The SAS System cond Obs symbol permno date seqno time BBid BOfr price size ex cond corr Flag g127 1 ESSX 93230 20100901 2 9:25:28 4.23 6.09 . . . . . 2 ESSX 93230 20100901 3 9:30:00 4.23 5.20 . . . . . 3 ESSX 93230 20100901 4 9:59:19 4.76 5.20 . . . . . 4 ESSX 93230 20100901 5 10:01:30 4.76 5.20 5.20 100 Z 0 0 0 5 ESSX 93230 20100901 6 10:01:32 4.40 5.20 . . . . . 6 ESSX 93230 20100901 7 10:02:08 4.23 5.20 . . . . . 7 ESSX 93230 20100901 8 12:08:20 4.23 5.01 4.31 100 Q @F 0 0 0 8 ESSX 93230 20100901 10 12:08:20 4.23 5.01 4.31 100 Q @F 0 0 0 9 ESSX 93230 20100901 12 12:08:20 4.23 5.01 4.26 100 P @F 0 0 0 10 ESSX 93230 20100901 13 12:08:46 4.26 5.01 . . . . . The SAS System daily cond closing Obs Vol symbol permno date seqno time BBid BOfr price size ex cond corr Flag g127 Price 1 9284 ESSX 93230 20100901 94 16:10:00 3.23 6.41 . . . . . 4.820 2 14719 ESSX 93230 20100902 92 15:59:49 4.90 5.00 . . . . . 4.950 3 800 ESSX 93230 20100903 143 16:00:14 4.18 5.20 . . . . . 4.690 4 1002 ESSX 93230 20100907 39 16:00:00 4.44 5.20 5.1 100 Q M 0 0 0 4.820 5 1832 ESSX 93230 20100908 22 16:00:08 4.57 5.20 . . . . . 4.885 6 1100 ESSX 93230 20100909 19 16:00:01 4.60 5.20 . . . . . 4.900 7 5700 ESSX 93230 20100910 24 16:00:01 4.60 5.20 . . . . . 4.900 8 23900 ESSX 93230 20100913 184 16:00:16 4.62 5.99 . . . . . 5.305 9 2900 ESSX 93230 20100914 44 16:00:16 4.48 6.17 . . . . . 5.325 10 6661 ESSX 93230 20100915 93 16:00:16 4.61 6.22 . . . . . 5.415 The SAS System Plot of closingPrice*date. Legend: A = 1 obs, B = 2 obs, etc. closingPrice | | 6.0 + | | | | A AAA | AAA 5.5 + A A A | AA A AA A | B | A | A A AA A | A A A A A A B AA 5.0 + BA B A A A A A | A AAA A A A A A A | A A A A AAA A A | A A A A A | A A AAA A | A 4.5 + A A | | A | A A A | A | A 4.0 + | | | | A | 3.5 + | ---+------------+------------+------------+------------+------------+------------+------------+-- 20100826 20100915 20101005 20101025 20101114 20101204 20101224 20110113 Transaction Date The SAS System The ARIMA Procedure Name of Variable = closingPrice Mean of Working Series 4.97375 Standard Deviation 0.385307 Number of Observations 84 Autocorrelations Lag Covariance Correlation -1 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 1 Std Error 0 0.148461 1.00000 | |********************| 0 1 0.111025 0.74784 | . |*************** | 0.109109 2 0.113087 0.76173 | . |*************** | 0.158810 3 0.090601 0.61027 | . |************ | 0.197574 4 0.083421 0.56190 | . |*********** | 0.218867 5 0.067941 0.45763 | . |********* | 0.235415 6 0.053975 0.36357 | . |******* . | 0.245778 7 0.047378 0.31913 | . |****** . | 0.252099 8 0.034977 0.23560 | . |***** . | 0.256863 9 0.026575 0.17901 | . |**** . | 0.259423 10 0.019318 0.13012 | . |*** . | 0.260889 11 0.017163 0.11561 | . |** . | 0.261661 12 0.0077603 0.05227 | . |* . | 0.262268 13 0.0042795 0.02883 | . |* . | 0.262392 14 0.00072251 0.00487 | . | . | 0.262430 15 0.0023523 0.01584 | . | . | 0.262431 16 -0.0049724 -.03349 | . *| . | 0.262442 17 0.0013076 0.00881 | . | . | 0.262493 18 -0.0022983 -.01548 | . | . | 0.262496 19 -0.0045883 -.03091 | . *| . | 0.262507 20 -0.0051380 -.03461 | . *| . | 0.262551 21 -0.011518 -.07758 | . **| . | 0.262605 "." marks two standard errors The SAS System The ARIMA Procedure Autocorrelation Check for White Noise To Chi- Pr > Lag Square DF ChiSq --------------------Autocorrelations-------------------- 6 192.91 6 <.0001 0.748 0.762 0.610 0.562 0.458 0.364 12 214.08 12 <.0001 0.319 0.236 0.179 0.130 0.116 0.052 18 214.34 18 <.0001 0.029 0.005 0.016 -0.033 0.009 -0.015 Augmented Dickey-Fuller Unit Root Tests Type Lags Rho Pr < Rho Tau Pr < Tau F Pr > F Zero Mean 0 0.0710 0.6967 0.15 0.7263 1 0.1116 0.7061 0.42 0.8023 2 0.1334 0.7113 0.48 0.8164 3 0.1380 0.7123 0.52 0.8248 Single Mean 0 -18.1445 0.0135 -2.99 0.0398 4.55 0.0593 1 -5.2884 0.3976 -1.40 0.5807 1.11 0.7888 2 -6.7120 0.2825 -1.56 0.4986 1.39 0.7184 3 -6.6956 0.2835 -1.49 0.5337 1.30 0.7408 Trend 0 -20.9021 0.0428 -3.30 0.0727 5.55 0.0958 1 -6.9114 0.6563 -1.72 0.7321 1.76 0.8251 2 -8.2652 0.5430 -1.82 0.6877 1.80 0.8175 3 -8.1904 0.5488 -1.73 0.7277 1.66 0.8446 Name of Variable = closingPrice Period(s) of Differencing 1 Mean of Working Series 0.010602 Standard Deviation 0.262723 Number of Observations 83 Observation(s) eliminated by differencing 1 The SAS System The ARIMA Procedure Autocorrelations Lag Covariance Correlation -1 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 1 Std Error 0 0.069023 1.00000 | |********************| 0 1 -0.037124 -.53784 | ***********| . | 0.109764 2 0.021607 0.31304 | . |****** | 0.137908 3 -0.014966 -.21683 | . ****| . | 0.146219 4 0.0096599 0.13995 | . |*** . | 0.150043 5 -0.0023887 -.03461 | . *| . | 0.151608 6 -0.0073320 -.10623 | . **| . | 0.151703 7 0.0067565 0.09789 | . |** . | 0.152596 8 -0.0031621 -.04581 | . *| . | 0.153351 9 0.00058853 0.00853 | . | . | 0.153516 10 -0.0067375 -.09761 | . **| . | 0.153521 11 0.0059910 0.08680 | . |** . | 0.154267 12 -0.0033000 -.04781 | . *| . | 0.154855 13 -0.0005869 -.00850 | . | . | 0.155032 14 -0.0045611 -.06608 | . *| . | 0.155038 15 0.0050020 0.07247 | . |* . | 0.155377 16 -0.0092981 -.13471 | . ***| . | 0.155784 17 0.010012 0.14506 | . |*** . | 0.157181 18 -0.0013251 -.01920 | . | . | 0.158786 19 -0.0018005 -.02608 | . *| . | 0.158814 20 0.0052304 0.07578 | . |** . | 0.158865 "." marks two standard errors Autocorrelation Check for White Noise To Chi- Pr > Lag Square DF ChiSq --------------------Autocorrelations-------------------- 6 40.46 6 <.0001 -0.538 0.313 -0.217 0.140 -0.035 -0.106 12 43.44 12 <.0001 0.098 -0.046 0.009 -0.098 0.087 -0.048 18 48.64 18 0.0001 -0.009 -0.066 0.072 -0.135 0.145 -0.019