Following the introduction of SPDR options, any inferences on improvements in the contribution made to price discovery by SPDRs may well be affected by changes in market liquidity over the sample period. Therefore, we follow Bollen and Whaley (1998) to adopt a dummy variable, along with trading volume and market volatility, all of which are employed as control variables in order to determine the improvements in the market liquidity of SPDRs as a direct result of the introduction of SPDR options.
The regression results are shown in Table VII, which depicts that all of the coefficients on the dummy variable are significantly positive, indicating that the market liquidity of SPDRs is significantly enhanced in all the four venues as a result of the introduction of SPDR options. Furthermore, the impacts on the market liquidity of SPDRs from both trading volume and mar-ket volatility are found to be consistent with the arguments of Bollen and Whaley (1998), in that greater price variability or a lower trading volume results in a lower MQI.
This study infers that improvements in the contribution made by SPDRs to price discovery are caused by the increase in market liquidity as a direct result of the introduction of SPDR options. Details on the relationship that
TABLE VII
Regression Analyses of Market Liquidity for SPDRs
Variables AMEX Island ArcaEx NASDAQ
DOpt 0.317*** 0.299*** 0.332*** 0.203***
(4.724) (7.376) (7.555) (2.817)
Log (Vol) 0.094 0.099* 0.144*** 0.093*
(1.296) (1.820) (2.781) (1.868)
Volatility 2.692*** 1.990*** 1.829*** 0.923
(3.815) (4.890) (4.252) (1.185)
Constant 1.569** 3.875*** 3.432*** 2.448***
(2.073) (6.129) (6.071) (4.518)
Adjusted R2 0.187 0.400 0.556 0.118
Note. Following the introduction of SPDR options, the changes in the MQI are tested based on the following regression model:
where t denotes the daily time interval, MQItrefers to the SPDR market quality index during trading day t, is a dummy variable that is equal to 0 for options in the pre-listing period, otherwise 1, Voltis the SPDR trading volume during trading day t and stis the Parkinson (1980) extreme value estimator that proxies for the volatility of the S&P 500 index market. The Newey and West (1987) procedure is used to calculate the consistent standard errors of the regression parameter estimates under a serially correlated and heteroskedastic error process. Figures in parentheses are t-statistics. ***indicates the significance of the traditional t-test at the 1%
level; **indicates significance at the 5% level and *indicates significance at the 10% level.
DOptt
log(MQIt) a0 a1DOptt a2log(Volt) a3st et
24 Chen and Chung
Journal of Futures Markets DOI: 10.1002/fut
exists between price discovery and the MQI based on the regression analysis are presented in Table VIII. The results of Model (1) in Table VIII-based upon Equation (10)–reveal that the coefficients on are all positive, thereby indicating a clear increase in the contribution made to price discov-ery by SPDRs as a result of the introduction of SPDR options.
Relative to all the other trading venues, the ArcaEx ECN is found to be dominant in the price-discovery process, since the results show that the coeffi-cient on the dummy variable is significantly positive for ArcaEx, thereby imply-ing that the contribution made by SPDRs to price discovery increases as a result of the introduction of SPDR options.
In order to provide support for the argument that this improvement in the contribution of the SPDRs to price discovery is caused by enhancements to market liquidity, the MQI is inserted into Equation (10) to obtain Equation (11). Model (2) in Table VIII shows that the coefficients on the MQI variable reveal significant explanatory power offsetting the effect of the dummy variable on the price discovery measures, especially for the ArcaEx ECN. In addition, the new regression models, with the addition of the MQI variable, almost always present insignificant constant terms and higher adjusted R2values than the original regression models. The results listed in Tables VII and VIII clearly demonstrate that the introduction of SPDR options results in improved liquid-ity within the SPDR market, which in turn leads to a substantial rise in the contribution made by SPDRs to the overall process of price discovery. As Bloomfield et al. (2005) point out, results such as these also raise the possibil-ity that informed traders provide more liquidpossibil-ity after the introduction of SPDR options.
The coefficients on the volatility variable are found to be negative, and nearly attain significance in Table VIII, a finding which indicates that informed traders have a preference for trading on the E-mini futures market during peri-ods of high volatility. These results can be seen as providing support for the leverage hypothesis proposed by Kawaller et al. (1987) where during periods of high volatility, informed traders have a preference for using high leverage instruments.
An additional advantage of E-mini futures—the fact that these instru-ments can be traded on an almost 24-hour basis—may also represent a strong attraction for informed traders to trade in the E-mini futures market during periods of high volatility, since this feature offers them the ability to adjust their position at any time. In contrast with Chakravarty et al. (2004), this finding stresses the importance of the leverage hypothesis on the analysis of price dis-covery in high-volatility periods.
