Predicting Value-at-Risk with Risk-aware Mutual Attention Mechanism

Value-at-Risk is a risk measurement method utilized in the capital market to estimate potential losses within a given period. However, the challenge of Value-at-Risk is a tradeoff between proximity and violations. Therefore, there is rare research available on Value-at-Risk prediction. This study utilized an attention-based model called Risk-Aware Mutual Attention Mechanism (RAMAM) to extract information between the global market and a single asset. The RAMAM model incorporated global indices, higher order moments and decomposed signals obtained through Variational Mode Decomposition (VMD) as features to capture relevant information and enable effective risk assessment. In experiments, the proposed model demonstrated significantly lower mean absolute error (MAE).