Systemic Risk

Contagion on Financial Networks: An Introduction ArXiv ID: 2402.08071 “View on arXiv” Authors: Unknown Abstract This mini-project models propagation of shocks, in time point, through links in connected banks. In particular, financial network of 100 banks out of which 15 are shocked to default (that is, 85.00% of the banks are solvent) is modelled using Erdos and Renyi network – directed, weighted and randomly generated network. Shocking some banks in a financial network implies removing their assets and redistributing their liabilities to other connected ones in the network. The banks are nodes and two ranges of probability values determine tendency of having a link between a pair of banks. Our major finding shows that the ranges of probability values and banks’ percentage solvency have positive correlation. ...

Structured factor copulas for modeling the systemic risk of European and United States banks ArXiv ID: 2401.03443 “View on arXiv” Authors: Unknown Abstract In this paper, we employ Credit Default Swaps (CDS) to model the joint and conditional distress probabilities of banks in Europe and the U.S. using factor copulas. We propose multi-factor, structured factor, and factor-vine models where the banks in the sample are clustered according to their geographic location. We find that within each region, the co-dependence between banks is best described using both, systematic and idiosyncratic, financial contagion channels. However, if we consider the banking system as a whole, then the systematic contagion channel prevails, meaning that the distress probabilities are driven by a latent global factor and region-specific factors. In all cases, the co-dependence structure of bank CDS spreads is highly correlated in the tail. The out-of-sample forecasts of several measures of systematic risk allow us to identify the periods of distress in the banking sector over the recent years including the COVID-19 pandemic, the interest rate hikes in 2022, and the banking crisis in 2023. ...

Decentralized Finance: Protocols, Risks, and Governance ArXiv ID: 2312.01018 “View on arXiv” Authors: Unknown Abstract Financial markets are undergoing an unprecedented transformation. Technological advances have brought major improvements to the operations of financial services. While these advances promote improved accessibility and convenience, traditional finance shortcomings like lack of transparency and moral hazard frictions continue to plague centralized platforms, imposing societal costs. In this paper, we argue how these shortcomings and frictions are being mitigated by the decentralized finance (DeFi) ecosystem. We delve into the workings of smart contracts, the backbone of DeFi transactions, with an emphasis on those underpinning token exchange and lending services. We highlight the pros and cons of the novel form of decentralized governance introduced via the ownership of governance tokens. Despite its potential, the current DeFi infrastructure introduces operational risks to users, which we segment into five primary categories: consensus mechanisms, protocol, oracle, frontrunning, and systemic risks. We conclude by emphasizing the need for future research to focus on the scalability of existing blockchains, the improved design and interoperability of DeFi protocols, and the rigorous auditing of smart contracts. ...

A General Framework for Importance Sampling with Markov Random Walks ArXiv ID: 2311.12330 “View on arXiv” Authors: Unknown Abstract Although stochastic models driven by latent Markov processes are widely used, the classical importance sampling methods based on the exponential tilting for these models suffers from the difficulties in computing the eigenvalues and associated eigenfunctions and the plausibility of the indirect asymptotic large deviation regime for the variance of the estimator. We propose a general importance sampling framework that twists the observable and latent processes separately using a link function that directly minimizes the estimator’s variance. An optimal choice of the link function is chosen within the locally asymptotically normal family. We show the logarithmic efficiency of the proposed estimator. As applications, we estimate an overflow probability under a pandemic model and the CoVaR, a measurement of the co-dependent financial systemic risk. Both applications are beyond the scope of traditional importance sampling methods due to their nonlinear features. ...

Estimating the impact of supply chain network contagion on financial stability ArXiv ID: 2305.04865 “View on arXiv” Authors: Unknown Abstract Realistic credit risk assessment, the estimation of losses from counterparty’s failure, is central for the financial stability. Credit risk models focus on the financial conditions of borrowers and only marginally consider other risks from the real economy, supply chains in particular. Recent pandemics, geopolitical instabilities, and natural disasters demonstrated that supply chain shocks do contribute to large financial losses. Based on a unique nation-wide micro-dataset, containing practically all supply chain relations of all Hungarian firms, together with their bank loans, we estimate how firm-failures affect the supply chain network, leading to potentially additional firm defaults and additional financial losses. Within a multi-layer network framework we define a financial systemic risk index (FSRI) for every firm, quantifying these expected financial losses caused by its own- and all the secondary defaulting loans caused by supply chain network (SCN) shock propagation. We find a small fraction of firms carrying substantial financial systemic risk, affecting up to 16% of the banking system’s overall equity. These losses are predominantly caused by SCN contagion. For every bank we calculate the expected loss (EL), value at risk (VaR) and expected shortfall (ES), with and without accounting for SCN contagion. We find that SCN contagion amplifies the EL, VaR, and ES by a factor of 4.3, 4.5, and 3.2, respectively. These findings indicate that for a more complete picture of financial stability and realistic credit risk assessment, SCN contagion needs to be considered. This newly quantified contagion channel is of potential relevance for regulators’ future systemic risk assessments. ...

