Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Management

TL;DR

This paper introduces a deep learning model using Gated Recurrent Units to predict liquidity coverage ratios, enhancing risk management accuracy for financial institutions amid complex market conditions.

Contribution

The study develops a novel GRU-based prediction model that outperforms traditional methods in forecasting liquidity coverage ratios, aiding risk management and regulatory decision-making.

Findings

GRU model achieves lower MAE than traditional methods

Model demonstrates high accuracy and robustness

Supports better liquidity risk management and policy formulation

Abstract

With the global economic integration and the high interconnection of financial markets, financial institutions are facing unprecedented challenges, especially liquidity risk. This paper proposes a liquidity coverage ratio (LCR) prediction model based on the gated recurrent unit (GRU) network to help financial institutions manage their liquidity risk more effectively. By utilizing the GRU network in deep learning technology, the model can automatically learn complex patterns from historical data and accurately predict LCR for a period of time in the future. The experimental results show that compared with traditional methods, the GRU model proposed in this study shows significant advantages in mean absolute error (MAE), proving its higher accuracy and robustness. This not only provides financial institutions with a more reliable liquidity risk management tool but also provides support for regulators to formulate more scientific and reasonable policies, which helps to improve the stability of the entire financial system.