This data set is the daily closing price of bitcoin from the 27th of April 2013 to the 3rd of March 2018 and sourced from coinmarketcap.

The series shows multiple micro trends ands lots of periods where the volatility is high and low. Possible existence of an ARCH effect. No sign of seasonality and observations are bouncing around particular mean levels.

We see obvious volatility, no sign of trend or seasonality

Test is highly significant

GARCH(1,1) has all significant coefficeints.

Call:
garch(x = r.bitcoin, order = c(1, 1), trace = FALSE)

Model:
GARCH(1,1)

Residuals:
     Min       1Q   Median       3Q      Max 
-8.55811 -0.33740  0.06798  0.53194  4.76716 

Coefficient(s):
    Estimate  Std. Error  t value Pr(>|t|)    
a0 3.632e-05   3.816e-06    9.518   <2e-16 ***
a1 1.485e-01   9.555e-03   15.546   <2e-16 ***
b1 8.522e-01   7.025e-03  121.310   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Diagnostic Tests:
    Jarque Bera Test

data:  Residuals
X-squared = 5626.9, df = 2, p-value < 2.2e-16


    Box-Ljung test

data:  Squared.Residuals
X-squared = 1.7522, df = 1, p-value = 0.1856

         df       AIC
model.11  3 -6633.729
model.22  5 -6604.584
model.33  7 -6597.135