This chapter starts with a definition of the term liquidity and its description as a multidimensional concept. Only the most frequently implemented liquidity measures are presented and categorized according to their dimensionality (one- or multi-dimensional) and their calculation base (order book data or transaction data). The result of this chapter is the choice of a multi-dimensional liquidity measure implemented in the empirical part of this study.


Limit Order Order Book Limit Price Market Impact Liquidity Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 59.
    Markowitz’s (1952) seminal work on portfolio theory and the development of the Capital Asset Pricing Model by Sharpe (1964) are both based on the assumption of frictionless capital markets.Google Scholar
  2. 60.
    The central research object of microstructure theory is the trading process. See Cohen/ Maier/ Schwartz/ Whitcomb (1986), p. 1. O’Hara (1997) provides a comprehensive literature overview on microstructure theory, Coughenour/Shastri (1999) provide a review of empirical research, Madhavan (2000) reviews theoretical, experimental, and empirical results relating to trading and markets, and Biais/Glosten/Spatt (2005) offer a synthesis of theoretical and empirical results concerning the consequences of market structure for price formation.Google Scholar
  3. 61.
    See Stoll (2000), p. 1481ff. He states: “Friction in financial markets measures the difficulty with which an asset is traded.” See Stoll (2000), p. 1479.Google Scholar
  4. 62.
    See Kempf (1999), p. 13.Google Scholar
  5. 63.
    See Oesterhelweg/ Schiereck (1993), p. 390.Google Scholar
  6. 64.
    Campell/ Lo/ MacKinlay (1997), p. 99f. Closely related definitions are “a market is liquid if traders can quickly buy or sell large numbers of shares when they want and at low transaction costs”, Harris (1990) p. 3. A similar definition is provided by Bernstein (1987), p. 54 stating that an asset is liquid, if it can be bought or sold immediately and without adversely affecting the price. Schwartz (1988), p. 532 describes liquid assets as “traded quickly at reasonable prices”.Google Scholar
  7. 65.
    See Oesterhelweg/ Schiereck (1993), p. 390f.Google Scholar
  8. 66.
    See Harris (1990), p. 3. Earlier works of Garbade (1982), p. 420, Kyle (1985), p. 1316 and Bernstein (1987), p. 57 define liquidity in terms of three dimensions: tightness, depth, and resilience. These dimensions cover three of the four dimensions of Harris. However, they implicitly assume that immediacy is not a dimension but given in automated markets as Schwartz (1988), p. 524 states “because transactions in highly organized markets can be obtained almost instantaneously”.Google Scholar
  9. 67.
    Kempf (1999) p. 17f. describes the combination of both dimensions as the price dimension which exists in addition to the time dimension of liquidity.Google Scholar
  10. 68.
    Hasbrouck (1988), p. 235 distinguishes between transitory and permanent price changes. While the former are randomly introduced through large orders or order imbalances the latter are caused by information driving the price to its new equilibrium level. The above liquidity definition only excludes transitory price changes. Thus, liquidity and (information) efficiency are compatible. For a description of the potential conflict between liquidity and information efficiency, see Bernstein (1987), p. 62 and Oesterhelweg/ Schiereck (1993), p. 391.Google Scholar
  11. 69.
    See Lee/ Mucklow/ Ready (1993), p. 349ff. The authors describe this relation in detail and note that any study of liquidity has to take the changes in both prices and depth into account, requiring a multi-dimensional view.Google Scholar
  12. 70.
    See Schiereck (1995), p. 25, Oesterhelweg/Schiereck (1993), p. 392 and Brunner (1996), p. 6ff.Google Scholar
  13. 71.
    See Harris (1990), p. 5 and Handa/Schwartz (1996a), p. 45. An exception is a market order that enters a limit order book with an empty opposite side. These orders remain in the order book as unlimited orders. Instruments with an empty order book side would be classified as illiquid and traded in auction only. See Exhibit 2–5.Google Scholar
  14. 73.
    A similar classification is implemented by Anand/ Chakravarty/ Martell (2005), p. 295. Oesterhelweg (1998), p. 18f. proposes a distinction into four categories, additionally distinguishing orders entered at or better than the current BBA into two categories.Google Scholar
  15. 74.
