Market Quality Surrounding a Tick Size Increase Evidence from the

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					    Market Quality Surrounding a Tick Size Increase: Evidence from the Sydney

                                        Futures Exchange




            ANTHONY FLINT, DIONIGI GERACE and ANDREW LEPONE*



                   Finance Discipline, Faculty of Economics and Business,

                         University of Sydney, Sydney, 2006, Australia




* This research was funded by the Sydney Futures Exchange under Corporations Regulation 7.5.88(2).
   Market Quality Surrounding a Tick Size Increase: Evidence from the Sydney

                                    Futures Exchange




Abstract


Numerous studies have examined the relationship between tick size reductions and

liquidity under different market structures. This study is the first to examine the impact of

a tick size increase on market quality in a futures market setting. It uses the natural

experiment provided by the tick size increase for 3-Year Commonwealth Treasury Bond

Futures on the Sydney Futures Exchange in 2009. Results show an increase in bid-ask

spreads following the change, in addition to a significant increase in quoted depth at the

best quotes and throughout the limit order book. Bid-ask spreads per minimum tick

declined, as quoting around the minimum tick is tighter. Execution costs are shown to be

significantly higher after the change. We conclude that an increase in the tick size has a

negative impact on liquidity.
1. Introduction


Financial exchanges worldwide have lowered the minimum price increment to facilitate

greater levels of liquidity to market participants. Reductions in the tick size do not always

generate improvements in market quality if it leads to a pricing grid that is too fine. A

change in tick size has important implications for the bid-ask spread, the level of depth,

trading and order submission strategies, the aggregate of which may lead to an increase

or decrease in overall transaction costs. Recently, a number of exchanges have begun to

increase the minimum tick for certain interest-rate futures.1 The stated reason for this

change is that the higher tick size is expected to encourage quoting on the part of traders

as placing orders is more profitable. This paper examines the impact of a tick size

increase on market quality under a futures market setting.



The decision by major exchanges to reduce the minimum tick has lead to considerable

interest on the part of academic researchers on the relationship between tick size and

transaction costs. Harris (1994) argued that a finer pricing grid would lower the quoted

spread as the tick size constitutes the lower bound of the spread, while quoted depth

would fall as the marginal profitability of supplying liquidity is reduced. The empirical

evidence on tick size reductions support the predictions of Harris (1994), finding both

lower quoted spreads and lower quoted depth, though the literature conflicts on which has

the larger impact on liquidity. Examining the reduction in tick size for stocks priced

below five dollars on the American Stock Exchange, Ahn et al., (1996) document a 19%

1
 The Sydney Futures Exchange increased the size of the minimum tick on the 3-Year Commonwealth
Treasury Bond Futures from 0.5 to 1 basis point. The Eurex increased the Euro Bobl Futures Bond from 0.5
percent to 1 percent on the 30th August, 2009. The CME Group increased the tick size of the 30-Year US
Treasury Bond from 0.5 to 1 basis point on the 30 th August, 2009.
decline in spreads for the most actively traded stocks, while market depth is unaffected.

In another study, Ahn et al., (1998) find the reduction in tick size on the Toronto Stock

Exchange (TSE) to five cents for stocks cross-listed on NYSE/AMEX decreases quoted

spreads by 27% and share volume by 19.9%, respectively. Supporting Ahn et al., (1998),

Bacidore (1997) report a decline in both spreads and depth on the TSE with no adverse

affect to large investors, concluding market quality remained unchanged after the tick

decrease.



In an analysis of the 1997 reduction in the minimum price increment from an eighth to a

sixteenth on the NYSE, Ricker (1998) reports a decline in volume-weighted bid-ask

spreads of 26% and a decline in quoted depth of 45%. The author concludes the reduction

lead to an improvement in liquidity, particularly for low-priced shares. Studying the same

event, Goldstein and Kavajecz (2000) find reduced transaction costs for small market

orders but higher trading costs for larger market orders, concluding the reduction in tick

size did not improve liquidity for all market participants. The results were sensitive to

how actively the security was traded, with the benefit to small orders sharply reduced for

infrequently traded stocks.



Johnson and Lipson (2001) show higher transaction costs for institutional orders after the

decrease in tick size on the NYSE, with orders above 100,000 shares facing increased

transaction costs of a third more after the tick size reduction. Bessembinder (2003)

reports reduced spreads and depth on the NYSE and NASDAQ after the move to

decimalisation in 2001, concluding that liquidity increased. In contrast to Jones and
Lipson (2001), Chakravarty et al., (2005) associate the move to decimalisation with

decreased transaction costs to institutional investors and a significant decline in trading

costs overall.



Bourghelle and Declerck (2004) examine a change in the pricing grid on the Paris Bourse

which raised the tick size for certain stocks and lowered it for others. The authors reveal

the reduction (increase) in the tick size is associated with a decrease (increase) in quoted

depth, while investors use more (less) hidden orders after the decrease (increase) in tick

size. The results show no change in relative quoted and effective spreads under both an

increase and decrease in tick size, suggesting a convex relationship between the tick size

and bid-ask spread. They conclude that reducing the tick size is not always optimal as a

coarse pricing grid may not lead to excessively large spreads, increases quoted depth and

encourages liquidity providers to expose their trading interest.



Investigating the move to decimalisation for UK Long Gilt Futures on LIFFE, ap Gwilym

et al., (2005) report increased quoted spreads and a fall in the mean trade size after the

change. Alampieski and Lepone (2008) examine the reduction in the minimum price

increment of the 3-Year Commonwealth Treasury Bond Futures (3-Year bond futures) on

the SFE. Results indicate a significant reduction in both bid-ask spreads and quoted

depth. The authors estimate price impacts of orders which are found to decrease after the

change, indicating an improvement in liquidity as the reduction in bid-ask spreads offset

the reduced levels of quoted depth.
This paper contributes to the literature by investigating the impact of the increase in

minimum tick of the 3-Year futures bond on the Sydney Futures Exchange (SFE) on

market quality. Contrary to the proponents of tick size reductions, a coarser pricing grid

may have a positive impact on liquidity. In the theoretical model of Cordella and

Foucault (1999), the price increment which minimizes the cost of immediacy is not zero.

