Help interpreting the results of a Granger Causality test I am still learning how to interpret the results of a Granger causality test.
Granger Causality Test: Y = f(X)
Model   Res.DF  Diff. DF    F   p-value
Complete model  1000            
Reduced model   1001    -1  9.98656377696739    0.00162424264659028

Granger Causality Test: X = f(Y)
Model   Res.DF  Diff. DF    F   p-value
Complete model  1000            
Reduced model   1001    -1  61.7745391599339    9.92054758804637e-15

My interpretation is that in both tests the null hypothesis is not rejected which means that both variables are likely to cause each other. Although the 2nd test is more likely to be casual because of the significantly smaller p-value. Can anyone please help me figure out if I am correct or incorrect?

P-values are very low in both cases, so $H_0$ of no Granger causality is rejected. That is, there seems to be Granger causality in both cases. The evidence is pretty strong in both cases.
