WaveQTE: Wavelet-based Quantile Transfer Entropy Analysis

Implements Wavelet-based Quantile Transfer Entropy analysis for financial time series. Provides tools for analyzing market interconnectedness, financial contagion, and risk spillover effects across different time scales and market conditions.

Main Functions

• Data Preparation: get_stock_data, process_returns, calculate_summary_stats

• Wavelet Analysis: wavelet_decompose, safe_wavelet_decompose, analyze_wavelet_variance

• Quantile Transfer Entropy: calculate_qte, calculate_multiscale_qte, bootstrap_qte

• Network Analysis: create_qte_network, calculate_network_metrics, create_multiscale_networks

• Visualization: plot_enhanced_network, plot_qte_heatmap, plot_market_strength

Data Requirements

The package works with financial time series data. The main input should be:

• Return series (preferably daily)

• Clean data without missing values

• Sufficient length for wavelet decomposition

Usage Example

library(WaveQTE)

# Get data
data <- get_stock_data(c("AAPL", "MSFT"), "2019-01-01", "2023-12-31")

# Wavelet decomposition
wave <- lapply(1:ncol(data), function(i) wavelet_decompose(data[,i]))

# Calculate QTE
qte <- calculate_multiscale_qte(wave[[1]], wave[[2]], c(0.1, 0.5, 0.9))

# Create network
net <- create_qte_network(data, wave, scale = 1)

# Visualize
plot_enhanced_network(net, scale = 1, tau = 0.5)

References

Add relevant papers and references here.

See also

Useful links:

• https://github.com/avishekb9/WaveQTE

• Report bugs at https://github.com/avishekb9/WaveQTE/issues

Author

Maintainer: Avishek Bhandari bavisek@gmail.com