Well, I'm not sure that merge would be the way to go. Personally I would build a new data frame by creating an index of the dates and then constructing the columns using list comprehensions. Possibly not the most pythonic way, but it seems to work for me!
import pandas as pd
import numpy as np
df1 = pd.DataFrame(np.random.randn(5,3), index=pd.date_range('01/02/2014',periods=5,freq='D'), columns=['a','b','c'] )
df2 = pd.DataFrame(np.random.randn(8,3), index=pd.date_range('01/01/2014',periods=8,freq='D'), columns=['a','b','c'] )
# Create an index list from the set of dates in both data frames
Index = list(set(list(df1.index) + list(df2.index)))
Index.sort()
df3 = pd.DataFrame({'df1': [df1.loc[Date, 'c'] if Date in df1.index else np.nan for Date in Index],\
'df2': [df2.loc[Date, 'c'] if Date in df2.index else np.nan for Date in Index],},\
index = Index)
df3