library(car)
## Loading required package: carData
library(urca)
library(forecast)
## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo
library(tseries)
library(highcharter)
## Highcharts (www.highcharts.com) is a Highsoft software product which is
## not free for commercial and Governmental use
library(readxl)
Divisa <- read_excel("C:/Users/user/Desktop/Divisa.xlsx")

Divisa

summary(Divisa)
##      Dólar     
##  Min.   :3707  
##  1st Qu.:3913  
##  Median :4052  
##  Mean   :4168  
##  3rd Qu.:4403  
##  Max.   :5107
Divisa<- ts(Divisa,frequency = 365 )
length(Divisa)
## [1] 318
autoplot(Divisa)

acf(Divisa, lag.max=30)

pacf(Divisa, lag.max=30)

adf.test(Divisa)
## 
##  Augmented Dickey-Fuller Test
## 
## data:  Divisa
## Dickey-Fuller = -1.8972, Lag order = 6, p-value = 0.6198
## alternative hypothesis: stationary
plot(ur.pp(diff(Divisa,12),type="Z-tau",
           model="constant", lags="long"))

class(Divisa)
## [1] "ts"
library(TSstudio)
ts_cor(diff(Divisa))
modelUS<-stats::arima(x = Divisa, order = c(7, 1, 7), fixed = c(NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA))
## Warning in log(s2): Se han producido NaNs
## Warning in stats::arima(x = Divisa, order = c(7, 1, 7), fixed = c(NA, NA, :
## possible convergence problem: optim gave code = 1
summary(modelUS)
## 
## Call:
## stats::arima(x = Divisa, order = c(7, 1, 7), fixed = c(NA, NA, NA, NA, NA, NA, 
##     NA, NA, NA, NA, NA, NA, NA, NA))
## 
## Coefficients:
##          ar1      ar2     ar3      ar4     ar5     ar6      ar7      ma1
##       0.7225  -0.3793  0.3068  -0.8204  0.2073  0.1167  -0.3319  -0.6126
## s.e.  0.4531   0.4116  0.3667   0.3642  0.5257  0.3191   0.2032   0.4590
##          ma2      ma3     ma4      ma5      ma6     ma7
##       0.2810  -0.2360  0.7325  -0.0330  -0.3463  0.2984
## s.e.  0.3546   0.3073  0.3117   0.4765   0.2691  0.1669
## 
## sigma^2 estimated as 1456:  log likelihood = -1607.24,  aic = 3244.48
## 
## Training set error measures:
##                    ME     RMSE      MAE        MPE      MAPE    MASE
## Training set 3.290853 38.10149 26.19017 0.06974477 0.6156137 1.05066
##                      ACF1
## Training set -0.006570241
BIC(modelUS)
## [1] 3300.866
et1<-residuals(modelUS) 
et1
