If the response variable is in the units in which you are interested (annual performance percentage), then what you really want to do is to take advantage of the intercept in your regression model. As one comment notes, the regression coefficient for UK per se is 0, by construction, if that is the reference category.
With the treatment contrasts that you seem to be using to do your analysis (comparing all levels of each categorical predictor against a reference category), that intercept will be the value of the response variable when all predictor categories are at their reference level and all continuous predictors are at 0. In particular, it will represent that situation specifically for UK funds. (The 0 coefficient for UK means you add 0 to the intercept to get the value for UK.)
You can then use the regression coefficients to add in the contributions from all the other predictors to get the response value for UK under other combinations of predictor values. For error estimates you incorporate information from the covariance matrix of the regression coefficients, using the formula for the variance of a sum of correlated variables.
This assumes, however, that there is no interaction term involving your categorical variable
country. If there is, then your interpretation of the 3% coefficient for Germany is incomplete: it represents the difference between Germany and UK only at the reference values of all other categorical variables and at 0 values of all continuous variables. You must also add in the contributions of all the interaction terms to compare Germany and UK in any other scenario.