DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks

Area of Science:

• Financial Risk Management
• Quantitative Finance
• Machine Learning in Finance

Background:

• Financial institutions face significant challenges in determining and minimizing risk exposure.
• Value at Risk (VaR) is a prevalent risk assessment metric, but its efficiency falters during economic crises.
• Existing VaR models struggle to provide accurate predictions during volatile market conditions like the 2008 crisis and the COVID-19 pandemic.

Purpose of the Study:

• To introduce a novel probabilistic deep learning approach for enhanced risk monitoring.
• To leverage time-series forecasting for efficient portfolio risk assessment.
• To improve the accuracy and efficiency of Value at Risk (VaR) calculations.

Main Methods:

• Development of a probabilistic deep learning model.
• Application of time-series forecasting techniques.
• Evaluation and comparison against prominent VaR calculation methods.

Main Results:

• The proposed deep learning approach demonstrates high potential for efficient portfolio risk monitoring.
• Promising results were achieved for Value at Risk (VaR) 99% calculations.
• The model outperformed existing methods in specific scenarios, particularly for forex-based portfolios.

Conclusions:

• The probabilistic deep learning approach offers a more efficient and robust method for VaR calculation.
• This technique shows significant promise for improving financial risk management, especially in volatile markets.
• The study highlights the potential of advanced machine learning techniques in addressing limitations of traditional financial risk models.