## Time Series:
## Start = c(1, 1) 
## End = c(1, 318) 
## Frequency = 365 
##   [1]  3.981158e+00 -5.253720e-04  1.105422e-04 -2.615578e-04  9.363305e+01
##   [6] -1.132340e+01 -3.555407e+01 -5.975411e+00  1.384823e+01  2.330690e+00
##  [11]  6.052286e+00  1.724521e+01 -2.945048e+01 -4.786239e+01 -2.026150e+01
##  [16]  3.180540e+01 -1.197184e+01  1.011404e+01 -3.412287e+00  3.559173e+01
##  [21] -3.518263e+01 -2.250796e+01 -1.689275e+01  1.520584e+01  3.076876e-01
##  [26]  2.021704e+01  1.279649e+01 -4.998823e+01 -1.177643e+01  3.289757e+01
##  [31] -9.594648e+00  4.041305e+00 -2.621786e+01 -1.354433e+01  5.021293e-01
##  [36]  2.957915e+01  2.850753e+00 -1.043286e+00  2.667217e+00 -9.355905e+00
##  [41] -6.002867e+00 -2.008769e+01 -1.818366e+01  7.694760e+00  1.189810e+01
##  [46] -3.554516e+00  1.807115e+01  4.112639e+00  1.556373e+00 -1.864102e+01
##  [51] -1.931951e+01 -1.976435e+00  9.355898e+00  1.373111e+01  4.642439e+00
##  [56] -1.489380e+01  2.544191e+01 -4.844049e+01  1.226060e+00 -3.217621e+00
##  [61] -4.872063e-02 -4.127839e+00 -2.814366e+01 -9.133248e+01  4.160133e+01
##  [66] -7.032608e+00 -3.580457e+00  2.263697e+00 -2.268539e+01 -5.963371e+01
##  [71]  3.482574e+01  4.005943e+01 -5.739827e+00  1.096377e+01 -1.118471e+01
##  [76]  2.446324e+01 -1.646455e+01  2.169269e+00 -5.071354e+00  1.086285e+01
##  [81]  6.834136e+00 -1.117644e+01 -5.159999e+01 -2.274717e+00  2.746045e+01
##  [86] -8.734060e+00  5.224780e+00 -8.128891e+00  4.534501e+00 -2.840892e+01
##  [91] -2.340186e+01  1.788437e+01  2.025229e+01 -2.497704e+00  1.053285e+01
##  [96] -7.341718e+01  1.202532e+01  1.991782e+01  2.327584e+01 -8.055644e-01
## [101]  1.690631e+01 -8.652591e+00 -4.362040e+01  2.255486e+00 -1.529453e+00
## [106]  3.075247e+00  1.690984e+01 -1.073621e+00 -9.880175e+00 -6.973025e+00
## [111]  1.117773e+01 -7.348984e+00  1.258854e+01  5.879118e+01 -1.926916e+00
## [116]  1.140112e+01  1.062166e+02  5.831453e+00  2.538267e+01  1.379388e+01
## [121] -7.756237e+00  4.315564e+00  1.002709e+01  5.362366e+01  1.363599e+01
## [126]  4.306951e+01  2.271362e+01 -4.645585e+01 -7.192521e+00  2.992401e+00
## [131]  3.595403e+01  9.686650e+00  1.663112e+01  2.866355e+01 -6.836010e+00
## [136] -4.518260e+00 -1.254023e+01 -3.995426e+01 -2.516160e+01  2.667047e+01
## [141]  2.102098e+00 -5.455107e+01  5.721028e-02 -6.792289e+00 -5.092552e+01
## [146]  1.190925e+01 -1.108212e+01 -2.931687e+01 -2.146283e+01  1.152456e+01
## [151] -8.515781e+00 -3.747644e+00 -1.262606e+02  2.112840e+01 -2.066291e+01
## [156] -1.611092e+00 -1.452873e+01  6.155344e+00  2.005111e+01 -3.233853e+01
## [161] -1.001010e+01  5.188692e+01  6.911025e+01  8.240785e+00  