ConnectednessApproach {ConnectednessApproach}

Connectedness Approach

Description

This function provides a modular framework combining various models and connectedness frameworks.

Usage

ConnectednessApproach(
  x,
  nlag = 1,
  nfore = 10,
  window.size = NULL,
  corrected = FALSE,
  model = c("VAR", "QVAR", "LAD", "LASSO", "Ridge", "Elastic", "TVP-VAR", "DCC-GARCH"),
  connectedness = c("Time", "Frequency", "Joint", "Extended Joint", "R2"),
  VAR_config = list(QVAR = list(tau = 0.5, method = "fn"), ElasticNet = list(nfolds = 10,
    alpha = NULL, loss = "mae", n_alpha = 10), TVPVAR = list(kappa1 = 0.99, kappa2 =
    0.99, prior = "BayesPrior", gamma = 0.01)),
  DCC_config = list(standardize = FALSE),
  Connectedness_config = list(TimeConnectedness = list(generalized = TRUE),
    FrequencyConnectedness = list(partition = c(pi, pi/2, 0), generalized = TRUE,
    scenario = "ABS"), R2Connectedness = list(method = "pearson", decomposition = TRUE,
    relative = FALSE))
)

Arguments

x

zoo data matrix

nlag

Lag length

nfore

H-step ahead forecast horizon

window.size

Rolling-window size or Bayes Prior sample size

corrected

Boolean value whether corrected or standard TCI should be computed

model

Estimation model

connectedness

Type of connectedness approach

VAR_config

Config for VAR model

DCC_config

Config for DCC-GARCH model

Connectedness_config

Config for connectedness approach

Value

Get connectedness measures

Author(s)

David Gabauer

References

Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158-171.

Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57-66.

Barunik, J., & Krehlik, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2), 271-296.

Gabauer, D. (2020). Volatility impulse response analysis for DCC-GARCH models: The role of volatility transmission mechanisms. Journal of Forecasting, 39(5), 788-796.

Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4), 84.

Lastrapes, W. D., & Wiesen, T. F. (2021). The joint spillover index. Economic Modelling, 94, 681-691.

Balcilar, M., Gabauer, D., & Umar, Z. (2021). Crude Oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach. Resources Policy, 73, 102219.

Chatziantoniou, I., & Gabauer, D. (2021). EMU risk-synchronisation and financial fragility through the prism of dynamic connectedness. The Quarterly Review of Economics and Finance, 79, 1-14.

Chatziantoniou, I., Gabauer, D., & Stenfors, A. (2021). Interest rate swaps and the transmission mechanism of monetary policy: A quantile connectedness approach. Economics Letters, 204, 109891.

Gabauer, D. (2021). Dynamic measures of asymmetric & pairwise connectedness within an optimal currency area: Evidence from the ERM I system. Journal of Multinational Financial Management, 60, 100680.

Gabauer, D., Gupta, R., Marfatia, H., & Miller, S. (2020). Estimating US Housing Price Network Connectedness: Evidence from Dynamic Elastic Net, Lasso, and Ridge Vector Autoregressive Models (No. 202065). University of Pretoria, Department of Economics.

Chatziantoniou, I., Gabauer, D., & Gupta, R. (2021). Integration and Risk Transmission in the Market for Crude Oil: A Time-Varying Parameter Frequency Connectedness Approach (No. 202147).

Chatziantoniou, I., Aikins Abakah, E. J., Gabauer, D., & Tiwari, A. K. (2022). Quantile time-frequency price connectedness between green bond, green equity, sustainable investments and clean energy markets. Journal of Cleaner Production.

Cunado, J, Chatziantoniou, I., Gabauer, D., Hardik, M., & de Garcia, F.P. (2022). Dynamic spillovers across precious metals and energy realized volatilities: Evidence from quantile extended joint connectedness measures.

Examples

data("acg2020")
dca = ConnectednessApproach(acg2020, 
                            nlag=1, 
                            nfore=12,
                            model="VAR",
                            connectedness="Time",
                            VAR_config=list(TVPVAR=list(kappa1=0.99, kappa2=0.96,
                                            prior="MinnesotaPrior", gamma=0.1)))
dca$TABLE