DeFi: Shadow Banking 2.0? ArXiv ID: ssrn-4038788 “View on arXiv” Authors: Unknown Abstract The growth of so-called “shadow banking” was a significant contributor to the financial crisis of 2008, which had huge social costs that we still grapple with t Keywords: shadow banking, financial crisis, systemic risk, regulatory arbitrage, non-bank financial intermediation, Fixed Income Complexity vs Empirical Score Math Complexity: 0.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a legal/regulatory analysis using historical case studies and conceptual arguments, with no mathematical modeling or empirical backtesting. flowchart TD A["Research Goal"] --> B["DeFi as Shadow Banking?"] B --> C["Methodology"] C --> D["Empirical Analysis"] D --> E["Data: Tether Reserves & Fixed Income"] E --> F["Computational Process"] F --> G["Correlation & Stress Tests"] G --> H["Findings"] H --> I["Systemic Risk & Regulatory Arbitrage"]

Investment Opportunities and Strategies in an Era of Coronavirus Pandemic ArXiv ID: ssrn-3567445 “View on arXiv” Authors: Unknown Abstract The COVID-19 continues to hit the world economy as well as the financial markets. As a result of the coronavirus spread across all continents, the majority of t Keywords: COVID-19 Impact, Market Volatility, Systemic Risk, Economic Shock, Financial Contagion, Global Equities Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper relies on qualitative analysis and sector descriptions without advanced mathematical models, and the empirical component is limited to basic stock price observations and news citations rather than rigorous backtesting or data analysis. flowchart TD A["Research Goal:<br>Assess COVID-19 impact on markets and identify investment strategies"] --> B{"Key Methodology"}; B --> C["Data: Global Equities, Volatility Indices, Economic Indicators"]; B --> D["Analysis: Systemic Risk &<br>Financial Contagion Modeling"]; C --> E["Computational Process:<br>Shock Simulation & Volatility Correlation"]; D --> E; E --> F["Key Findings & Outcomes"]; F --> G["Identified High-Risk Sectors"]; F --> H["Revealed Opportunities in Resilient Assets"]; F --> I["Strategic Recommendations for Mitigating Economic Shock"];

DigitalFinance& The COVID-19 Crisis ArXiv ID: ssrn-3558889 “View on arXiv” Authors: Unknown Abstract The COVID-19 coronavirus crisis is putting unprecedented strain on markets, governments, businesses and individuals. The human, economic and financial costs are Keywords: COVID-19, Market Volatility, Systemic Risk, Economic Impact, Cross-Asset Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a high-level policy and regulatory analysis with no mathematical models or empirical backtesting, focusing on conceptual strategies and qualitative recommendations. flowchart TD A["Research Goal: Impact of COVID-19<br>on Digital Finance Markets"] --> B["Data Collection"] B --> C["Methodology: Cross-Asset Analysis"] C --> D["Computational Process:<br>Volatility & Risk Modeling"] D --> E["Key Findings"] subgraph B ["Data/Inputs"] B1["Market Volatility Data"] B2["Systemic Risk Indicators"] B3["Economic Impact Metrics"] end subgraph E ["Outcomes"] E1["Increased Market Volatility"] E2["Systemic Risk Transmission"] E3["Cross-Asset Correlation Spike"] end

The Lehman Brothers Bankruptcy A: Overview ArXiv ID: ssrn-2588531 “View on arXiv” Authors: Unknown Abstract On September 15, 2008, Lehman Brothers Holdings, Inc., the fourth-largest U.S. investment bank, sought Chapter 11 protection, initiating the largest bankruptcy Keywords: Bankruptcy, Lehman Brothers, Financial Crisis, Chapter 11, Systemic Risk, Equities Complexity vs Empirical Score Math Complexity: 0.0/10 Empirical Rigor: 0.0/10 Quadrant: Philosophers Why: This is a qualitative case study overview of the Lehman Brothers bankruptcy, focusing on historical narrative, business operations, and regulatory questions without any mathematical formulas, data analysis, or backtesting components. flowchart TD A["Research Goal: Analyze<br>Lehman Brothers Bankruptcy"] --> B["Key Methodology:<br>Case Study & Financial Analysis"] B --> C["Data Inputs:<br>Financial Reports &<br>Chapter 11 Filings"] C --> D["Computational Process:<br>Reconstruct Timeline &<br>Assess Systemic Risk"] D --> E["Key Findings:<br>Highlighted Role of<br>Systemic Risk &<br>Liquidity Failure"]

Explaining the Housing Bubble ArXiv ID: ssrn-1669401 “View on arXiv” Authors: Unknown Abstract There is little consensus as to the cause of the housing bubble that precipitated the financial crisis of 2008. Numerous explanations exist: misguided monetary Keywords: Housing bubble, Financial crisis, Systemic risk, Real Estate Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 4.0/10 Quadrant: Philosophers Why: The paper is primarily an economic and legal analysis of the housing bubble, relying on theoretical frameworks like information asymmetry and supply-side explanations with minimal advanced mathematics. While it uses historical data and discusses market mechanisms, it lacks backtests, quantitative models, or implementation-heavy empirical validation. flowchart TD A["Research Question: Causes of the 2008 Housing Bubble"] --> B["Data Collection: Financial, Macroeconomic, & Real Estate Data"] B --> C["Methodology: Econometric Analysis & Risk Modeling"] C --> D{"Computational Process: Identification of Systemic Risk Drivers"} D --> E["Key Finding: Inadequate Capital Buffers & Misguided Monetary Policy"] D --> F["Key Finding: Complex Derivatives Amplified Market Volatility"] E --> G["Outcome: Framework for Macroprudential Regulation"] F --> G