    See Bae/ Jang/ Park (2003), p. 517f. Cohen/Maier/Schwartz/Whitcomb (1981), p. 298 explain the existing spread due to gravitational pull effects: As limit orders face execution risk, it becomes relatively more attractive to execute a market order with certainty than to place a limit order and narrow the spread when the spread is already small, and vice versa.Google Scholar
  16. 75.
    Order submission strategies include the choice of market versus limit order placement, limit order prices, and trade size. Daníelsson/ Payne (2001) analyze the dynamic behavior of liquidity supply and demand in the foreign exchange market on Reuters. Biais/Hillion/Spatt (1995) examine the dynamics for Paris Bourse. Foucault (1999) and Parlour (1998) provide theoretical models for order submission strategies.Google Scholar
  17. 77.
    See Aitken/ Comerton-Forde (2003), p. 47. Kindermann (2005), p. 109 also provides a classification methodology for liquidity measures along the calculation base.Google Scholar
  18. 78.
    Fernandez (1999), p. 1 points out the need to compute several liquidity measures to capture the different dimensions. Amihud (2002), p. 33 doubts that one single measure will be able to capture all dimensions of liquidity, while one-dimensional measures can provide insight to certain questions of a market’s liquidity. Von Wyss (2004), p. 9 distinguishes between single and multi-dimensional measures, concluding that “for a global liquidity measure, certainly one of the multi-dimensional liquidity measures has to be used”.Google Scholar
  19. 79.
    See Admati/ Pfleiderer (1988), p. 33ff.Google Scholar
  20. 80.
    See Kempf (1999), p. 35 and Aitken/Comerton-Forde (2003), p. 58.Google Scholar
  21. 81.
    See Gomber/ Schweickert/ Theissen (2005), pp. 13–19. Coppejans/Domowitz/Madhavan (2004), p. 8f. find evidence for strategic order placement behavior in the Swedish futures market.Google Scholar
  22. 83.
    For quote-driven and specialist markets, effective spreads have been reported to be significantly smaller than quoted spreads due to price improvement granted by the market maker or specialist e.g. Chordia/ Roll/ Subrahmanyam (2001), p. 506 find supporting results for within-quote trading in their long term study for securities traded at NYSE (1988 to 1998). See Glosten/Harris (1988), p. 128, Lee/Ready (1991), p. 739f. and Huang/Stoll (1996b), p. 28ff., as well as a series of articles by Bessembinder, (1997) with Kaufman, p. 296f., (1999), p. 393f. and (2003a), p. 388.Google Scholar
  23. 84.
    Starting with Demsetz (1968), seminal work bid-ask spreads were widely implemented: Schmidt/Iversen (1991), Lee/Mucklow/Ready (1993), Iversen (1994), Booth/Iversen/Sarkar/Schmidt/Young (1995), Lin/Sanger/Booth (1995), Treske (1996), Brockman/Chung (1998), Chordia/Roll/Subrahmanyam (2001), Chung/Van Ness (2001), and Wolff (2003).Google Scholar
  24. 85.
    The two described spread measures (quoted and effective spread) are based on the assumption that bid-ask spreads are readily observable. Additional measures were developed that estimate effective spreads from time series data of transactions alone, so-called implicit spreads (Roll (1984) and Hasbrouck/ Schwartz (1988)). The main difference to spread measures lies in their elimination of the effects of changes to the underlying value on the transaction prices; i.e. the size of bid-ask spread does not react to changes in the underlying value consequently precluding asymmetric information.Google Scholar
  25. 86.
    Demsetz (1968), in his attempt to define market depth, focuses on the marginal increase of the bid-ask spread if the defined trading volume increases. Engle/Lange (1997), p. 9f. propose a statistic for the measurement of the depth of liquidity based on the one-sided volume sustained before a subsequent price move. It is calculated as the sum of the number of shares traded over all transactions within a given price duration. Aitken/Comerton-Forde (2003), p. 50f. compute the relative depth, which is defined as the total volume in the order book divided by the total number of shares in the issue.Google Scholar
  26. 87.
    Kempf (1999), p. 13 and p. 45ff. distinguishes between liquidity measures that are founded theoretically and heuristic liquidity measures, so-called liquidity indicators. Empirical implementation of the measurement methods was often a problem leading to the implementation of liquidity indicators.Google Scholar
  27. 88.
