contagionchannels

Two-Stage Detection and Attribution of Cross-Border Financial Contagion Channels

contagionchannels is an R package implementing the two-stage framework for the joint detection and attribution of cross-border financial contagion developed in Bhandari, Parida and Sahu (2026), arXiv:2604.26546. The framework first detects directional information flows between equity markets via Wavelet-Quantile Transfer Entropy and then attributes each significant directional link to one of five mutually exclusive transmission channels — Trade, Financial, Geopolitical, Behavioural, Monetary Policy — through a multi-method structural-identification architecture.

Highlights

Stage 1: Wavelet-Quantile Transfer Entropy (WQTE) over a six-scale MODWT decomposition and three quantile levels, with absolute-thresholding to construct directed contagion networks.
Stage 2: Five complementary identification strategies for channel attribution:
- Instrumental-variables / Two-Stage Least Squares with channel-specific external instruments — Stock & Watson (2018) doi:10.1111/ecoj.12593
- LASSO instrument selection — Belloni, Chernozhukov & Hansen (2014) doi:10.1093/restud/rdt044
- Local projections at h=1, 5, 22 days — Jordà (2005) doi:10.1257/0002828053828518
- Heteroskedasticity-based identification — Rigobon (2003) doi:10.1162/003465303772815727
- Cinelli-Hazlett (2020) robustness-value sensitivity — doi:10.1111/rssb.12348
Bundled datasets: 18 G20 equity markets × 5,036 daily log-returns (Jan 2006 – Mar 2026); 14 channel proxies; 8 crisis sub-period definitions.
Three vignettes: full paper replication; methodology overview; using with custom datasets.

Installation

From source (the package is not yet on CRAN):

# install.packages("devtools")
devtools::install_local("path/to/contagionchannels", build_vignettes = TRUE)

Or directly from the source tarball:

install.packages("contagionchannels_0.1.3.tar.gz", repos = NULL, type = "source")

Quick start

library(contagionchannels)

# Load bundled data
d <- load_paper_data()

# Build the five channel composites
channels <- build_channel_composites(d$proxies)

# Run the full pipeline (Stage 1 detection + Stage 2 attribution)
res <- run_contagion_pipeline(d$returns, channels, d$periods,
                               scale = 5, tau = 0.50, n_cores = 4)

# Per-period channel-attribution shares
res$period_shares
#>            Period Trade Financial Geopolitical Behavioral Monetary  Dominant
#> 1       PreCrisis   8.8      35.9          9.4       13.8     32.1 Financial
#> 2             GFC  27.9      15.0         27.0        2.9     27.2     Trade
#> 3            ESDC  13.7      39.5         19.0       19.6      8.1 Financial
#> 4             CSC  15.9      21.6         15.6       21.7     25.3  Monetary
#> 5        PreCOVID  18.1      31.6          8.2       13.5     28.6 Financial
#> 6           COVID  18.7      18.3         27.5        8.2     27.2 Geopolit.
#> 7          RusUkr  22.6      23.4          6.1       20.3     27.6  Monetary
#> 8 MidEastTariffs   27.6      26.8          7.5        6.3     31.8  Monetary

Key functions

Function	Purpose
`compute_wqte_matrix()`	Stage-1 pairwise Wavelet-Quantile Transfer Entropy
`build_channel_composites()`	Construct five channel composites from raw proxies
`iv_2sls_attribute()`	Stage-2 IV/2SLS attribution per significant link
`lasso_iv_attribute()`	LASSO IV variant per Belloni-Chernozhukov-Hansen
`local_projections()`	Jordà 2005 local projections at multiple horizons
`rigobon_id()`	Heteroskedasticity-based identification
`cinelli_hazlett_rv()`	Robustness-value sensitivity bound
`build_network()`	Construct directed contagion network from QTE matrix
`walktrap_communities()`	Pons-Latapy community detection
`run_contagion_pipeline()`	End-to-end wrapper

Bundled datasets

Object	Description
`g20_returns`	xts of daily log-returns: 5,036 days × 18 markets
`channel_proxies`	data.frame of 14 channel-proxy time series
`crisis_periods`	Named list of 8 crisis sub-period date ranges

Vignettes

vignette("replication", package = "contagionchannels")  # Reproduce the paper
vignette("methodology", package = "contagionchannels")  # Methodology overview
vignette("custom_data", package = "contagionchannels")  # Use custom datasets

Replication scripts

The package includes a standalone replication suite at system.file("scripts", package = "contagionchannels"). To reproduce every numerical result and figure:

master <- system.file("scripts", "99_replicate_paper.R",
                       package = "contagionchannels")
source(master)

Citation

If you use the package in published work, please cite the methodology paper:

Bhandari, A., Parida, I., & Sahu, H. K. (2026). What Drives Contagion? Identifying and Attributing Cross-Border Transmission Mechanisms. arXiv preprint arXiv:2604.26546.

@misc{bhandari2026contagion,
  title         = {What Drives Contagion? Identifying and Attributing
                   Cross-Border Transmission Mechanisms},
  author        = {Bhandari, Avishek and Parida, Ipsita and Sahu, Hitesh Kumar},
  year          = {2026},
  eprint        = {2604.26546},
  archivePrefix = {arXiv},
  primaryClass  = {q-fin.ST},
  url           = {https://arxiv.org/abs/2604.26546}
}

@manual{contagionchannels2026,
  title    = {contagionchannels: Two-Stage Detection and Attribution of
              Cross-Border Financial Contagion Channels},
  author   = {Bhandari, Avishek and Parida, Ipsita and Sahu, Hitesh Kumar},
  year     = {2026},
  note     = {R package version 0.1.3},
  url      = {https://github.com/avishekb9/contagionchannels}
}