Manuscript Topics

The global financial crisis has taught us that in order to get information about the underlying financial risk dynamics, one needs to fully understand the complex, non-linear, time-varying and multidimensional nature of the data.

We propose a special issue on Machine Learning Methods and Models for Financial Stability because Machine Learning techniques can provide several advantages over traditional empirical models often used to monitor and predict financial developments, e.g. (1) They allow us to deal with unbalanced datasets; (2) They retain all of the information available; (3) They are purely data driven. However, as "black box" models, they are still much underutilized in Financial Stability.

The goal of the special issue is to raise awareness of the use of these methods in Financial Stability. The topics can include:
(1) Systemic Risk and Market Stability.
(2) High-Frequency Trading, Market Microstructure and instabilities in financial markets.
(3) Big Data Tools for Market Stability.
(4) Information Theoretic tools for time series and financial econometrics and Markets Stability

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