Financial risk management includes identifying risk, quantifying the uncertainty, and mitigating an investor's or a company's exposure to that risk. Increasingly, mathematics and statistics play a central role in financial risk management. In the past few decades financial instruments and investment strategies have become more complex requiring more complex analysis and solutions. Risk management requires building models to understand dependencies and interactions among the data. Moreover, visualization plays an important role in understanding what the data are saying. Thus, the use of statistical methods and software play an integral role in financial risk management. The advancement of computational power makes applications of methods like deep learning, neural networks, and long short-term memory networks viable solutions to complex problems in finance and financial risk management.

Papers submitted to the Mathematics and Statistics for Financial Risk Management Section generally cover statistical analysis of financial risk, models for predicting potential risk, illustrations or methodologies for managing or mitigating risk. The goal is to bring forward the understanding of financial risk to the readership and enhance investors' capability to make better informed investment decisions.

The application of statistical methods to problems in risk management and the development of new methodologies are the focus of the Mathematics and Statistics for Financial Risk Management section of the Financial Statistical Journal. Research articles and review articles on the application of statistical techniques, mathematical methods, simulation or computation are welcome.