1.478817e+01
## [166]  1.002757e+02 -4.171413e+01 -4.750715e+01 -7.499226e+00  1.287293e+01
## [171]  1.072219e+00  9.413498e+00  1.098771e+01  1.030355e+02 -2.962706e+01
## [176]  4.044110e+01  3.617189e+01  4.982884e+00  1.250108e+01  1.802152e+01
## [181] -1.501687e+01  5.746305e+01  2.347572e+01  5.042797e+01 -1.780222e+01
## [186] -9.467428e+00 -1.324130e+01  5.603251e+01  8.608379e+01  3.343973e+01
## [191]  3.141442e+01  7.988795e+00  2.231092e+00  1.185419e+02  1.100895e+02
## [196] -6.442805e+01 -2.285918e+01 -1.119316e+02  8.960699e+00 -8.191040e+00
## [201] -4.693598e+01 -1.075573e+00 -2.790192e+00  9.066238e+01 -3.971912e+01
## [206] -1.957953e+01 -7.698759e+00  3.523807e+01 -8.044137e+00  1.492054e+01
## [211] -2.776810e+01 -5.483556e+01  1.052819e+01 -1.653657e+01 -6.686549e+01
## [216]  6.330666e+01 -7.729171e+00 -7.555312e+01  6.843304e+01 -7.763296e+00
## [221]  8.434627e-01 -1.790200e+01  2.744152e+01 -4.362207e+01 -2.870354e+01
## [226] -4.070051e+01 -8.436140e+00 -5.017986e+00  5.678683e-01  1.434491e+01
## [231]  9.182693e+01  8.093636e+01 -3.586888e+01  1.015474e+01  8.823216e+00
## [236]  2.255916e+00  1.502387e+00  4.128162e+01  3.153853e+01 -1.452741e+01
## [241] -6.271139e+00 -3.037166e+01 -1.366242e+01  8.387970e+00  1.921319e+01
## [246]  5.903786e+01  7.917068e+00 -4.552638e-01  3.814068e+00 -1.552949e+00
## [251]  4.133732e+00 -2.186966e+01 -3.802743e+01 -3.005594e+01  1.059478e+01
## [256] -6.445152e+00 -2.905870e+01  7.107664e+01 -4.285739e+01  3.333247e+00
## [261]  2.906925e+01 -1.530154e+00 -7.124173e+00  4.871189e+00  1.551583e+01
## [266] -2.179814e+01 -4.976183e+00  4.299471e+01 -1.920832e+01  7.841530e+00
## [271]  5.914271e+01  4.169588e+01 -6.672032e+01  5.321213e+01  5.352294e+01
## [276]  1.313755e+01  3.972153e+00 -2.791941e+01 -4.976652e+01  5.890146e+01
## [281]  7.506172e+01 -2.608733e+01 -5.313380e+00  3.894119e+00 -1.317315e+01
## [286] -9.631728e+00  2.117279e+01  1.871715e+01  2.011856e+01  1.803381e+01
## [291] -9.709280e+00 -1.749583e+01  1.135562e+02  4.918482e+01  1.018856e+02
## [296]  4.551287e+01 -4.205872e+01  7.706761e+01 -3.267419e+01 -3.590243e+01
## [301] -5.290513e+01  5.045524e+01 -1.508618e+01  1.798252e+02 -6.507077e+00
## [306]  2.985883e+00 -3.627317e+01 -3.259934e+01  7.014367e+01 -1.037516e+02
## [311] -9.221588e+01  2.351937e+01 -1.787179e+01  4.050305e+01 -1.309141e+01
## [316]  1.128147e+02 -8.706329e+01  4.329545e+01
autoplot(et1)

