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The steps to fit copula models: Estimate the margins. Transform the data from original to copula data; you can use the pobs function from the copula package in R. … Copula …

Copula: Very complicated in multivariate cases. Not all copula can be extended into multivariate copula. It imposes the same dependency among all variables, not the case for many real-life data. … Vine copula: Allow to model two variables at a time. Hence, there is no limitation on the type of copula used. …

Copula only works on standard uniform margins. So, if you have real data you need to transform it into copula data using the pobs function. … Vine Copula: Vine copula can be identified as an extended version of the copula. It is very flexible and works with d >= 3, where d is the number of variables. …

I have simulated copula data and computed the MI and the results are as follows: Independent copula (MI = 0.04) Very weak dependency Gaussian copula (MI = -0.03)! … Is that because the relationship between mutual information and copula is negative? Is it acceptable to have > 0 for completely independent variables. I used an 'infotheo` package in R. …

I have 20 years of daily data set for some stock prices. I would like to investigate the autocorrelation for this using acf plot and Ljung-Box test. However, I wonder how to set the lag.max that requi …

transform the residuals to the copula data (uniform margins. Then, estimate the model parameters). … Generate 100 1-day a hed forecast from copula Simulate from copula transform the simulated data to the original margins. I really understand the first 3 steps. …

After that, I need to transfer the residuals to uniform margins to fit the copula model. My question is, how to do the latter step using IFM by Joe, in R? … So, to use IFM method of Joe, I can use U. <- pt(Z.) to transfer the standardized residuals to copula data. Is that correct? …

My problem starts when I try to understand why copula relies on CDF and not on PDF. I searched and found this: the probability density function can be hard to work with directly. … So, 1- Why copula relies on CDF and not PDf? 2- Why is it hard to work with PDF directly? 3- Why always CDF is used in integral transformation function. …

I read a book titled "Statistics and Data Analysis for Financial Engineering with R examples". At page 203, I read the following paragraph. "Figure 8.7 shows density histograms of both samples of unif …

I would like to fit a copula model to this data (just to understand the dependency between these two variables. I do not want to fit a regression copula model). …

I applied a copula model to my data. Then I plot the surface plot for each selected copula function. As I understand, each colour in the plot indicates the strength of the dependency. … Based on this, I supposed that the plot for Frank and Gaussian copula will give me red colour in the middle and blue at the tails! However, I got the opposite. …

Suppose I have a data with Gaussian copula with ($\rho =0.8$). Suppose further the contour plot is as follows: My question is: How can I interpret the result with respect to my original values? …

Selecting the best-fit copula family is an important step in the copula model. One way to select the best-fit copula is by using a scatter plot to identify the shape of the dependency structures. …

Suppose I have two variables and that their dependency structure is Gaussian copula. Suppose that the parameter of the Gaussian copula is 0.8 and the corresponding Kendall's tau is 0.7. …

I am reading about Frank copula. From my reading, I found that Frank can model both positive and negative dependency structure between two variables only at the center of the scatter plot. … However, from the plot of Frank copula, the upper tails are at the top corner (at 1) so, I think should be strong dependency structure. Any idea, please? …