DOptt
706
TABLE VIII Regression Analyses of Price Discovery for SPDRs AMEXIslandArcaExNASDAQ Model (1)Model (2)Model (1)Model (2)Model (1)Model (2)Model (1) A: Common factor (PT) model 0.042**0.0290.0170.0140.0090.046*0.116*** (2.306)(1.360)(0.958)(0.571)(0.638)(1.736)(6.304) Volt/Volt1)0.076**0.064**0.046**0.040**0.058***0.041**0.018 0.017 (2.478)(2.058)(2.544)(2.334)(3.196)(2.036)(1.092) 0.525**0.3831.438***1.270***0.951***0.731***1.368*** (2.022)(1.391)(5.463)(4.747)(3.959)(2.784)(6.376) MQI)–0.044–0.095*–0.140**– –(1.295)–(1.928)–(2.286)– 0.283***0.167*0.717***0.2390.773***0.0660.318*** (10.531)(1.810)(28.876)(0.975)(35.222)(0.212)(13.236) R20.0330.0370.0820.1100.0420.0750.196 0.026***0.022**0.0010.0240.036*0.0350.101*** (2.639)(2.148)(0.034)(0.753)(1.696)(0.888)(7.975) Volt/Volt1)0.047**0.044**0.068***0.063***0.101***0.079**0.019 (2.407)(2.174)(2.772)(2.668)(3.174)(2.309)(1.442) 0.242**0.201*1.059***0.924***0.917***0.632*0.465*** (2.178)(1.814)(3.467)(2.877)(2.621)(1.692)(3.120) MQI)–0.013–0.076–0.181**– –(0.794)–(1.239)–(2.078)– 0.069***0.0350.473***0.0900.539***0.3760.089*** (5.814)(0.825)(14.947)(0.291)(15.681)(0.854)(5.932) R20.0360.0360.0280.0420.0270.0470.194 (Continued
TABLE VIII (Continued) AMEXIslandArcaExNASDAQ Panel C: Modified information share (MIS) model DOpt0.026***0.022**0.0010.0240.036*0.035 0.100***0.099*** (2.633)(2.143)(0.039)(0.751)(1.697)(0.885)(7.931)(7.397) Log (Volt/Volt1)0.047**0.044**0.068***0.063***0.102***0.080**0.019 0.019 (2.407)(2.174)(2.762)(2.657)(3.176)(2.313)(1.438)(1.407) Volatility0.242**0.200*1.067***0.931***0.929***0.642*0.464***0.462*** (2.177)(1.813)(3.473)(2.882)(2.629)(1.702)(3.115)(3.063) Log (MQI)–0.013–0.077–0.182**–0.006 –(0.793)–(1.241)–(2.075)–(0.393) Constant0.069***0.0350.473***0.0870.540***0.3820.089***0.070 (5.810)(0.825)(14.856)(0.280)(15.553)(0.860)(5.922)(1.281) Adjusted R20.0360.0360.0280.0420.0270.0470.1920.191 Note:Following the introduction of SPDR options, the changes in the contribution of SPDRs to price discovery are tested based on the following regression model (Equation 10): where tindicates the daily time interval, PDtrefers to the daily share of information for SPDRs measured by the common factor (PT), information share (IS) and modified information share (MIS) models for SPDR trades on an venue and compared with E-mini futures prices, is a dummy variable that is equal to 0 for options in the pre-listing period, otherwise 1; Log(Volt/Volt1) is the rate of change in trading volume for SPDRs during trading day tand stis the Parkinson (1980) extreme value estimator that proxies for the volatility of the S&P500 index market. In order to provide support for our argument that the improvement in the contribution of SPDRs to price discovery is caused by enhancements to market quality, the MQI is added into the above equation and defined as follows (Equation 11): where MQItrefers to the SPDR market quality index during trading day t. Model (2) is estimated by using the two-stage least-squares (2SLS) approach, which uses the lagged MQI, lagged market volatility and the previous day’s trading volume as the instrument variables for the MQI. The Newey and West (1987) procedure is used to calculate the consistent standard errors of the regression parameter estimates under a serially correlated and heteroskedastic error process. Figures in parentheses are t-statistics. ***indicates the significance of the traditional t- test at the 1% level; **indicates significance at the 5% level and *indicates significance at the 10% level.
PDtb0 b1D
Opt t
b2log(VoltyVolt1) b3st b4log(MQIt) et
DOpt t
PDtb0 b1D
Opt t
b2log(VoltyVolt1) b3st et
5. CONCLUSIONS
This study examines the impact of the introduction of SPDR options on the contribution to price discovery made by SPDRs. Consistent with the findings of Kumar et al. (1998) and Chakravarty et al. (2004), we find that the introduc-tion of SPDR opintroduc-tions has improved the liquidity of SPDRs, which has further reduced their implicit trading costs. According to the ‘transaction cost’ hypoth-esis of Fleming et al. (1996), those securities with lower trading costs make a higher contribution to price discovery. We therefore argue that following the introduction of SPDR options, the major benefit for market participants from the improvement in market liquidity has led to a reduction in the implicit trad-ing costs, which in turn, may have induced a greater contribution to price dis-covery by SPDRs.
Furthermore, when comparing only the contributions to price discovery made by SPDRs and E-mini futures, the SPDRs traded on ArcaEx are found to make a contribution of about 50% to the price-discovery process. The empirical results also indicate that informed traders have a preference for trading on the E-mini futures market during periods of high volatility, thereby highlighting the importance of the leverage hypothesis for the analysis of price discovery in high-volatility periods. Overall, these findings imply that developments in the derivatives markets can lead to improvements in market quality for the under-lying securities in terms of both liquidity and price discovery.
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