Whenever we make a prediction we will make an error of a varying degree. What is worse,positive errors or negative ones? This question is important to answer before estimating amodel. When estimating a model a loss function is chosen, a function that gives an instruction of how to transform a particular error. Previous research hints at applications whereasymmetric loss functions provide more optimal models than using symmetric loss functions.Through a simulation study, this thesis highlights the consequences of using symmetric andasymmetric loss functions when assuming the actual problem is asymmetric. This thesis isconducted to cover a gap in literature as well as to correct a common statistical misunderstanding. Our core findings are that the models that take the asymmetry into account havethe lowest prediction errors, while also demonstrating that the larger the degree of asymmetry leads to a greater difference in performance between asymmetric and symmetric modelsin favour of the models estimated with asymmetric loss functions. This confirms what isdemonstrated in existing literature and what can be found in statistical theory.