader's prices due to liquidity issues. Kurov and Lasser8 show that when large stock index futures orders are sent to the floor for execution, market makers often first make trades for their own account in the electronic markets before executing the large customer trades on the floor.

Kurov, A. and Lasser, D.J. (2004) Price dynamics in the regular and e-mini futures markets. Journal of Financial and Quantitative Analysis 39: 365.

Easley, D. and O'Hara, M. (1987) Price, trade size, and information in securities markets. Journal of Financial Economics 19: 69–90.

Informed traders want to hide their identity in order to maximize their trading profits, since counterparty traders will not trade with the informed traders at an advantageous price to the latter group if they know that they are informed traders. Pagano and Roell's11 model shows that market makers can better protect themselves against losses to informed traders in more transparent markets, allowing the market maker to narrow their spreads. Gramming et al 12 find that the probability of informed trading (PIN) is lower on non-anonymous floor-based trading markets. Jain et al 13 find that the permanent price impact of trades is much larger on electronic trading systems than on the open outcry systems, and that the temporary price impact of trades is significantly larger on the electronic trading system than on the open outcry trading system. The permanent price impact is caused by the asymmetric information component, whereas the temporary price impact is caused by the bid-ask bounce (see Harris14).

Pagano, M. and Roell, A. (1996) Transparency and liquidity: A comparison of auction and dealer markets with informed trading. Journal of Finance 51: 579–611.

Gramming, J., Schiereck, D. and Theissen, E. (2001) Knowing me, knowing you: Trader anonymity and informed trading in parallel markets. Journal of Financial Markets 4: 385–412.

Jain, P.K., Jiang, C, McInish, T.H. and Taechapiroontong, N. (2006) Informed trading in parallel auction and dealer markets. The case of the London Stock Exchange: University of Memphis (mimeo).

Harris, L. (2003) Trading and Exchanges. New York: Oxford.

Naidu, G.N. and Rozeff, M.S. (1994) Volume, volatility, liquidity and efficiency of the Singapore stock exchange before and after automation. Pacific-Basin Finance Journal 2: 23.

Ferris, S.P., McInish, T.H. and Wood, R.A. (1997) Automated trade execution and trading activity: The case of the Vancouver stock exchange. Journal of International Financial Markets Institutions and Money 7: 61.

Kyle, A.S. (1985) Continuous auctions and insider trading. Econometrica 53: 1315.

Admati, A. and Pfleiderer, P. (1988) A theory of intraday patterns: Volume and price variability. Review of Financial Studies 1: 3.

See Martens,20 Theissen,21 Hasbrouck,22 Tse and Zabotina,23 Kurov and Lasser,8 and Tse et al 24.

Martens, M. (1998) Price discovery in high and low volatility periods: Open outcry versus electronic trading. Journal of International Financial Markets, Institutions and Money 8: 243–260.

Theissen, E. (2002) Price discovery in floor and screen trading systems. Journal of Empirical Finance 9: 455–474.

Hasbrouck, J. (2003) Intraday price formation in US equity index markets. Journal of Finance 58: 2375–2400.

Tse, Y. and Zabotina, T. (2004) Do designed market makers improve liquidity in open outcry futures markets? Journal of Futures Markets 24: 479–502.

Tse, Y., Xiang, J. and Fung, J.K.W. (2006) Price discovery in the foreign exchange futures market. Journal of Futures Markets 26: 1131.

Refer to Naidu and Rozeff,15 Pirrong,6 Ferris et al,16 Frino et al,26 Huang,27 Ates and Wang,28 Fung et al 29 and Jain et al. 13 Naidu and Rozeff15 investigate the quality of the market after the open outcry markets were fully converted to an electronic trading system on the Singapore Stock Exchange. Alternatively, Coppejans and Domowitz30 and Ulibarri and Schatzberg31 have the opposite findings, that is they show that open outcry markets have lower bid-ask spreads than electronic markets. However, Coppejans and Domowitz's findings do not provide a direct comparison of the market quality between open outcry and electronic markets, since their electronic trading sample data are taken from off-hours trading rather than from side-by-side trading. Ulibarri and Schatzberg do employ a T-bond futures electronic trading sample; however, the sample only uses the first year of side-by-side trading, which was a period of very low electronic trading volume.

Frino, A., McInish, T.H. and Toner, M. (1998) The liquidity of automated exchanges: New evidence from German Bund futures. Journal of International Financial Markets, Institutions and Money 8: 225–241.

Huang, Y.C. (2004) The market microstructure and relative performance of Taiwan stock index futures: A comparison of the Singapore exchange and the Taiwan futures exchange. Journal of Financial Markets 7: 335–350.

Ates, A. and Wang, G.H.K. (2005) Liquidity and Price Discovery on Open-Outcry versus Electronic Trading Foreign Exchange Futures Markets. Akdeniz University (mimeo).

Fung, J.K., Lien, W.D., Tse, Y. and Tse, Y.K. (2005) Effects of electronic trading on the Hang Seng Index futures market. International Review of Economic and Finance 14: 415–425.

Coppejans, M. and Domowitz, I. (1999) Price behavior in an off-hours computerized market. Journal of Empirical Finance 6: 583–607.

Ulibarri, C.A. and Schatzberg, J. (2003) Liquidity costs: Screen-based trading versus open outcry. Review of Financial Economics 12: 381–396.

