Understanding the Effects of Green Swan Events on Financial Stability: Annex II Countries and Turkey

Şenay Açıkgöz and Şahika Gökmen

Chapter 6 in Modeling and Advanced Techniques in Modern Economics, 2022, pp 127-160 from World Scientific Publishing Co. Pte. Ltd.

Abstract: Green swan events are as important and unpredictable as black swan events since they include climate-related risks and their consequences. One of the important consequences of such events is related to financial risks, such as credit, market, operational, liquidity and insurance risks, that create financial and price instability. Therefore, almost all central banks started to have a role in fighting climate change to maintain financial stability — the main aim of these banks. The relationship between the building blocks of green swan events and financial stability is constructed as the main idea of this chapter. Based on this, this research summarizes the mechanisms between climate-related financial risks and financial stability as well as price stability in the green swan context. The current situation of the world and Turkey are reviewed with data, and the interaction between climate risks and financial and price stabilities is explained. A couple of stability indicators for Annex II countries and Turkey are also reviewed to understand their current financial stability situation. Scenario analysis, which is the main tool for understanding the possible effects of climate change to produce policies for transition to low-carbon or net-zero target, is also reviewed. Therefore, this chapter contributes to this very recent literature by giving a clear view of the green swan.

Keywords: Harmonic Regression; Periodograms; Consumer Price Index; Food Inflation; Turkey; Gaussian Distribution; Europe Union; GDP; Panel Data; Spatial Regression; Measurement Errors; Nonlinear Time Series; Chaotic Time Series; Weibull Distribution; Location Parameters; Fiducial Approach; Hypothesis Testing; Green Swan; Financial Stability; Annex II Countries; Financial Time Series; Kernels; Stock Index; Machine Learning; Statistical Learning; Optimization; WSAR Algorithm; Deep Neural Networks; Phyton; Parameter Estimation; COVID-19; Clustering Analyses; Artificial Neural Networks; Performance Criteria; Time Series Forecasting; Statistical Inference (search for similar items in EconPapers)
JEL-codes: C1 C4 C5 C6 C63 (search for similar items in EconPapers)
Date: 2022
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