If the current tick size is too fine, an increase in the minimum tick will increase the

propensity of investors to post at the competitive spread. Second, even considering that a

higher minimum tick increases the cost of immediacy, this may be offset by significant

growth in limit orders leading to an overall improvement in market quality.



We examine market quality indicators such as the bid-ask spread, quoted depth, traded

volume and volatility before and after the change in tick size in the 3-Year bond futures

on the SFE. As changes in liquidity can result from changes in market conditions as

opposed to the increase in minimum tick, the substitute 10-Year Commonwealth

Treasury Bond Futures (10-Year bond futures) is used as a control. An issue with prior

studies showing declines in both spreads and depth is determining which has a greater

impact on liquidity. We estimate the price impact of trades before and after the change in

tick size to examine the overall change in liquidity. Results suggest that bid-ask spreads

and quoted depth increase significantly after the change, while there is a reduction in bid-

ask spreads per minimum tick. Execution costs are found to increase significantly. These

results hold in overnight trading and are robust to market-wide factors. This suggests that

a higher tick size reduces liquidity under a futures market setting.
The remainder of this paper is structured as follows. Section 2 describes the data and

empirical results and section 3 concludes. A brief overview of the institutional details of

the Sydney Futures Exchange are provided in the Appendix.



2. Data and Empirical Results



The data used in this study is provided by Securities Industry Research Centre of Asia

Pacific (SIRCA) and contains a record describing each transaction, including the contract

code, date, time and the price and volume of each trade. The data also provides the prices

and volumes of prevailing bid and ask quotes throughout the limit order-book time

stamped to the nearest second. On the 11th of May 2009 the SFE increased the minimum

tick size from 0.5 to 1 basis-point for the 3-Year bond futures contract.2 To examine the

impact of the increase in minimum tick on market quality, we examine two subsamples 3

months before and 3 months after the change. The pre-period is 10 February, 2009 to 10

May, 2009 and the post-period is the 13 May, 2009 to 13 August, 2009. As the change in

tick size occurred at 17:00 hours on the 11th of May, 2009, both the 11th and the 12th of

May are excluded from the sample. In line with Frino and McKenzie (2002) who find

abnormal levels of trading near expiry, we exclude the 5 days prior to expiration. In line

with Bortoli, Frino, Jarnecic and Johnstone (2006), analysis is restricted to the nearest to

expiry contract only.




2
 The change in tick size on the 11th of May 2009 coincided with a reduction in the visibility of the order
book in the 3-Year bond futures from 5 to 3 price levels.
Changes in liquidity before and after the increase in minimum tick may reflect changes in

market conditions as opposed to the change in tick size. To determine whether this is the

case, the 10-Year Commonwealth Treasury Bond Futures (10-Year bond futures) is used

as a control contract. The two futures contracts are regarded as substitutes as they trade

on the same platform during the same hours, with underlying assets being risk-free

Australian Government bonds. The minimum tick size of 0.5 basis points and the level of

transparency of 5 price levels on each side of the order-book for the 10-Year bond futures

remained constant over the sample period.



The variables used to assess changes in market quality before and after the change to full-

basis point trading in the 3-Year bond futures are the bid-ask spread, quoted depth, traded

volume and volatility. The bid-ask spread is calculated using two measures. The first is

the absolute bid-ask spread in points, measured as the ask-price minus the bid-price.

Following Frino, Lepone and Wearin (2008), the second measure employed divides the

absolute bid-ask spread by the minimum tick. The bid-ask spread is sampled over 5-

minute (15-minute) intervals for the day (night) and then averaged over each trading day.



Lee et al (1993) establish that an examination of liquidity must involve an analysis of

both spreads and depth, with Harris (1994) arguing that changes in liquidity can only be

determined by assessing changes in depth throughout the limit order book. Goldstein and

Kavajecz (2000) note that an analysis of depth at the best prices omits valuable

information as to whether the change in tick size results in a sufficient change to

cumulative depth to change the transaction costs of large orders. Consequently, quoted
depth is examined using two measures; best depth and total depth. Best depth is defined

as the combined volume of shares available at both the best bid price and best ask price at

the end of each interval. Total depth is the sum of the volume of shares at each bid and

ask price throughout the visible limit-order book at the end of each interval. Similar to

bid-ask spreads, best and total depth are sampled over 5-minute (15-minute) intervals for

the day (night) and then averaged over each trading day. Traded volume is calculated as

the total number of shares traded during the day. Volatility is measured as the natural

logarithm of the highest traded price divided by the lowest traded price for each day.


2.1 Univariate Results


Table 1 provides descriptive statistics of the market quality indicators for the 3-Year and

10-Year bond futures before and after the increase in minimum tick size. Prior literature

including Ahn et al., (1996, 1998), Goldstein and Kavajecz (2000) and Alampieski and

Lepone (2008) indicate reductions in the minimum tick lead to lower bid-ask spreads.

Supporting this empirical evidence, bid-ask spreads for the 3-year Treasury bond are

significantly wider after the increase in minimum tick. Average bid-ask spreads increase

from 0.0053 basis points in the pre-period to 0.0102 points in the post-period for trading

during the day, and from 0.0089 basis points to 0.0119 basis points for night trading. Bid-

ask spreads as a function of the minimum tick are tighter after the change however,

decreasing from 1.055 ticks in the pre-period to 1.015 ticks in the post-period for day

trading, a statistically significant decline. A similar result occurs for night trading. The

reduction in spreads per minimum tick may reflect an increasing proportion of traders
quoting at the competitive spread as it is more profitable to trade given a larger tick size.3

Results for the 10-Year bond indicate a significant decline in both measures of the bid-

ask spread for day and night trading after the change. This suggests the reduction in

spreads per minimum tick for the 3-Year futures contract could be driven by market-wide

improvements in liquidity. The increase in the absolute bid-ask spread however is

confined to the 3-Year contract.



Harris (1994) predicted a reduction in the tick size would decrease quoted depth as

liquidity provision is less profitable and more risky. Supporting this conjecture, depth at

the best prevailing quotes and total depth throughout the visible order book exhibits

significant growth after the change for the 3-Year bond futures. Depth at the best

prevailing quotes increases by a significant 959 contracts during day trading and a

significant 255 contracts during night trading. Total depth in the order book follows a

similar trend, increasing from 1833 contracts (1008 contracts) during the day (night) to

4686 contracts (1610 contracts). This result is consistent with Alampieski and Lepone

(2006) who report a decline in quoted depth on the SFE after the reduction in the

minimum tick in 2006.



