Abstract: This paper proposes a systemic risk index based on Functional Data Analysis (FDA), overcoming salient shortcomings of standard methodologies related to data usage, data sparseness, and high dimensionality issues. Using Mexican data, a set of systemic risk indexes are constructed and we show that the proposed functional index captures new information, and through simulations, that it outperforms previous methods when the indicators become increasingly nonlinear. Finally, we show which indexes serve as complements, and which are the best early warning indicators