Overview

Reactive PublishingFinancial systems are complex adaptive networks where shocks propagate through balance sheets, institutions, and sovereigns in non-linear ways. Understanding and modeling these dynamics is now a core requirement for central banks, regulators, macro-prudential analysts, and market participants operating in a post-GFC environment defined by volatility, leverage, and structural fragility. Financial Stability & Systemic Risk Modeling with Python provides a comprehensive framework for modeling systemic risk using empirical data, network-based contagion mechanics, macro-prudential stress testing, and sovereign-bank feedback loops. Readers learn how to construct, simulate, and interpret systemic stress scenarios and translate them into operational insights for policy analysis, investment strategy, and risk management. Topics include: - Balance sheet-based contagion modeling and network spillovers - Stress testing frameworks across banks, sovereigns, corporates, and funds - Macro-prudential capital buffers, procyclicality, and regulatory triggers - Sovereign-bank doom loops and fiscal-financial feedback channels - Cross-border contagion and currency-denominated liabilities - Funding markets, collateral chains, and liquidity crunch dynamics - Agent-based systemic simulations for non-linear shock propagation - Market microstructure stress factors (volatility, basis spreads, fragmentation) - Early warning indicators for financial instability - Python implementations of core systemic models and simulation pipelines Designed for practitioners, researchers, and advanced students in finance, economics, and risk management, the book integrates real-world datasets, empirical calibration, and executable Python models. By bridging academic rigor with applied modeling, it equips readers to evaluate systemic vulnerabilities, stress test financial architecture, and foresee how shocks can cascade through an interconnected global economy. This is not a theoretical textbook. It is a modeling playbook for understanding financial stability under uncertainty, where the cost of misjudging contagion is measured in crises.

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