While this indicates that the change in tick size has improved the level of quoted depth,

depth for the 10-Year bond futures contract shows a similar increase after the move to

full basis-point trading. There is a statistically significant increase of 37 contracts at the

best prevailing quotes and 210 contracts for total depth (best depth increases by 10

3
  As a test of this assertion, we calculate the proportion of minimum tick spreads to the total number of
minimum tick spreads during each trading day and night over the two sample periods. A paired t-test shows
the proportion of minimum tick spreads quoted increases significantly after the tick size change.
contracts and total depth increases 44 contracts in overnight trading). These results

suggest the improvement in depth levels for the 3-Year Bond futures could also be driven

by market-wide factors, not the change in tick size.



The level of average daily volume for the 3-Year bond futures displays a significant

improvement after the tick increase, growing from 39,665 contracts in the pre-period to

51,461 contracts in the post period. A similar significant increase of 5,381 contracts

occurs for the overnight session. There is a decrease in traded volume for 10-Year bond

futures, though this movement is statistically insignificant. In contrast, overnight volume

for the 10-Year bond futures contract shows an increase of 981 contracts, which is

significant at the 1 percent level. There is no significant change in volatility for both

futures bonds.



To further ensure the change in liquidity in the 3-Year bond contract results from the

increase in the minimum tick and not the impact of seasonality in trading, the post period

is compared to the period 13 May, 2008 to 13 August, 2008. Descriptive statistics

presented in Table 2 indicate that the change in liquidity is driven by the increase in tick

size and not market-wide impacts. Supporting earlier results, there is a significant

increase of 0.005 basis points in the bid-ask spread for the 3-Year bond, while spreads as

a function of the minimum tick declined by a significant 0.0215 ticks. For the 10-Year

bond, both raw spreads and spreads per minimum tick declined for day trading. There

was a statistically significant increase in bid-ask spreads for night trading, though less
than the 3-Year bond (0.0008 basis point increase for the 10-Year bond as opposed to the

0.004 basis point increase for the 3-Year bond).



Quoted depth at both the best quotes and throughout the limit order book increased for

the 3-Year bond, in contrast to the 10-Year bond where depth levels declined. Trading

volume is significantly higher for the 3-Year bond futures but significantly lower for the

10-Year bond, while volatility is significantly higher across both contracts. The greater

levels of trading volume may have been caused by the tick increase, as the higher profit

potential from wider bid-ask spreads attracts traders to the market. Controlling for

seasonal trading patterns, the increase in the bid-ask spread and depth levels are confined

to the 3-Year contract.


2.2 Regression results


As documented by Chordia, Roll and Subrahmanyam (2000), changes in market quality

indicators such as bid-ask spreads and quoted depth covary with changes in market-wide

liquidity factors. To control for broad market movements that captures market-wide

factors affecting bid-ask spreads and quoted depth, the following regressions are

estimated



       BAS3 = α0 + α1Change + α2BAS10 + ε                                       (1)

       Ln(BestDepth3) = α0 + α1Change + α2Ln(BestDepth10) + ε                   (2)

       Ln(TotalDepth3) = α0 + α1Change + α2Ln(TotalDepth10) + ε                 (3)
where Change is a dummy variable assigned the value of 1 if the observation is taken

from the post-event sample, or 0 otherwise, BAS3 is the bid-ask spread per minimum tick,

Ln(BestDepth3) is the logarithm of the aggregate order volume at the best bid and best

ask price and Ln(TotalDepth3) is the logarithm of the aggregate order volume throughout

the limit-order book for the 3-Year bond futures. The variables BAS10, Ln(BestDepth10)

and Ln(TotalDepth10) represent bid-ask spreads, the logarithm of best depth and the

logarithm of total depth for the 10-Year bond contract, respectively. In equation (1) the

bid-ask spread per minimum tick as opposed to the absolute bid-ask spread is used as the

dependent variable. One of the major reasons for the current trend on exchanges to

increase the tick size is to encourage quoting on the part of liquidity suppliers. It is more

informative therefore to examine the spread per minimum tick as it indicates whether an

increasing proportion of traders are quoting at the competitive spread. These three

regressions are re-estimated using the 10-Year bond futures as dependent variables.



Chordia, Roll and Subrahmanyam (2000) also show that liquidity measures such as the

bid-ask spread and quoted depth are dependent on factors specific to the particular

financial instrument in addition to market-wide liquidity factors. Harris (1994) argues

that two important determinants of the bid-ask spread and quoted depth are traded

volume and price volatility. To control for both market-wide and security specific factors

on the bid-ask spread and quoted depth, we follow Frino, Gerace and Lepone (2008) and

estimate the following equations



       BAS3= α0 + α1Change + α2 Ln(Volume10) + α3Volatility10
               + α4 Ln(Volume3) + α5Volatility3 + ε                                    (1)

       Ln(BestDepth3) = α0 + α1Change + α2 Ln(Volume10) + α3Volatility10

                          + α4 Ln(Volume3) + α5Volatility3 + ε                         (2)

       Ln(TotalDepth3) = α0 + α1Change + α2 Ln(Volume10) + α3Volatility10

                           + α4 Ln(Volume3) + α5Volatility3 + ε                        (3)



where the dependent variables represent the bid-ask spread, the logarithm of best depth

and the logarithm of total depth, respectively. Change is again a dummy variable

assigned the value of 1 if the observation is taken from the post-event sample, or 0

otherwise. Ln(Volume10) is the logarithm of the average daily traded volume in the 10-

year bond futures and Volatility10 is the natural logarithm of the highest traded price

divided by the lowest traded price for each day in the 10-Year bond futures. Ln(Volume3)

is daily traded volume in the 3-Year bond futures. Volatility3 represents volatility in the

3-Year bond futures. The above regressions control for contract-specific and market-wide

impacts as it includes the control variables traded volume and volatility for both the 3-

Year bond futures and its substitute contract the 10-Year bond futures. In equation (1) the

bid-ask spread per minimum tick as opposed to the absolute bid-ask spread is used as the

dependent variable for reasons stated above. These three regressions are re-estimated

using the 10-Year bond futures as dependent variables.