fitted(modelUS)
## Time Series:
## Start = c(1, 1) 
## End = c(1, 318) 
## Frequency = 365 
##               x
##   [1,] 3977.179
##   [2,] 3981.161
##   [3,] 3981.160
##   [4,] 3981.160
##   [5,] 3989.117
##   [6,] 4095.433
##   [7,] 4077.914
##   [8,] 4045.285
##   [9,] 4029.612
##  [10,] 4041.129
##  [11,] 4037.408
##  [12,] 4026.215
##  [13,] 4041.100
##  [14,] 4017.942
##  [15,] 3970.661
##  [16,] 3961.845
##  [17,] 4005.622
##  [18,] 3983.536
##  [19,] 3997.062
##  [20,] 3997.778
##  [21,] 4039.133
##  [22,] 4003.308
##  [23,] 3981.193
##  [24,] 3949.094
##  [25,] 3963.992
##  [26,] 3957.293
##  [27,] 3974.524
##  [28,] 3997.818
##  [29,] 3955.816
##  [30,] 3949.702
##  [31,] 3992.195
##  [32,] 3978.559
##  [33,] 3968.948
##  [34,] 3937.154
##  [35,] 3927.548
##  [36,] 3922.381
##  [37,] 3959.829
##  [38,] 3963.723
##  [39,] 3960.013
##  [40,] 3973.196
##  [41,] 3971.413
##  [42,] 3959.398
##  [43,] 3935.934
##  [44,] 3909.825
##  [45,] 3905.622
##  [46,] 3921.075
##  [47,] 3920.899
##  [48,] 3942.767
##  [49,] 3962.164
##  [50,] 3971.901
##  [51,] 3946.570
##  [52,] 3929.226
##  [53,] 3917.894
##  [54,] 3913.519
##  [55,] 3927.758
##  [56,] 3928.684
##  [57,] 3914.758
##  [58,] 3959.080
##  [59,] 3909.414
##  [60,] 3913.858
##  [61,] 3910.329
##  [62,] 3905.748
##  [63,] 3891.094
##  [64,] 3863.102
##  [65,] 3764.509
##  [66,] 3813.143
##  [67,] 3809.690
##  [68,] 3811.146
##  [69,] 3809.865
##  [70,] 3806.064
##  [71,] 3751.174
##  [72,] 3787.581
##  [73,] 3833.380
##  [74,] 3816.676
##  [75,] 3812.035
##  [76,] 3812.097
##  [77,] 3843.355
##  [78,] 3814.261
##  [79,] 3825.741
##  [80,] 3809.807
##  [81,] 3813.836
##  [82,] 3831.846
##  [83,] 3817.270
##  [84,] 3758.915
##  [85,] 3771.440
##  [86,] 3794.394
##  [87,] 3780.435
##  [88,] 3793.789
##  [89,] 3781.165
##  [90,] 3794.369
##  [91,] 3771.552
##  [92,] 3738.146
##  [93,] 3754.538
##  [94,] 3777.288
##  [95,] 3764.257
##  [96,] 3780.367
##  [97,] 3711.765
##  [98,] 3726.592
##  [99,] 3748.554
## [100,] 3778.216
## [101,] 3760.504
## [102,] 3786.063
## [103,] 3787.780
## [104,] 3734.445
## [105,] 3738.849
## [106,] 3734.245
## [107,] 3720.410
## [108,] 3738.394
## [109,] 3747.200
## [110,] 3738.283
## [111,] 3744.672
## [112,] 3765.999
## [113,] 3746.951
## [114,] 3760.279
## [115,] 3820.997
## [116,] 3807.669
## [117,] 3825.523
## [118,] 3941.799
## [119,] 3941.937
## [120,] 3970.976
## [121,] 3974.026
## [122,] 3961.954
## [123,] 3956.243
## [124,] 3950.446
## [125,] 4002.704
## [126,] 4013.340
## [127,] 4063.366
## [128,] 4100.386
## [129,] 4061.123
## [130,] 4050.938
## [131,] 4049.806
## [132,] 4077.023
## [133,] 4063.689
## [134,] 4081.046
## [135,] 4117.366
## [136,] 4115.048
## [137,] 4123.070
## [138,] 4110.204
## [139,] 4059.012
## [140,] 4028.040
## [141,] 4048.778
## [142,] 4044.391
## [143,] 3989.783
## [144,] 3996.632
## [145,] 4001.276
## [146,] 3959.371
## [147,] 3970.132
## [148,] 3960.207
## [149,] 3933.803
## [150,] 3900.815
## [151,] 3920.856
## [152,] 3916.088
## [153,] 3902.781
## [154,] 3770.612
## [155,] 3805.643
## [156,] 3773.241
## [157,] 3786.159
## [158,] 3765.475
## [159,] 3779.449
## [160,] 3823.219