Stoll, H.R. (1989) Inferring the component of the bid-ask spread: Theory and empirical tests. Journal of Finance 44: 115.

Huang, R.D. and Stoll, H.R 1997 The components of the bid-ask spread: A general approach. Review of Financial Studies 10: 995–1034.

McInish, T. and Wood, R. (1992) An analysis of intraday patterns in bid-ask spreads for NYSE stocks. Journal of Finance 47: 753–763.

French and Roll36 note that price volatility is a direct measure of risk and an indirect measure of the level of information. Since information asymmetry gets larger as the rate of information arrival increases, the risk of adverse selection rises. However, the information asymmetry given a rate of information arrival decreases if trading volume is large, because trading volume dilutes information asymmetry. Therefore, volatility has to be conditioned on volume. This study uses the ratio of volatility to volume as the proxy of information asymmetry. This measure of information asymmetry is consistent with the idea inherent in the measure of liquidity (volume/volatility) in Naidu and Rozeff.15 The conditioning between volume and volatility is performed by including ln(Volume) and ln(Volatility) in the model. It is well known that the regression coefficient of independent variable captures the relation of the independent variable to the dependent variable conditioning on the other included independent variables.

French, K. and Roll, R. (1986) Stock return variances: The arrival of information and the reaction of traders. Journal of Financial Economics 17: 5–26.

The Commodity Futures Trading Commission (CFTC) distinguishes traders into four groups by customer trade indicator (CTI): market makers (CTI1), member floor brokers (CTI2), other floor traders (CTI3) and off-the-floor traders (CTI4).

Refer to Pirrong,6 Frinoet al,26 Huang,27 Fung et al,29 Jain et al,13 Naidu and Rozeff15 and Ferris et al 16.

The negative coefficients for volume in Table 3 are consistent with the negative relation between the bid-ask spread and the competition of limit orders for market makers, as well as the effects of the dilution of information asymmetry in order flow. The positive coefficients for volatility are consistent with the positive relation between bid-ask spread and information asymmetry (proxied by the return volatility in (A3)). McInish and Woods34 and George et al 40 find equivalent results.

George, H., Wang, K., Yau, J. and Baptiste, T. (1997) Trading volume and transaction costs in futures markets. Journal of Futures Markets 17: 757–780.

The results in Table 3 and 4 show that the average spread is larger on Tuesday and Friday for T-notes and T-bonds, respectively. These results are consistent with the untabulated summary statistics on the spreads in weekdays. The average daily half realized spreads of weekdays from Monday to Friday are 2.76, 4.14, 2.95, 3.03 and 2.70 for T-notes and 4.83, 4.98, 4.99, 5.08 and 5.16 for T-bonds.

The value of a2>0 is also consistent with increased fixed operational costs of market making, and increased inventory risk due to decreased liquidity in the open outcry markets. Market makers who trade frequently can spread their fixed costs of market making over more volume in liquid markets relative to less liquid markets. However, a market maker in a less liquid market cannot easily spread out inventory risk, since infrequent trading in the less liquid market reduces the opportunities to quickly lay off inventory imbalances.

Pirrong15 uses German Bund futures traded simultaneously on the LIFFE (open outcry) and DTB (electronically). He finds that the bid-ask spreads and their corresponding volatilities are less on the DTB than on LIFFE. The signs and magnitudes of estimated coefficients of weekday dummies for T-notes in Table 5 are different from those in Tables 3 and 4. These results occur due to the two main reasons: the GARCH model for Table 5 and the correlation of spread to volatility. The GARCH model incorporates the conditional volatility of the spread into the model. The conditional volatility of spread in the model takes some of the explanatory power from the coefficients of other variables. Meanwhile, the untabulated correlation coefficients show the negative relation of spread to volatility on Monday, Tuesday and Wednesday and the positive relation on Thursday and Friday. As a result of the aforementioned two reasons, when the volatility rises on Friday (results show that the volatility is highest on Friday), the spread also goes up on Friday and goes down in other weekdays. Consequently, as the GARCH model incorporates the conditional volatility of spread, the net spread for the weekday dummies is higher on Friday although the coefficients are not statistically significant.

Manaster, S. and Mann, S.C. (1996) Life in the pits: Competitive market making and inventory control. Review of Financial Studies 9: 953–975.

Berkowitz, S.A., Logue, D.E. and Noser, E.A. (1988) The total cost of transactions on the NYSE. Journal of Finance 43: 97–112.

Madhavan, A. (2002) VWAP strategies, in transaction performance. In: B.R. Bruce (ed.) The Changing Face of Trading. New York: Institutional Investor, pp. 32–38.

Manaster and Mann,48 Locke and Sarajoti49 and Choe et al 50 present similar measures of a good trade execution.

Locke, P.R. and Sarajoti, P. (2004) Interdealer trading in futures markets. Journal of Futures Markets 24: 923–944.

Choe, H., Kho, B.-C. and Stulz, R.M. (2005) Do domestic investors have an edge? The trading experience of foreign investors in Korea. Review of Financial Studies 18: 795–829.

Similarly, a positive (negative) sell return means that the majority volume of CTI1's sales occur at trade prices P it higher (lower) than the naïve benchmark VWAP during day t.