Pattern causality matrix
The DJS data includes 29 stock price series, which is large enough for our analysis.
Then we could estimate the pattern causality in this dataset by
pcMatrix function.
dataset <- DJS[,-1] # remove the date column
result <- pcMatrix(dataset, E = 3, tau = 1, metric = "euclidean", h = 2, weighted = TRUE)Then we could see the three matrixs after calculating.
head(result$positive)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] NA 0.4464555 0.3686338 0.4334828 0.4497469 0.4185127 0.4137931
#> [2,] 0.4135754 NA 0.3552519 0.4511914 0.4284627 0.3827940 0.4149026
#> [3,] 0.3920266 0.4120172 NA 0.3998358 0.4099279 0.3393939 0.3786247
#> [4,] 0.4114420 0.4368088 0.3821340 NA 0.4387443 0.3498717 0.4031852
#> [5,] 0.4311008 0.4414003 0.4054487 0.4507148 NA 0.4685803 0.4017642
#> [6,] 0.3847981 0.3716814 0.3782051 0.3813694 0.4300077 NA 0.3394415
#> [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,] 0.3973064 0.4672897 0.3859504 0.4559156 0.4193548 0.4021036 0.4080505
#> [2,] 0.3819320 0.4179343 0.3576105 0.4190476 0.4506627 0.4022169 0.4017642
#> [3,] 0.2782609 0.3852107 0.3427386 0.3776824 0.3878357 0.3935644 0.3949247
#> [4,] 0.3473590 0.4079602 0.3453947 0.4336283 0.4123879 0.3575655 0.3755069
#> [5,] 0.3725000 0.4882353 0.4197719 0.4362205 0.4740061 0.4344718 0.4030327
#> [6,] 0.3746702 0.3904221 0.5105802 0.3807947 0.4021824 0.3965658 0.3848168
#> [,15] [,16] [,17] [,18] [,19] [,20] [,21]
#> [1,] 0.3995253 0.4121014 0.4221840 0.3377897 0.3647364 0.4228243 0.3413422
#> [2,] 0.3537937 0.4459459 0.3902439 0.3885516 0.3686306 0.3764706 0.3641869
#> [3,] 0.3049956 0.3900245 0.3573854 0.2811355 0.3933764 0.3582215 0.2842960
#> [4,] 0.3682403 0.3904221 0.3144816 0.3555556 0.3424296 0.3800648 0.3422645
#> [5,] 0.3213028 0.4351563 0.3818636 0.3592073 0.3909014 0.3858203 0.3493151
#> [6,] 0.3670782 0.3573123 0.3670569 0.3561888 0.3259494 0.3466440 0.3212389
#> [,22] [,23] [,24] [,25] [,26] [,27] [,28]
#> [1,] 0.3581197 0.4031373 0.4038462 0.4668630 0.3491525 0.3667482 0.3673820
#> [2,] 0.3474218 0.4297386 0.4132997 0.4375000 0.3766122 0.3549518 0.3470437
#> [3,] 0.2729659 0.3705933 0.3204904 0.3851295 0.3144816 0.3289474 0.2942255
#> [4,] 0.3417722 0.3878583 0.3823287 0.4591915 0.3112640 0.3524229 0.3477157
#> [5,] 0.3142123 0.4148936 0.3838631 0.4982505 0.3713178 0.3520140 0.3539604
#> [6,] 0.3312388 0.3469055 0.3327815 0.3809148 0.3279797 0.3393162 0.3135739
#> [,29]
#> [1,] 0.4131642
#> [2,] 0.4271318
#> [3,] 0.3732681
#> [4,] 0.4026080
#> [5,] 0.3933447
#> [6,] 0.3718506Then we can visualize the result by plotMatrix
function.
- positive status
- negative status
- dark status
We could see that there is a obvious positive connection in this system.
