Financial Contagion Analysis with WaveQTE

Introduction

This vignette demonstrates how to analyze financial contagion using the WaveQTE package.

Market Data Setup

library(WaveQTE)

# Define market groups
dm_symbols <- c("SPY", "EWU", "EWJ", "EWG", "EWQ")  # Developed markets ETFs
em_symbols <- c("EWZ", "FXI", "INDA", "EWY", "EIDO") # Emerging markets ETFs

# Get and process data
dm_returns <- get_stock_data(dm_symbols, "2019-01-01", "2023-12-31")
em_returns <- get_stock_data(em_symbols, "2019-01-01", "2023-12-31")

dm_returns <- process_returns(dm_returns)
em_returns <- process_returns(em_returns)

Wavelet Decomposition

# Parameters
params <- list(
  n_levels = 4,
  tau_levels = c(0.1, 0.25, 0.5, 0.75, 0.9),
  wf = "la8"
)

# Decompose developed market returns
dm_wave <- lapply(1:ncol(dm_returns), function(i) {
  wavelet_decompose(dm_returns[,i], n.levels = params$n_levels, wf = params$wf)
})

# Decompose emerging market returns
em_wave <- lapply(1:ncol(em_returns), function(i) {
  wavelet_decompose(em_returns[,i], n.levels = params$n_levels, wf = params$wf)
})

Contagion Analysis

# Calculate contagion measures
contagion_matrix <- calculate_contagion_index(dm_returns, em_returns)
dci_results <- calculate_dynamic_contagion(dm_returns, em_returns)

# Plot results
plot_contagion_network(contagion_matrix, dm_symbols, em_symbols)
plot_contagion_heatmap(contagion_matrix, dm_symbols, em_symbols)

Crisis Period Analysis

# Identify crisis periods
crisis_periods <- identify_crisis_periods(cbind(dm_returns, em_returns))

# Analyze crisis vs non-crisis periods
crisis_results <- crisis_analysis(dm_returns, em_returns, crisis_periods)

# Compare results
plot_crisis_comparison(crisis_results)

Cross-Market Correlation Analysis

# Calculate rolling correlations
correlations <- calculate_cross_market_correlation(
  dm_returns, 
  em_returns, 
  window_size = 252
)

# Analyze correlation patterns
plot_rolling_qte(correlations, window_size = 252)

Multiscale Analysis

# Calculate QTE at different scales
for(scale in 1:params$n_levels) {
  for(tau in params$tau_levels) {
    cat("\nScale:", scale, "Tau:", tau, "\n")
    
    # Calculate and plot QTE networks
    net <- create_qte_network(
      cbind(dm_returns, em_returns),
      c(dm_wave, em_wave),
      scale = scale,
      tau = tau
    )
    
    plot_enhanced_network(net, scale, tau)
  }
}

Saving Results

# Save analysis results
save_results(list(
  contagion = contagion_matrix,
  dci = dci_results,
  crisis = crisis_results,
  correlations = correlations
), "contagion_analysis.RData")

Additional Resources

For more information, see: - Package documentation: ?WaveQTE - Basic usage vignette: vignette("basic-usage", package = "WaveQTE") - Advanced features vignette: vignette("advanced-features", package = "WaveQTE")