## [161,] 3792.660
## [162,] 3781.453
## [163,] 3843.400
## [164,] 3904.269
## [165,] 3897.722
## [166,] 3916.224
## [167,] 4021.014
## [168,] 3971.467
## [169,] 3919.649
## [170,] 3892.177
## [171,] 3903.978
## [172,] 3895.637
## [173,] 3894.062
## [174,] 3923.884
## [175,] 4056.147
## [176,] 4028.309
## [177,] 4093.698
## [178,] 4124.887
## [179,] 4117.369
## [180,] 4111.848
## [181,] 4104.737
## [182,] 4070.007
## [183,] 4127.734
## [184,] 4148.342
## [185,] 4216.572
## [186,] 4208.237
## [187,] 4212.011
## [188,] 4203.827
## [189,] 4262.596
## [190,] 4336.260
## [191,] 4356.856
## [192,] 4380.281
## [193,] 4386.039
## [194,] 4394.738
## [195,] 4517.371
## [196,] 4622.478
## [197,] 4542.509
## [198,] 4507.562
## [199,] 4386.669
## [200,] 4403.821
## [201,] 4362.346
## [202,] 4304.416
## [203,] 4306.130
## [204,] 4319.478
## [205,] 4463.579
## [206,] 4443.440
## [207,] 4431.559
## [208,] 4426.392
## [209,] 4453.054
## [210,] 4405.829
## [211,] 4403.278
## [212,] 4355.136
## [213,] 4289.772
## [214,] 4316.837
## [215,] 4312.855
## [216,] 4249.993
## [217,] 4338.879
## [218,] 4343.853
## [219,] 4268.847
## [220,] 4345.043
## [221,] 4336.437
## [222,] 4324.992
## [223,] 4282.248
## [224,] 4317.442
## [225,] 4260.154
## [226,] 4226.191
## [227,] 4193.926
## [228,] 4190.508
## [229,] 4184.922
## [230,] 4204.135
## [231,] 4224.643
## [232,] 4332.924
## [233,] 4436.119
## [234,] 4390.095
## [235,] 4391.427
## [236,] 4396.904
## [237,] 4372.948
## [238,] 4338.908
## [239,] 4376.411
## [240,] 4402.547
## [241,] 4394.291
## [242,] 4418.392
## [243,] 4399.792
## [244,] 4391.772
## [245,] 4403.557
## [246,] 4407.992
## [247,] 4458.813
## [248,] 4467.185
## [249,] 4462.916
## [250,] 4468.283
## [251,] 4475.966
## [252,] 4468.230
## [253,] 4434.717
## [254,] 4395.376
## [255,] 4354.725
## [256,] 4371.765
## [257,] 4375.969
## [258,] 4342.813
## [259,] 4432.657
## [260,] 4401.307
## [261,] 4406.771
## [262,] 4437.370
## [263,] 4442.964
## [264,] 4410.239
## [265,] 4404.864
## [266,] 4425.618
## [267,] 4384.776
## [268,] 4383.475
## [269,] 4445.678
## [270,] 4418.628
## [271,] 4437.847
## [272,] 4514.724
## [273,] 4553.660
## [274,] 4478.858
## [275,] 4537.017
## [276,] 4577.402
## [277,] 4586.568
## [278,] 4573.579
## [279,] 4534.507
## [280,] 4489.989
## [281,] 4552.548
## [282,] 4631.377
## [283,] 4610.603
## [284,] 4601.396
## [285,] 4618.463
## [286,] 4621.512
## [287,] 4590.007
## [288,] 4601.063
## [289,] 4616.711
## [290,] 4618.796
## [291,] 4646.539
## [292,] 4654.326
## [293,] 4630.484
## [294,] 4765.905
## [295,] 4821.744
## [296,] 4914.617
## [297,] 4960.159
## [298,] 4921.832
## [299,] 4980.814
## [300,] 4931.192
## [301,] 4874.825
## [302,] 4768.965
## [303,] 4834.506
## [304,] 4826.175
## [305,] 5026.507
## [306,] 5056.014
## [307,] 5097.273
## [308,] 5045.799
## [309,] 5036.856
## [310,] 5077.752
## [311,] 4894.096
## [312,] 4782.471
## [313,] 4782.782
## [314,] 4764.807
## [315,] 4858.771
## [316,] 4861.885
## [317,] 5027.823
## [318,] 4978.735
plot(scale(et1),type="l",main="Residuales")
abline(h=2*sqrt(var(scale(et1))),col="red",lty=2)
abline(h=-2*sqrt(var(scale(et1))),col="red",lty=2)

