One way that this could happen is to put two highly correlated predictors into your model. Because they are highly correlated, each of their coefficients will have relatively wide standard errors. As a result, these coefficients may not be statistically significant separately. Taken together, they may be a strong predictor of the outcome.
The covariance between the coefficient estimates would be pretty negative (increasing the estimate of one of the coefficients leads to a strong decrease in the estimate of the other coefficient because the two are highly linearly related), cancelling out the high variances for the estimators separately. As a result, your model has predictive power and your regressors are important, but their colinearity obscures this fact by creating large (though accurate) standard errors.