The regression results presented in Table 3 controls for broad market movements using

the Chordia, Roll and Subrahmanyam (2000) specification. In Panel A, the results for the

bid-ask spread regression for the 3-Year Bond futures shows a significant decline in bid-
ask spreads as a function of the minimum tick. A similar result is found for the night

trading session. The results for the 10-Year bond futures show a negative coefficient on

the dummy variable, indicating that spreads per minimum tick narrowed over the period.

The size of the coefficient however was smaller relative to the 3-Year bond and was only

significant at the 5 percent level. For night trading the dummy coefficient is positive and

statistically significant.



As reported in Panel B and C of Table 3, best depth and total visible depth for the 3-Year

futures contracts has grown significantly in the post-period after controlling for depth in

the 10-Year bond contract. These results are consistent across the night time period. The

regressions for the control contract show a reduction in best depth after the change in

minimum tick, though this decline is statistically insignificant for both day and night

trading. Total depth shows no significant change during the day and a significant increase

overnight. This suggests the change in tick size and not market-wide improvements are

driving the changes in spreads and depth for the 3-Year bond futures.



Similar conclusions are drawn from the results of the combined regression specification

of Harris (1994) and Chordia et al., (2000). Panel A of Table 4 shows that after

controlling for volatility and volume for both the 3-Year and 10-Year futures contracts,

bid-ask spreads as a function of the minimum tick experience a statistically significant

decline after the change in tick size for both day and night trading. For the 10-Year bond

futures, the dummy coefficient is significantly negative for both day and night trading as

well.
The regression results in Panel B and C of Table 4 show a significant improvement in

both the best and total depth levels after the increase in minimum tick. The coefficient on

the change dummy variable is significantly positive for both day and night trading. This

result is specific to the 3-Year bond futures, with the coefficient on the change dummy

variable for the 10-Year bond contract being insignificant for both day and night trading,

except for total depth in overnight trading which exhibited a significant increase. The

change in depth is isolated to the 3-Year bond futures contract.



As a robustness test, the above regressions were re-estimated using the period of 13 May,

2008 to 13 August, 2008 as the pre-event sample period. Confirming the prior results,

regression results in Table 5 and 6 show a decline in bid-ask spreads as a function of the

minimum tick after the change in tick size for the 3-Year bond futures, suggesting the

increase tick size has resulted in more traders quoting at the minimum tick. Quoted depth

for the 3-Year bond futures continue to increase after the tick change when controlling

for seasonality in trading, while depth for the 10-Year contract declines. Interestingly, the

results reveal that total depth in overnight trading for the 3-Year bond futures exhibited

no real change, suggesting the tick size had no apparent affect. However, the results show

a decline in total depth on overnight trading for the 10-Year bond futures, indicating total

depth did improve relative to market conditions. 4 5


4
  The increase in the minimum tick coincided with a reduction in the visibility of the order book in the 3-
Year bond futures from 5 to 3 price levels. This change in transparency could affect the impact of the
increase in the tick size on liquidity. However, in a study of order-book transparency and market quality on
the SFE, Alampieski and Lepone (2006) report no change in market quality when the SFE increased order-
book visibility from 3 to 5 levels for the 10-Year bond futures. Given the high substitutability between the
3-Year and 10-Year bond futures, it is therefore considered unlikely that the change in transparency in the
3-Year bonds has had an impact on liquidity.
5
  Another factor that could impact liquidity in the 3-Year contract is changes in the cash rate by the Reserve
Bank of Australia. To examine whether monetary policy has had an impact on liquidity, the regressions
2.3 Execution Costs



The results in the previous section show a reduction in spreads per minimum tick and an

increase in absolute bid-ask spreads and quoted depth for the 3-Year bond futures. To

provide a more accurate assessment of the change in liquidity after the increase in

minimum tick (i.e whether the change in bid-ask spreads dominates the change in quoted

depth), we calculate the price impact of executing orders. To calculate price impact, each

trade is classified into four mutually exclusive quartiles based on trade size. The first

quartile contains the smallest 25% of trade sizes and the fourth quartile contains the

largest 25% of trade sizes. The price impact of each trade is measured by calculating the

return from the price 10 trades prior to the trade price. The absolute value of each price

impact is recorded to incorporate both buy and sell trades. This is averaged across each

day (night) and then across each sample period. Price impacts are reported in basis points.



Results of the price impact analysis are reported in Table 7. Examining the price impact

results for the 3-Year bond, there is no significant change in execution costs across all

four quartiles for day and night trading. Execution costs for the first quartile during day

trading averaged 0.5793 basis points before the tick increase and 0.5836 basis points

after, an insignificant change. A similar result is shown for the fourth quartile, showing

an insignificant increase from 0.6350 to 0.6585 basis points. These results differ from

Alampieski and Lepone (2009) who report a reduction in execution costs after the tick


were re-estimated including the daily settlement price of the 30-Day interbank bill rate. All estimated
coefficients on the interest rate variable were insignificant.
size reduction on the SFE and indicate that the increase in minimum tick has not had an

impact on liquidity.



Broad-market movements may be causing execution costs for the 3-Year bond to remain

unchanged, with an examination of the 10-Year bond suggesting this is the case. As

shown in Panel C of Table 7, there is a reduction in execution costs across all four

quartiles for day and night trading for the 10-Year bond. Furthermore, this change is

significant for 3 out of the 4 quartiles, indicating execution costs for the 3-Year bond

increased relative to market conditions.



To further test whether execution costs for the 3-Year bond are driven by market

conditions, we estimate price impacts for the sample period 13 May, 2008 to 13 August,

2008. As shown in Table 8, there is a significant increase in price impacts across the four

quartiles for both day and night trading for the 3-Year bond. For the 10-Year bond, price

impacts are significantly reduced for day trading but are higher for night trading,

suggesting market conditions cannot explain the increased price impacts for the 3-Year

bond. As the cost of executing trades is significantly higher, we conclude that the

increase in the minimum tick has lead to a significant reduction in liquidity.