qqPlot(scale(et1))

## [1] 304 153
tsdiag(modelUS)

plot(forecast(modelUS,h=30, fan=T))
lines(fitted(modelUS), col="purple") 

pronostico1<-forecast(modelUS,h=60)
pronostico1
##          Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
## 1.871233       5027.388 4978.348 5076.428 4952.388 5102.388
## 1.873973       5006.397 4933.242 5079.553 4894.515 5118.279
## 1.876712       4999.505 4909.039 5089.972 4861.148 5137.862
## 1.879452       4923.717 4818.239 5029.194 4762.403 5085.031
## 1.882192       4892.941 4775.191 5010.691 4712.859 5073.024
## 1.884932       4895.596 4765.843 5025.350 4697.155 5094.038
## 1.887671       4873.779 4735.453 5012.105 4662.228 5085.330
## 1.890411       4904.093 4760.203 5047.984 4684.032 5124.155
## 1.893151       4950.781 4802.289 5099.272 4723.682 5177.879
## 1.895890       4951.205 4798.873 5103.536 4718.234 5184.176
## 1.898630       4983.113 4826.611 5139.616 4743.763 5222.464
## 1.901370       5001.454 4838.959 5163.950 4752.939 5249.970
## 1.904110       4967.287 4798.119 5136.456 4708.567 5226.008
## 1.906849       4965.544 4789.181 5141.908 4695.820 5235.269
## 1.909589       4952.166 4767.914 5136.418 4670.377 5233.956
## 1.912329       4908.806 4717.777 5099.834 4616.653 5200.958
## 1.915068       4917.436 4720.496 5114.376 4616.242 5218.630
## 1.917808       4921.910 4719.466 5124.354 4612.299 5231.521
## 1.920548       4909.109 4702.406 5115.813 4592.984 5225.235
## 1.923288       4944.748 4734.215 5155.282 4622.765 5266.731
## 1.926027       4959.669 4745.126 5174.212 4631.554 5287.784
## 1.928767       4950.505 4732.241 5168.768 4616.699 5284.310
## 1.931507       4975.983 4753.536 5198.430 4635.780 5316.186
## 1.934247       4968.207 4740.868 5195.546 4620.522 5315.892
## 1.936986       4942.284 4710.206 5174.363 4587.351 5297.217
## 1.939726       4953.340 4716.238 5190.442 4590.724 5315.956
## 1.942466       4935.884 4693.686 5178.082 4565.473 5306.294
## 1.945205       4916.769 4670.237 5163.301 4539.731 5293.807
## 1.947945       4938.642 4687.939 5189.345 4555.225 5322.059
## 1.950685       4932.531 4677.876 5187.186 4543.069 5321.993
## 1.953425       4930.127 4672.106 5188.148 4535.518 5324.736
## 1.956164       4959.373 4697.840 5220.905 4559.393 5359.352
## 1.958904       4951.925 4686.773 5217.077 4546.410 5357.440
## 1.961644       4947.826 4679.235 5216.416 4537.052 5358.599
## 1.964384       4966.261 4693.804 5238.718 4549.574 5382.948
## 1.967123       4946.385 4669.926 5222.844 4523.578 5369.193
## 1.969863       4937.698 4657.484 5217.912 4509.148 5366.248
## 1.972603       4950.645 4666.450 5234.840 4516.006 5385.283
## 1.975342       4930.646 4642.660 5218.632 4490.210 5371.083
## 1.978082       4930.746 4639.381 5222.111 4485.141 5376.351
## 1.980822       4948.892 4654.044 5243.739 4497.961 5399.822
## 1.983562       4934.969 4636.850 5233.088 4479.036 5390.902
## 1.986301       4942.733 4641.576 5243.889 4482.154 5403.312
## 1.989041       4959.354 4654.897 5263.810 4493.728 5424.980
## 1.991781       4942.650 4634.966 5250.334 4472.088 5413.212
## 1.994521       4948.493 4637.650 5259.335 4473.100 5423.885
## 1.997260       4956.976 4642.672 5271.279 4476.290 5437.662
## 2.000000       4936.091 4618.467 5253.714 4450.328 5421.854
## 2.002740       4942.255 4621.433 5263.076 4451.601 5432.909
## 2.005479       4948.337 4624.154 5272.520 4452.542 5444.132
## 2.008219       4930.773 4603.493 5258.054 4430.242 5431.305
## 2.010959       4942.787 4612.544 5273.030 4437.724 5447.850
## 2.013699       4949.657 4616.317 5282.997 4439.858 5459.457
## 2.016438       4935.712 4599.497 5271.928 4421.516 5449.909
## 2.019178       4950.038 4610.965 5289.112 4431.471 5468.606
## 2.021918       4952.950 4610.830 5295.070 4429.723 5476.178
## 2.024658       4938.130 4593.132 5283.128 4410.501 5465.759
## 2.027397       4950.809 4602.890 5298.727 4418.713 5482.904
## 2.030137       4948.652 4597.664 5299.640 4411.863 5485.442
## 2.032877       4934.414 4580.571 5288.257 4393.257 5475.571
autoplot(pronostico1)

