Commodity prices
Commodity prices on successive business days, Chicago Exchange These data exhibit classic randow walk behavior.
data(Commodities)
The format is: List of 5 $ gold:'data.frame': 97 obs. of 3 variables: ..$ close: num [1:97] 700 671 680 677 690 ... ..$ high : num [1:97] 714 698 683 682 692 ... ..$ low : num [1:97] 700 669 664 676 684 ... $ feed:'data.frame': 95 obs. of 3 variables: ..$ close: num [1:95] 79 79 78.6 79.9 79.3 ... ..$ high : num [1:95] 80 79.5 79.2 79.9 79.8 ... ..$ low : num [1:95] 79 78.5 78.6 78.8 79.3 ... $ port:'data.frame': 99 obs. of 3 variables: ..$ close: num [1:99] 57.7 56.8 57.5 57 59 ... ..$ high : num [1:99] 59.9 57.5 58 58.1 59 ... ..$ low : num [1:99] 57.2 56.4 55.1 56.8 56.4 ... $ soy :'data.frame': 99 obs. of 3 variables: ..$ close: num [1:99] 766 790 804 794 824 ... ..$ high : num [1:99] 788 791 805 808 824 ... ..$ low : num [1:99] 764 764 778 792 809 ... $ us :'data.frame': 100 obs. of 3 variables: ..$ close: num [1:100] 91.6 91.6 91.4 91.4 91.2 ... ..$ high : num [1:100] 91.9 91.7 91.6 91.4 91.5 ... ..$ low : num [1:100] 91.6 91.5 91.3 91.3 91.1 ...
Data from 1981. feed: April; gold: June, pork: March, us: March
I obtained these data from a broker.
dim(Commodities$gold) dimnames(Commodities$gold)[[2]] TimeSeriesPlot(Commodities$gold$close)
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