To determine the reason for the increase in execution costs, we calculate the average

trade size for the 3-Year bond. The average trade size during the day for the 13 May,

2008 to 13 August, 2008 period was 32 contracts and for the period of 10 February, 2009

to 10 May, 2009 it was 22 contracts. Best depth levels in contrast were 531 and 291
contracts respectively for these two sample periods. This suggests that the increase in

spreads dominates the increase in quoted depth as there was already sufficient depth prior

to the tick increase to accommodate trading.



3. Conclusion



This paper examines the impact of a tick size change on market quality for the Sydney

Futures Exchange. Several studies have examined the impact of a tick size reduction on

liquidity. The literature for both equities and futures markets provide mixed results. A

reduction in the tick size is associated with lower spreads and quoted depth. As these

changes have conflicting effects on liquidity, certain studies attribute the change in

spreads and depth as indicative of an improvement in liquidity, while other studies

conclude a reduction in overall liquidity.



This paper is the first to examine the impact of an increase in the tick size under a futures

market setting. Results indicate that the change in minimum tick is associated with an

increase in bid-ask spreads and depth at the best quotes and throughout the limit order-

book. Bid-ask spreads per minimum tick decline after the change, suggesting an

increasing proportion of trades quoted at the competitive spread. Despite a reduction

spreads per minimum tick, execution costs are higher after the change, with the increase

in spreads dominating the higher depth levels. This is attributed to depth at the best levels

in the order book before the tick size change being sufficient to accommodate trading.

These results hold in overnight trading and are robust to market-wide factors. We
conclude that an increase in tick size has a negative impact on liquidity under a futures

market setting.
Appendix



The Sydney Futures Exchange is the largest futures exchange in the Asia-Pacific Region.

Trading on the SFE operates through a fully automated electronic limit order book called

SYCOM. The two main trader types, local participants and full participants, enter orders

directly into the order-book with trades taking place based on price and time precedence

rules.



The 3-Year bond futures and 10-Year bond futures follow a quarterly expiration cycle.

The contracts start on the 15th of December, March, June or September, with settlement

occurring 3 days before expiration. Both bonds each have face values of AUD 100,000

and are quoted on a “100-yeild” basis (yield deducted from an index of 100.00). The

trading hours for both contracts are between 8:30 and 16:30 hours for daytime trading

and 17:10 and 7:00 hours for night time trading during US daylight savings time.6 The 3-

Year bond futures has a minimum tick of 0.5 basis points and pre-trade transparency of 5

levels either side of the limit order-book before the 11th of May, 2009 and a minimum

tick of 1 basis points after that date and pre-trade transparency of 3 levels either side of

the limit order-book. The 10-Year bond futures has a minimum tick of 0.5 basis points

and pre-trade transparency of 5 levels either side of the limit order-book. Traders can

view in real time prices and order volume on each side of the order book and the traded

volume and price of each trade that occurs. Trading identity however is anonymous as

broker mnemonics are not visible.



6
    Trading is between 17:10 and 7:10 hours for night time trading during US non daylight savings time.
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Cordella, T. and T. Foucault, 1999. Minimum Price Variations, Time Priority, and Quote
Dynamics, Journal of Financial Intermediation 8, 141-173.

Frino, A., D. Gerace and A. Lepone, 2008. Liquidity in auction and specialist market
structures: Evidence from the Italian bourse, Journal of Banking & Finance 32, 2581-
2588.

Frino, A. and M. McKenzie, 2002. The pricing of stock index futures spreads at contract
expiration, Journal of Futures Markets 22, 451 - 469.

ap Gwilym, O., I. McManus and S. Thomas, 2005. Fractional versus decimal pricing:
Evidence from the UK Long Gilt Futures market, The Journal of Futures Markets 25,
429-442.
Goldstein, M.A. and K.A. Kavajecz, 2000. Eighths, sixteenths, and market depth:
Changes in tick size and liquidity provision on the NYSE?, Journal of Financial
Economics 56, 125-149.

Harris, L.E., 1994. Minimum price variations, discrete bid-ask spreads, and quotation
sizes, The Review of Financial Studies 7, 149-178.

Lee, C., B. Mucklow and M. Ready, 1993. Spreads, depths, and the impact of earnings
information: An intraday analysis, The Review of Financial Studies 6, 345-374.

Ricker, J.P., (1998). 'Breaking the eighth: Sixteenths on the New York Stock Exchange '.
working paper. 1730 Filbert Street No. 105, San Francisco. CA 94123.
 Table 1
 Descriptive Statistics

 Descriptive statistics are presented for measures of market liquidity surrounding the increase in minimum
 tick in the 3-Year bond futures. The tick size of the 3-Year bond futures was increased from a half to one
 basis point on 11th May 2009. The pre-event sample period extends from 10 February, 2009 to 10 May,
 2009. The post-event sample period extends from 13 May, 2009 to 13 August, 2009. Bid-ask spreads and
 depth are sampled every 5 minutes (15 minutes) and then averaged for each day (night). Bid-Ask Spread is
 the best ask price minus the best bid price in contract points. BAS is calculated as the bid-ask spread divided
 by the minimum tick. Best Depth is the aggregate order volume at the best bid and best ask price. Total
 depth is the aggregate order volume throughout the limit-order book. Volatility is the natural logarithm of
 the highest traded price divided by the lowest traded price for each day. Volume is the average daily traded
 volume. Night trading results are presented in parentheses.