En un contexto mundial de alta inflación y escasez de recursos e interrupciones en la cadena de suministro por cuenta de la guerra de Rusia en Ucrania, la devaluación del peso colombiano y otras monedas es una realidad.

Los operadores de mercado van a sentir un efecto de crecimiento en la demanda por parte de unos individuos que se pueden sentir nerviosos ante el nuevo presidente y sus políticas económicas.

En los últimos días hemos tenido una composición mixta en la que los bancos centrales siguen agresivos anunciando más subidas de tasas de interés, esto genera que el dólar se fortalezca frente al grueso de monedas internacionales, tanto las de países desarrollados como las de los emergentes.

Un análisis sobre el impacto estimado de una supuesta suspensión de nuevas exploraciones de hidrocarburos a partir de 2023 y hasta 2027, el peso colombiano se devaluaría entre 39,9% y 43,7% para 2027 y la tasa de cambio se situaría entre 5.080 y 7.000.

El dólar sigue alcanzando su máximo en Colombia ya que nuevamente muchos pesos se están convirtiendo en dólares, básicamente por la subida de tasas de interés de EE.UU. Además varios inversionistas han preferido irse del país por inestabilidades económicas futuras y refugiarse en otros países más estables.

Hay contratos en el mercado de futuros en Colombia que se están negociando en $5.200. Sobre eso lo que están haciendo los inversionistas es realizando adquisiciones bajo esos compromisos.

La tasa promedio de negociación del dólar en Colombia se situó sobre los 4.968,75 pesos este lunes, 55,5 pesos por encima del precio oficial del la divida estadounidense de 4.913,24 peso 2022/10/24.

El dólar, en el mercado interbancario colombiano alcanzó ha llegar al nivel récord de 4.999 pesos durante la mañana. Para el mediodía de hoy, el precio promedio era de 4.965,56 y cerró la jornada en 4.967,09 pesos.

Un día después de aprobada la reforma tributaria, el mercado registró una sesión de alta volatilidad con fuertes bajas y alzas

De acuerdo con la Bolsa de Valores de Colombia, la moneda extranjera cerró en un precio promedio de $5.069, es decir, subió 11 pesos frente a la TRM del día que fue de $5.058, un día después de que el Congreso aprobara la reforma tributaria.

La cotización del dólar en Colombia para el día Viernes 4 de Noviembre del 2022 subió 42.18 Pesos, correspondiente a un aumento del 0.84% con respecto al día anterior.

La devaluación del peso frente al dólar ha sido explicada hasta la saciedad por los expertos: se conjugan la subida de las tasas de interés en Estados Unidos, la crisis post pandemia con los insumos y el nerviosismo por lo que ocurre con el gobierno colombiano. La moneda colombiana es una de las más devaluadas del mundo y a esta crisis se le suma la inflación.