tvtpnormal: Dynamic Gaussian Copula Model

In woraphonyamaka/TVTPcop: Dynamic Copula

Description

This function estimates the parameter(s) of a bivariate dynamic gaussian copula using maximum likelihood estimation.

Usage

dynamicnormal(data, plot=TRUE)

Arguments

data	The matrix T x 2 margins of the two random varibles
plot	plot the time varying dependence parameter (plot=TRUE, default)

Details

This model can be used as an alternative way of connecting the marginal distributions to restore the joint distribution. Patton (2006) decomposed the conditional joint distribution of a collection of two variables into their marginal distributions and a conditional copula. Thus, we can use it to link models of the conditional marginal distributions (e.g., ARMA-GARCH models) to form a dynamic conditional joint distribution

Value

result	Estimated parameter, Standard error, t-stat, p-value
AIC	Akaiki Information Criteria
BIC	Bayesain Information Criteria
Loglikelihood	Log Likeihood function'
tvtpdep	the vector of time varying dependence parameter

Author(s)

Woraphon Yamaka

References

Patton, A. J. (2006). Modelling asymmetric exchange rate dependence. International economic review, 47(2), 527-556.

Maneejuk, P., & Yamaka, W. (2019). Predicting Contagion from the US Financial Crisis to International Stock Markets Using Dynamic Copula with Google Trends. Mathematics, 7(11), 1032.

Examples

library(VineCopula)
library("tseries")
library("quantmod")
library("PerformanceAnalytics")

tckk <- c("^N225", "CL=F") # ticker names defined
numtk <- length(tckk);
ustart <- "2010-12-30";
uend <- "2020-2-29" # start and end date
all_dat <- list(); # empty list to fill in the data
for(i in 1:numtk)
{
  all_dat[[i]] <- xxx <- get.hist.quote(instrument = tckk[i], start=ustart, end=uend, quote = c("Close"), provider = "yahoo", compression = "m")
}
OIL=all_dat[[2]]+0.000001
N225=all_dat[[1]]
rOIL=diff(log(OIL))
rN225=diff(log(N225))
# normal margins
u=pnorm(rOIL/sd(rOIL))
v=pnorm(rN225/sd(rN225))

# Correlation rho
cor(u,v)
# maximum likelihood estimates for comparison
BiCopEst(u, v, family = 1, method = "mle")
# Dynamic Gaussian Copula
data=cbind(u,v)
model=dynamicnormal(data, plot=TRUE)

woraphonyamaka/TVTPcop documentation built on June 6, 2020, 9:47 a.m.