                Bid-Ask Spread             BAS         Best Depth   Total Depth     Volatility       Volume
 Panel A - Pre-period
 3-Year               0.0053              1.055            291         1832           0.1027          39665
                     (0.0089)            (1.774)          (134)       (1008)         (0.0902)        (10799)
 10-Year              0.0054              1.072            110          654           0.1002          17029
                     (0.0096)            (1.912)           (50)        (344)         (0.0946)         (4378)
 Panel B - Post-period
 3-Year               0.0102              1.015           1179         4686           0.1008          51461
                     (0.0119)            (1.190)          (389)       (1610)         (0.0962)        (16180)
 10-Year              0.0052              1.034            148          865           0.0921          15356
                     (0.0089)            (1.780)           (60)        (387)         (0.0972)         (5360)
 Panel C - Change from pre-period to post-period
 3-Year              0.0050**          -0.0314**         959**       2853**           -0.0018       11796**
                   (0.0040)**         (-0.5066)**       (289)**      (602)**         (0.0060)       (5381)**
 10-Year            -0.0002**          -0.0379**          37**        210**           -0.0018         -1673
                   (-0.0007)*          (-0.1308)*        (10)**       (44)*           (0.003)        (981)**
** Indicates statistical significance at the 0.01 level
 * Indicates statistical significance at the 0.05 level
 Table 2
 Descriptive Statistics – Seasonal Control
 Descriptive statistics are presented for measures of market liquidity surrounding the increase in minimum
 tick in the 3-Year bond futures. The tick size of the 3-Year bond futures was increased from half to a full-
 basis point on 11th May 2009. The pre-event sample period extends from 13 May, 2008 to 13 August, 2008.
 The post-event sample period extends from 13 May, 2009 to 13 August, 2009. Bid-ask spreads and depth
 are sampled every 5 minutes (15 minutes) and then averaged for each day (night). Bid-Ask Spread is the best
 ask price minus the best bid price in contract points. BAS is calculated as the bid-ask spread divided by the
 minimum tick. Best Depth is the aggregate order volume at the best bid and best ask price. Total depth is
 the aggregate order volume throughout the limit-order book. Volatility is the natural logarithm of the highest
 traded price divided by the lowest traded price for each day. Volume is the average daily traded volume.
 Night trading results are presented in parentheses.

               Bid-Ask Spread           BAS          Best Depth   Total Depth      Volatility       Volume
 Panel A - Pre-period
 3-Year            0.0052              1.043            531           3471           0.0860          48939
                  (0.0080)            (1.600)           (214)        (1645)         (0.0785)        (14586)
 10-Year           0.0053              1.059             192          1227           0.0860          19662
                  (0.0081)            (1.625)            (85)         (514)         (0.0832)         (7765)
 Panel B - Post-period
 3-Year            0.0102              1.015            1179          4686           0.1008          51461
                     (0.0119)             (1.190)        (389)       (1610)         (0.0962)        (16180)
 10-Year              0.0052               1.034          148          865           0.1008          15356
                     (0.0090)             (1.791)         (60)        (389)         (0.0972)         (5360)
 Panel C - Change from pre-period to post-period
 3-Year              0.0050**           -0.0215**        724**       1215**         0.0150*           2521
                   (0.0040)**         (-0.3339)**       (210)**       (-35)        (0.018)**         (1595)
 10-Year            -0.0001**           -0.0250**        -44**       -362**         0.0150*         -4307**
                   (0.0008)**          (0.1660)**       (-26)**     (-125)**       (0.014)**       (-2405)**
** Indicates statistical significance at the 0.01 level
 * Indicates statistical significance at the 0.05 level
Table 3
Market wide regressions

This table reports the regression results of spreads and depth around the move to full-basis point trading in
the 3-Year contract. The pre-event sample period extends from 10 February, 2009 to 10 May, 2009. The
post-event sample period extends from 13 May, 2009 to 13 August, 2009. The following regression
models are estimated:
         BASi = α0 + α1Change + α2 ControlBASi + ε
         Ln(BestDepthi) = α0 + α1Change + α2Ln(ControlBestDepthi) + ε
         Ln(TotalDepthi) = α0 + α1Change + α2Ln(ControlTotalDepthi) + ε
where BASi is the bid-ask spread per minimum tick for each contract. Ln(BestDepthi) is the logarithm of
the aggregate order volume at the best bid and best ask price. Ln(TotalDepthi) is the logarithm of the
aggregate order volume throughout the limit-order book. The variable Change is a dummy variable
assigned the value of 1 if the observation is taken from the post-event sample, or 0 otherwise. Night
trading results are presented in parentheses. All t statistics are adjusted for heteroscedasticity and
autocorrelation using the procedure by Newey and West (1987).

                        Intercept          Change         Control(10yr)     Control(3yr)     R2
Panel A: Bid-Ask Spreads
BAS3                    0.7462**           -0.0295**      0.2885**          -                0.4865
                        (1.184)**          (-0.5433)**    (0.3088)**        -                (0.7157)
BAS10                   0.5071**           -0.0162*       -                 -                0.3480
                        (0.8781)**         (0.2090)*      -                 (0.5822)**       (0.2064)
Panel B: Best Depth
Ln(BestDepth3)          3.881**            1.378**        0.3502**          -                0.9239
                        (4.021)**          (1.070)**      (0.1511)          -                (0.7834)
Ln(BestDepth10)         2.289**            -0.2372        -                 0.3982**         0.4295
                        (2.781)**          (0.0445)       -                 (0.1547)         (0.1232)
Panel C: Total Depth
Ln(TotalDepth3)         5.305**            0.8636**       0.3321**          -                0.8166
                        (7.213)**          (0.4463)**     (-0.0682)         -                (0.3909)
Ln(TotalDepth10)        3.763**            -0.0273        -                 0.3530**         0.3524
                        (6.282)**          (0.1898)*       -                (-0.0866)        (0.0487)
** Indicates statistical significance at the 0.01 level
 * Indicates statistical significance at the 0.05 level
 Table 4
 Combined Regressions
 This table reports the regression results of spreads and depth around the move to full-basis point trading in the 3-Year contract. The pre-event sample period
 extends from 10 February, 2009 to 10 May, 2009. The post-event sample period extends from 13 May, 2009 to 13 August, 2009. The following regression
 models are estimated:
          BASi = α0 + α1Change + α2 Ln(Volume10) + α3Volatility10 + α4 Ln(Volume3) + α5Volatility3
          Ln(BestDepthi) = α0 + α1Change + α2 Ln(Volume10) + α3Volatility10 + α4 Ln(Volume3) + α5Volatility3
          Ln(TotalDepthi) = α0 + α1Change + α2 Ln(Volume10) + α3Volatility10 + α4 Ln(Volume3) + α5Volatility3
 where BASi is the bid-ask spread per minimum tick for each contract. Ln(BestDepthi) is the logarithm of the aggregate order volume at the best bid and best ask
 price. Ln(TotalDepthi) is the logarithm of the aggregate order volume throughout the limit-order book. The variable Change is a dummy variable assigned the
 value of 1 if the observation is taken from the post-event sample, or 0 otherwise. Ln(Volume3) is the logarithm of the average daily traded volume in the 3-year
 bond futures. Volatility3 is the natural logarithm of the highest traded price divided by the lowest traded price for each day in the 3-Year bond futures.
 Ln(Volume10) is daily traded volume in the 10-Year bond futures. Volatility10 represents volatility in the 10-Year bond futures. Regression results are presented
 separately for the 3-Year and 10-Year bond futures contracts. Night trading results are presented in parentheses. All t statistics are adjusted for heteroscedasticity
 and autocorrelation using the procedure by Newey and West (1987).

