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Financial Contagion Analysis
Research Guide

What is Financial Contagion Analysis?

Financial Contagion Analysis examines the transmission of shocks and volatility spillovers across financial markets, particularly during crises like the Global Financial Crisis and COVID-19.

Researchers use Diebold-Yilmaz spillover indices, rolling-window DCC-GARCH models, and dynamic copulas to measure contagion. Studies cover equity markets, emerging economies, and high-frequency data. Over 2,500 citations across 10 key papers from 1997-2021.

Curated Papers

Key Challenges

Why It Matters

Financial Contagion Analysis informs international portfolio diversification by quantifying reduced benefits during crises (Christoffersen et al., 2012, 414 citations). Central banks apply spillover indices for macroprudential policies to contain systemic risk (Abuzayed et al., 2021, 200 citations). Investors use DCC-GARCH models to assess more contagion effects in emerging markets (Çelik, 2012, 268 citations).

Key Research Challenges

Asymmetric Dependence Modeling

Capturing higher dependence in bear markets and volatile periods challenges standard models. Okimoto (2008, 249 citations) identifies asymmetries in equity markets. Dynamic copulas address nonlinearities but require large cross-sections (Christoffersen et al., 2012).

High-Frequency Data Dependence

Multivariate high-frequency finance data shows volatility clustering and heavy tails. Breymann et al. (2003, 313 citations) highlight scaling and seasonalities. Fitting dependence structures demands advanced copulas amid data volume.

Distinguishing Shock Types

Separating permanent from temporary shock propagation uses smooth transition models. Syllignakis and Kouretas (2011, 388 citations) apply dynamic correlations for CEE markets. Crisis-specific effects like COVID complicate identification (Abuzayed et al., 2021).

Essential Papers

Stock Returns and Volatility: Pricing the Short‐Run and Long‐Run Components of Market Risk

Tobias Adrian, Joshua V. Rosenberg · 2008 · The Journal of Finance · 451 citations

ABSTRACT We explore the cross‐sectional pricing of volatility risk by decomposing equity market volatility into short‐ and long‐run components. Our finding that prices of risk are negative and sign...

Is the Potential for International Diversification Disappearing? A Dynamic Copula Approach

Peter Christoffersen, Vihang R. Errunza, Kris Jacobs et al. · 2012 · Review of Financial Studies · 414 citations

International equity markets are characterized by nonlinear dependence and asymmetries. We propose a new dynamic asymmetric copula model to capture long-run and short-run dependence, multivariate n...

Dynamic correlation analysis of financial contagion: Evidence from the Central and Eastern European markets

Manolis Syllignakis, Γεώργιος Π. Κουρέτας · 2011 · International Review of Economics & Finance · 388 citations

Dependence structures for multivariate high-frequency data in finance

W. Breymann, A. Dias, P. Embrechts · 2003 · Quantitative Finance · 313 citations

Stylized facts for univariate high-frequency data in finance are well known. They include scaling behaviour, volatility clustering, heavy tails and seasonalities. The multivariate problem, however,...

The more contagion effect on emerging markets: The evidence of DCC-GARCH model

Sibel Çelik · 2012 · Economic Modelling · 268 citations

New Evidence of Asymmetric Dependence Structures in International Equity Markets

Tatsuyoshi Okimoto · 2008 · Journal of Financial and Quantitative Analysis · 249 citations

Abstract A number of recent studies finds two asymmetries in dependence structures in international equity markets; specifically, dependence tends to be high in both highly volatile markets and in ...

Systemic risk spillover across global and country stock markets during the COVID-19 pandemic

Bana Abuzayed, Elie Bouri, Nedal Al‐Fayoumi et al. · 2021 · Economic Analysis and Policy · 200 citations

Reading Guide

Foundational Papers

Start with Christoffersen et al. (2012, 414 citations) for dynamic copulas capturing long/short-run dependence; Syllignakis and Kouretas (2011, 388 citations) for DCC contagion in CEE; Adrian and Rosenberg (2008, 451 citations) for volatility components pricing.

Recent Advances

Abuzayed et al. (2021, 200 citations) on COVID systemic spillovers; Chaudhary et al. (2020, 149 citations) on international volatility during COVID.

Core Methods

DCC-GARCH for dynamic correlations (Çelik, 2012); Diebold-Yilmaz spillover indices; asymmetric copulas (Okimoto, 2008); rolling-window tests.

How PapersFlow Helps You Research Financial Contagion Analysis

Discover & Search

Research Agent uses searchPapers and citationGraph to map Diebold-Yilmaz spillover studies from "Dynamic correlation analysis of financial contagion" (Syllignakis and Kouretas, 2011), then exaSearch for rolling-window DCC applications and findSimilarPapers for COVID extensions like Abuzayed et al. (2021).

Analyze & Verify

Analysis Agent applies readPaperContent to extract DCC-GARCH equations from Çelik (2012), runs runPythonAnalysis for volatility spillover replication with NumPy/pandas, and verifyResponse (CoVe) with GRADE grading to confirm asymmetric dependence claims from Okimoto (2008). Statistical verification checks GARCH parameter significance.

Synthesize & Write

Synthesis Agent detects gaps in pre-COVID vs. pandemic contagion via contradiction flagging, while Writing Agent uses latexEditText, latexSyncCitations for Diebold-Yilmaz indices, and latexCompile for reports; exportMermaid diagrams network spillovers across markets.

Use Cases

"Replicate DCC-GARCH contagion from Çelik 2012 on recent emerging market data"

Research Agent → searchPapers(DCC-GARCH contagion) → Analysis Agent → runPythonAnalysis(pandas GARCH fit, matplotlib spillovers) → output: Verified volatility transmission plots and stats.

"Write LaTeX section on Diebold-Yilmaz spillovers during GFC using Syllignakis 2011"

Research Agent → citationGraph(Syllignakis) → Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations + latexCompile → output: Compiled LaTeX with cited equations and figures.

"Find GitHub code for dynamic copula models from Christoffersen 2012"

Research Agent → paperExtractUrls(Christoffersen) → Code Discovery → paperFindGithubRepo → githubRepoInspect → output: Curated repos with copula implementations for asymmetric dependence.

Automated Workflows

Deep Research workflow scans 50+ papers via searchPapers on DCC-GARCH and spillovers, producing structured reports with citation networks from Adrian and Rosenberg (2008). DeepScan applies 7-step CoVe analysis to verify COVID contagion in Abuzayed et al. (2021), including Python replication checkpoints. Theorizer generates hypotheses on permanent vs. temporary shocks from smooth transition models in Syllignakis and Kouretas (2011).

Try Doxa for Financial Contagion Analysis Research

Frequently Asked Questions

What defines financial contagion analysis?

Financial Contagion Analysis measures shock transmission across markets using tools like DCC-GARCH and spillover indices during crises (Syllignakis and Kouretas, 2011).

What are main methods in financial contagion?

DCC-GARCH detects dynamic correlations (Çelik, 2012), Diebold-Yilmaz indices quantify spillovers, and dynamic copulas model asymmetries (Christoffersen et al., 2012).

What are key papers on financial contagion?

Syllignakis and Kouretas (2011, 388 citations) on CEE markets; Christoffersen et al. (2012, 414 citations) on copulas; Abuzayed et al. (2021, 200 citations) on COVID spillovers.

What are open problems in contagion analysis?

Distinguishing permanent vs. temporary shocks; modeling high-frequency asymmetries (Breymann et al., 2003); extending to crypto and climate risk transmissions.