Risk management in trading activities through the lens of complex systems theory

The financial crisis of 2008, which led to a near collapse of the banking system, propelled financial institutions, regulators, and researchers alike to revisit their conventional approaches and offer comprehensive explanations of its causes. In the post-crisis era, there has been increasing interest in using ideas from complexity theory to achieve a better understanding of financial markets. Concepts, such as regime shifts, interconnectedness, contagion, feedback, and resilience, have entered the financial vocabulary but the actual use of models and results from complexity is still at an early stage. Moreover, while recent studies offer potential for better monitoring and supervision of the financial system, little work has been done on incorporating complexity tools in the pricing and risk management of individual portfolios. This thesis proposes novel risk management models which view financial markets as complex systems prone to sudden and major changes and consisting of many interacting nodes. The main conclusion is that in order for quantitative risk management to be able to cope with the complex, interconnected nature of modern financial systems, a paradigm shift is inevitable. Complexity-inspired models, in combination with more traditional modelling and simulation frameworks, can open up new avenues for risk management and fill some of the gaps and inadequacies of regulatory frameworks revealed by the global financial crisis. The proposed methods are built on this assumption and show that, by using models that account for characteristics of complex systems, the impact on capital requirements may be significant.