                          Intercept             Change           Ln(Volume10)         Volatility10         Ln(Volume3)          Volatility3         R2
 Panel A: Bid-Ask Spreads
 BAS3                     1.153**               -0.0421**        -0.0145              0.0989               0.0028               0.0384              0.4173
                          (2.922)**             (-0.5269)**      (0.0282)             (0.6776)             (-0.1538)            (-0.3333)           (0.6686)
 BAS10                    1.158**               -0.0455**        -0.0279*             -0.0827              0.0169               0.1474              0.2876
                          (2.469)**             (-0.1377)*       (-0.2424)**          (0.7058)             (0.1620)             (-1.179)            (0.0926)
 Panel B: Best Depth
 Ln(Best Depth3)          2.967**               1.412**          -0.0583              0.1949               0.3129**             -2.676**            0.9233
                          (4.311)**             (1.051)**        (-0.3067)**          (0.5873)             (0.3105)*            (-1.502)            (0.8048)
 Ln(BestDepth10)          1.160                 0.4572**         0.5414**             -0.2494              0.3446*              -3.213              0.6030
                          (2.166)**             (0.2030)**       (0.3496)**           (-1.627)             (0.3834)             (-2.304)            (0.5002)
 Panel C:Total Depth
 Ln(TotalDepth3)          4.532**               0.8667**         -0.0435              0.1580               0.3446**             -0.8724**           0.8298
                          (5.435)**             (0.3449)**       (-0.2278)**          (-0.4510)            (0.3834)**           (0.7213)            (0.5002)
 Ln(TotalDepth10)         3.185**               0.3935**         0.4945**             -0.3537              -0.1364*             -1.188              0.5191
                          (5.849)**             (0.2053)**       (0.3640)**           (-2.085)             (-0.3397)**          (1.648)             (0.2003)
** Indicates statistical significance at the 0.01 level
 * Indicates statistical significance at the 0.05 level
Table 5
Market wide regressions – Seasonal Control
This table reports the regression results of spreads and depth around the move to full-basis point trading in
the 3-Year contract. The pre-event sample period extends from 13 May, 2008 to 13 August, 2008. The post-
event sample period extends from 13 May, 2009 to 13 August, 2009. The following regression models are
estimated:
          BASi = α0 + α1Change + α2 ControlBASi + ε
          Ln(BestDepthi) = α0 + α1Change + α2Ln(ControlBestDepthi) + ε
          Ln(TotalDepthi) = α0 + α1Change + α2Ln(ControlTotalDepthi) + ε
where BASi is the bid-ask spread per minimum tick for each contract. Ln(BestDepthi) is the logarithm of the
aggregate order volume at the best bid and best ask price. Ln(TotalDepthi) is the logarithm of the aggregate
order volume throughout the limit-order book. The variable Change is a dummy variable assigned the value
of 1 if the observation is taken from the post-event sample, or 0 otherwise. All t statistics are adjusted for
heteroscedasticity and autocorrelation using the procedure by Newey and West (1987).

                        Intercept           Change         Control(10yr)    Control(3yr)      R2
Panel A: Bid-Ask Spreads
BAS3                    0.9004**            -0.0250**      0.1350**         -                 0.3717
                        (1.278)**           (-0.4416)**    (0.1989)**       -                 (0.5023)
BAS10                   0.7763**            -0.0173**      -                0.2711*           0.1941
                        (1.189)**           (0.2669)**     -                (0.2727)**        (0.1231)
Panel B: Best Depth
Ln(BestDepth3)          3.477**             1.011**        0.5102**         -                 0.7707
                        (3.838)**           (0.7771)**     (0.2797)*        -                 (0.5674)
Ln(BestDepth10)         2.881**             -0.5580**      -                0.3591**          0.3451
                        (2.744)**           (-0.5388)**    -                (0.2646)*         (0.3035)
Panel C: Total Depth
Ln(TotalDepth3)         5.325**             0.4616**       0.3892**         -                 0.3559
                        (5.770)**           (0.0325)       (0.2496)*        -                 (0.0430)
Ln(TotalDepth10)        4.756**             -0.4414**      -                0.2840**          0.4468
                        (4.730)**           (-0.2859)**    -                (0.1928)*         (0.2665)
 ** Indicates statistical significance at the 0.01 level
  * Indicates statistical significance at the 0.05 level
 Table 6
 Combined Regressions – Seasonal Control
 This table reports regression results of spreads and depth around the move to full-basis point trading in the 3-Year contract. The pre-event sample period extends
 from 13 May, 2008 to 13 August, 2008. The post-event sample period extends from 13 May, 2009 to 13 August, 2009. The following regression models are
 estimated:
          BASi = α0 + α1Change + α2 Ln(Volume10) + α3Volatility10 + α4 Ln(Volume3) + α5Volatility3
          Ln(BestDepthi) = α0 + α1Change + α2 Ln(Volume10) + α3Volatility10 + α4 Ln(Volume3) + α5Volatility3
          Ln(TotalDepthi) = α0 + α1Change + α2 Ln(Volume10) + α3Volatility10 + α4 Ln(Volume3) + α5Volatility3
 where BASi is the bid-ask spread per minimum tick for each contract. Ln(BestDepthi) is the logarithm of the aggregate order volume at the best bid and best ask
 price. Ln(TotalDepthi) is the logarithm of the aggregate order volume throughout the limit-order book. The variable Change is a dummy variable assigned the
 value of 1 if the observation is taken from the post-event sample, or 0 otherwise. Ln(Volume3) is the logarithm of the average daily traded volume in the 3-year
 bond futures. Volatility3 is the natural logarithm of the highest traded price divided by the lowest traded price for each day in the 3-Year bond futures.
 Ln(Volume10) is daily traded volume in the 10-Year bond futures. Volatility10 represents volatility in the 10-Year bond futures. Regression results are presented
 separately for the 3-Year and 10-Year bond futures contracts. Night trading results are presented in parentheses. All t statistics are adjusted for heteroscedasticity
 and autocorrelation using the procedure by Newey and West (1987).