    Examples are Chordia/ Roll/ Subrahmanyam (2001), Hasbrouck/Saar (2002), Hasbrouck/Seppi (2001), Hautsch/Pohlmeier (2002), Lee/Mucklow/Ready (1993), and Lin/Sanger/Booth (1995).Google Scholar
  28. 89.
    See Cooper/ Groth/ Avera (1985), p. 25, Grossman/Miller (1988), p. 630, Marsh/Rock (1986), p. 5 and Oesterhelweg/Schiereck (1993), p. 392.Google Scholar
  29. 90.
    To overcome conceptual problems of the Amivest Liquidity Ratio (the proportional relation of trading volume and price changes, which potentially overestimates liquidity in frequently traded instruments), Marsh/ Rock (1986) develop a liquidity ratio computed as the sum of percentage price changes divided by the number of transactions during a defined time interval. However, the same criticism applies as to the Amivest Liquidity Ratio.Google Scholar
  30. 91.
    See Goldstein/ Kavajecz (2000), p. 142 and p. 146. Jones/Lipson (2001), p. 274ff. also analyze the tick size reduction at NYSE in June 1997, but for institutional trades only. They find that execution costs increased for these (large) trades, concluding that spread alone is not sufficient to evaluate the results of a market structure change.Google Scholar
  31. 92.
    See Kempf (1999), p. 30f. The economic meaning of a liquidity measure based on supply and demand schedules is the gap between an instrument’s supply and demand schedules. If the gap is wide, liquidity is low and it is impossible to match orders. In that sense, it reflects the concession that an impatient trader has to make to achieve immediate execution. In fact, it is an inverse measure of liquidity.Google Scholar
  32. 93.
    For the non-linearity of the function, see Cao/ Hansch/ Wang (2004), p. 6f., Griese/Kempf (2006), p. 405ff., Irvine/Benston/Kandel (2000), p. 16ff., and Kempf (1999), p. 27f.Google Scholar
  33. 94.
    See Irvine/ Benston/ Kandel (2000), p. 7ff.Google Scholar
  34. 95.
    See Beltran/ Giot/ Grammig (2005), p. 7f., Coppejans/Domowitz/Madhavan (2004), p. 7, Domowitz/Hansch/ Wang (2005), p. 353f., and Kumar (2003), p. 6.Google Scholar
  35. 96.
    See Gomber/ Schweickert (2002a), p. 485ff. and Gomber/Schweickert/Theissen (2005), p. 6ff.Google Scholar
  36. 98.
    Exhibit 3-1 is adapted from Gomber/ Schweickert (2002a), p. 486.Google Scholar
  37. 99.
    See Gomber/ Schweickert (2002a), p. 486.Google Scholar
  38. 100.
    See Schwartz/ Francioni (2004), pp. 63–66.Google Scholar
  39. 102.
    Exhibit 3-2 is adapted from Gomber/ Schweickert (2002a), p. 486f.Google Scholar
  40. 103.
    See Deutsche Boerse AG (2002), pp. 1–3.Google Scholar
  41. 105.
    Deutsche Boerse AG (2002), p. 3.Google Scholar
  42. 106.
    For the case where the components of the market impact do not need to be calculated separately, Gomber/ Schweickert/ Theissen (2005), p. 7 have provided the following reduced formula that implements the quantity weighted average execution price \( \overline P \) B,t(V) and \( \overline P \) B,t(V) and the midpoint MPt:\( \left( V \right) = 10,000\frac{{\overline P _B ,t\left( V \right) - MP_t }} {{MP_t }} \) Google Scholar
  43. 109.
    See Coppejans/ Domowitz/ Madhavan (2004), p. 9f., Domowitz (2001), p. 142, or Griese/Kempf (2006), p. 403.Google Scholar
  44. 110.
    The fourth dimension, resilience, is analyzed for Xetra by Gomber/ Schweickert/ Theissen (2005), for the Swedish Stock Index Futures Market by Coppejans/Domowitz/Madhavan (2004), and for Paris Bourse (prior to its merger to Euronext) by Degryse/de Jong/Ravenswaaij/Wuyts (2005).Google Scholar

Copyright information

© Deutscher Universitäts-Verlag | GWV Fachverlage GmbH, Wiesbaden 2007

Personalised recommendations