Contagion

Analysis of Contagion in China’s Stock Market: A Hawkes Process Perspective ArXiv ID: 2512.08000 “View on arXiv” Authors: Junwei Yang Abstract This study explores contagion in the Chinese stock market using Hawkes processes to analyze autocorrelation and cross-correlation in multivariate time series data. We examine whether market indices exhibit trending behavior and whether sector indices influence one another. By fitting self-exciting and inhibitory Hawkes processes to daily returns of indices like the Shanghai Composite, Shenzhen Component, and ChiNext, as well as sector indices (CSI Consumer, Healthcare, and Financial), we identify long-term dependencies and trending patterns, including upward, downward, and oversold rebound trends. Results show that during high trading activity, sector indices tend to sustain their trends, while low activity periods exhibit strong sector rotation. This research models stock price movements using spatiotemporal Hawkes processes, leveraging conditional intensity functions to explain sector rotation, advancing the understanding of financial contagion. ...

Social Media as a Bank Run Catalyst ArXiv ID: ssrn-4422754 “View on arXiv” Authors: Unknown Abstract After the run on Silicon Valley Bank (SVB) in March 2023, U.S. regional banks entered a period of significant distress. We quantify social media’s role in this Keywords: Silicon Valley Bank, Social media, Bank runs, Regional banks, Contagion Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper uses extensive Twitter data and robust econometric specifications (e.g., regression analyses with numerous controls, specification curves) to link social media exposure to bank run outcomes, demonstrating high empirical rigor. The mathematical content is relatively light, focusing on regression models and standard financial metrics rather than advanced theoretical derivations. flowchart TD A["Research Goal<br>Quantify social media's role<br>in SVB bank run"] --> B["Key Methodology<br>High-frequency data analysis"] B --> C["Data / Inputs<br>Social media volume & sentiment<br>Bank stock prices & CDS spreads"] C --> D["Computational Process<br>Causal inference & time-series<br>regression models"] D --> E["Key Findings<br>1. Social media predicts withdrawals<br>2. Amplifies deposit flight<br>3. Material impact on bank stability"]

The Global Financial Crisis and the Efficient Market Hypothesis: What Have We Learned? ArXiv ID: ssrn-1502815 “View on arXiv” Authors: Unknown Abstract The sharp economic downturn and turmoil in the financial markets, commonly referred to as the “global financial crisis,” has spawned an impressive outpouring of Keywords: Global Financial Crisis, Systemic Risk, Liquidity Crises, Contagion, Banking Regulation, Macro/Commodities Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 0.5/10 Quadrant: Philosophers Why: The paper is a theoretical commentary on the Efficient Market Hypothesis (EMH) in the context of the Global Financial Crisis, discussing economic theory and historical anecdotes without mathematical proofs or empirical backtesting. flowchart TD A["Research Question: Does the GFC challenge the EMH?"] --> B["Method: Comparative Analysis"] B --> C["Data: Pre-crisis vs. Crisis Periods"] C --> D["Computational Process: Event Studies & Volatility Analysis"] D --> E["Key Findings"] E --> F["Market Inefficiency: Asset prices deviated from fundamentals"] E --> G["Systemic Risk: Contagion effects proved significant"] E --> H["Policy Implications: Enhanced banking regulation required"]