                          Intercept             Change           Ln(Volume10)         Volatility10         Ln(Volume3)          Volatility3         R2
 Panel A: Bid-Ask Spreads
 BAS3                     1.171**               -0.0285**        -0.0008              -0.1521*             -0.0116*             0.1789**            0.3710
                          (1.544)*              (-0.3893)**      (0.1014)             (1.772)              (-0.1058)            (0.1870)            (0.5060)
 BAS10                    1.136**               -0.0301**        -0.0135              -0.0613              0.0046               0.1370              0.1804
                          (2.041)**             (0.0911)         (-0.1673)*           (1.861)              (0.1133)             (-2.060)            (0.1014)
 Panel B: Best Depth
 Ln(Best Depth3)          1.755                 0.8727**         -0.0668              -0.0002              0.4870**             -2.980**            0.1804
                          (4.057)**             (0.5921)**       (-0.2426)*           (-0.7255)            (0.3471)*            (-2.078)            (0.1014)
 Ln(BestDepth10)          2.558**               -0.1040*         0.4294*              0.5729               0.4151*              -1.435*             0.4046
                          (2.522)**             (-0.1888)**      (0.3526)**           (-3.096)*            (0.3506)             (0.9440)            (0.3415)
 Panel C:Total Depth
 Ln(TotalDepth3)          4.351**               0.3273**         -0.0440              -0.3102              0.4151**             -3.147**            0.434547
                          (6.102)**             (-0.0814)        (-0.1992)            (-0.9114)            (0.3506)**           (-3.595)**          (0.1580)
 Ln(TotalDepth10)         5.630**               -0.2330**        0.3562**             -0.2891              -0.1888*             -0.5096             0.4843
                          (5.134)**             (-0.1476)*       (0.2847)**           (-3.724)*            (-0.1383)            (1.293)             (0.3082)
** Indicates statistical significance at the 0.01 level
 * Indicates statistical significance at the 0.05 level
 Table 7
 Execution Costs
 Price impact results are presented for the 3-Year and 10-Year bonds both before and after the change
 in tick size for the 3-Year bond. The pre-event sample period extends from 10 February, 2009 to 10
 May, 2009. The post-event sample period extends from 13 May, 2009 to 13 August, 2009. The price
 impact of each trade is measured as the return from the transaction price ten trades prior to the trade
 price. The absolute value of each price impact is recorded to incorporate both buy and sell trades. This
 is averaged across each day (night) and then across each sample period. Each trade is classified into
 four mutually exclusive quartiles based on trade size. The first quartile contains the smallest 25% of
 trade-sizes and the fourth quartile contains the largest 25% of trade-sizes. Price impact is reported in
 basis points. Night trading results are in parentheses.

                        Quartile 1             Quartile 2           Quartile 3            Quartile 4
 Panel A - Pre-period
 3-Year                   0.5793                 0.5776              0.6195                 0.635
                          (1.058)                (1.118)             (1.118)               (1.230)
 10-Year                  0.5865                 0.6016              0.5960                0.5495
                          (1.213)                (1.221)             (1.188)               (1.223)
 Panel B - Post-period
 3-Year                      0.5836                 0.5234           0.6595                0.6585
                            (1.040)                (1.064)           (1.170)               (1.200)
 10-Year                     0.5535                 0.5387           0.5349                0.5086
                            (1.087)                (1.064)           (1.082)               (1.032)
 Panel C - Change from pre-period to post-period
 3-Year                      0.0043                -0.0542            0.0400*                0.0235
                           (-0.0178)              (-0.0545)           (0.0521)             (-0.0292)
 10-Year                    -0.0330              -0.0629**          -0.0611**              -0.0409*
                          (-0.1258)*            (-0.1565)**          (-0.1060)           (-0.1911)**
** Indicates statistical significance at the 0.01 level
 * Indicates statistical significance at the 0.05 level
 Table 8
 Execution Costs – Seasonal Control
 Price impact results are presented for the 3-Year and 10-Year bonds both before and after the change
 in tick size for the 3-Year bond. The pre-event sample period extends from 13 May, 2008 to 13
 August, 2008. The post-event sample period extends from 13 May, 2009 to 13 August, 2009. The price
 impact of each trade is measured as the return from the transaction price ten trades prior to the trade
 price. The absolute value of each price impact is recorded to incorporate both buy and sell trades. This
 is averaged across each day (night) and then across each sample period. Each trade is classified into
 four mutually exclusive quartiles based on trade size. The first quartile contains the smallest 25% of
 trade-sizes and the fourth quartile contains the largest 25% of trade-sizes. Price impact is reported in
 basis points. Night trading results are in parentheses.

                        Quartile 1             Quartile 2           Quartile 3            Quartile 4
 Panel A - Pre-period
 3-Year                   0.5011                 0.4952              0.5732                 0.5744
                         (0.9403)               (0.8967)            (0.9984)               (0.9574)
 10-Year                  0.6127                 0.5742              0.5711                 0.5703
                         (0.9215)               (0.8983)            (0.9297)               (0.8782)
 Panel B - Post-period
 3-Year                      0.5836                0.5234            0.6595                0.6585
                             (1.040)               (1.064)           (1.170)               (1.200)
 10-Year                     0.5535                0.5387            0.5349                0.5086
                             (1.087)               (1.064)           (1.082)               (1.032)
 Panel C - Change from pre-period to post-period
 3-Year                     0.0825**               0.0282           0.0863**              -0.0841**
                           (0.0994)*             (0.1669)**        (0.1715)**             (0.243)**
 10-Year                   -0.0592**              -0.0355*          -0.0362*              -0.0617**
                          (0.1652)**             (0.1661)**        (0.1518)**            (0.1539)**
** Indicates statistical significance at the 0.01 level
 * Indicates statistical significance at the 0.05 level

				
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