Chapter 1. Machine Learning in Finance: Transformation of Financial Markets (Musa Gün) -- Chapter 2. Digital Currencies and Financial Transformation (Bilal Bagis) -- Chapter 3. A Hybrid ARIMA-LSTM/GRU Model for Forecasting Monthly Trends in Turkey’s Gold and Currency Markets with a...

Technological development particularly boosted by artificial intelligence (AI) has substantial potential to transform many aspects of human lives and the way doing businesses. On the one side, it can offer opportunities, while on the other brings challenges and increases risks. Financial...

We explore the design of climate stress tests to assess and manage macro-prudential risks from climate change in the financial sector. We review the climate stress scenarios currently employed by regulators, highlighting the need to (i) consider many transition risks as dynamic policy choices;...

Using the test of Granger-causality in tail of Hong et al. (2009), we define and construct Granger-causality tail risk networks between 33 systemically important banks (G-SIBs) and 36 sovereign bonds worldwide. Our purpose is to exploit the structure of the Granger-causality tail risk networks...

Special purpose vehicles (SPVs), extremely popular financial structures for the creation of highly-rated tranched securities, experienced spectacular demise in the 2007-08 financial crisis. These financial vehicles epitomize the shadow banking sector, characterized by high leverage,...

We explore the design of climate stress tests to assess and manage macro-prudential risks from climate change in the financial sector. We review the climate stress scenarios currently employed by regulators, highlighting the need to (i) consider many transition risks as dynamic policy choices;...

We give an explicit algorithm and source code for constructing risk models based on machine learning techniques. The resultant covariance matrices are not factor models. Based on empirical backtests, we compare the performance of these machine learning risk models to other constructions,...

The paper proposes an explainable AI model that can be used in credit risk management and, in particular, in measuring the risks that arise when credit is borrowed employing credit scoring platforms. The model applies similarity networks to Shapley values, so that AI predictions are grouped...