Author: Alexander J. McNeil and Martin Bladt
Date: 2024-02-16

# Basic Time Series Copula Processes

#### Alexander J. McNeil and Martin Bladt

#### 2024-02-16

This library currently implements 3 basic kinds of time series copula process: ARMA copula processes and d-vine copula processes of type 1 and type 2. These are described in the next 3 sections.

## 1. ARMA Copula Processes

### AR(1) Example

An ARMA copula is specified by a list of two vectors, the first named `ar` and the second `ma`. The following example creates an AR(1) copula process specification and then displays the spec.

    ar1 <- armacopula(list(ar = 0.7))
    ar1
    #> object class: armacopula
    #> name: ARMA(1,0)
    #> parameters:
    #> ar1
    #> 0.7

A realization can be generated with the generic command `sim`.

    set.seed(13)
    data1 <- sim(ar1, 1000)
    ts.plot(data1)

A model specification can be fitted to data with the generic command `fit`.

    ar1spec <- armacopula(list(ar = 0))
    ar1fit <- fit(ar1spec, data1)
    ar1fit
    #> object class: armacopula
    #> name: ARMA(1,0)
    #> _____________________
    #> Summary of estimates:
    #>    ar.ar1
    #> 0.7048399
    #> convergence status: 0, log-likelihood: 354.6794

### ARMA(1,1) Example

The next example simulates and fits an ARMA(1,1) copula process, giving standard errors for the parameter estimates.

    arma11 <- armacopula(list(ar = 0.95, ma = -0.85))
    data2 <- sim(arma11, 1000)
    ts.plot(data2)

    arma11spec <- armacopula(list(ar = 0.1, ma = 0.1))
    arma11fit <- fit(arma11spec, data2, tsoptions = list(hessian = TRUE))
    arma11fit
    #> object class: armacopula
    #> name: ARMA(1,1)
    #> _____________________
    #> Summary of estimates:
    #>         ar.ar1      ma.ma1
    #> par 0.95688150 -0.86695448
    #> se  0.01631116  0.02724842
    #> convergence status: 0, log-likelihood: 35.81021

Coefficients of the fitted model are obtained with the command `coef`, residuals with the command `resid` and various plots are generated by the generic command `plot`.

    coef(arma11fit)
    #>        ar1        ma1
    #>  0.9568815 -0.8669545
    res <- resid(arma11fit)
    acf(res)
    acf(abs(res))
    plot(arma11fit)
    plot(arma11fit, plottype = "kendall")
    mu_t <- resid(arma11fit, trace = TRUE)
    ts.plot(mu_t)

The data for these plots come from applying the Kalman filter command `kfilter`.

    head(kfilter(arma11fit@tscopula, data2))
    #>            mu_t   sigma_t       resid
    #> [1,]  0.0000000 1.0000000 -2.03983043
    #> [2,] -0.3381019 0.9861678 -0.81202147
    #> [3,] -0.4399684 0.9771623 -0.98922161
    #> [4,] -0.5479523 0.9710454 -0.09981730
    #> [5,] -0.5360906 0.9667704  1.16242677
    #> [6,] -0.3846082 0.9637228  0.06419146

## 2. D-Vine Copula Processes (type 1)

### Construction

We construct a copula of order \(p = 3\) in which the copulas are respectively Clayton, Frank, and Gauss. The parameters are given in a list. Individual copulas can be rotated through 180 degrees.

    copmod <- dvinecopula(
      family = c("Clayton","Frank", "Gaussian"),
      pars = list(1.2, 2, 0.15),
      rotation = c(180,0, 0)
    )
    copmod
    #> object class: dvinecopula
    #> name: d-vine(3)
    #> copula family:  clayton180 frank gaussian
    #> parameters:
    #> cop1.p1 cop2.p1 cop3.p1
    #>    1.20    2.00    0.15

### Simulation

A realization can be generated with the generic command `sim`.

    set.seed(29)
    data1 <- sim(copmod, n = 2000)
    hist(data1)
    ts.plot(data1)

### Estimation

A model spec can be fitted to data with the generic command `fit`.

    copspec <- dvinecopula(
      family = c("Clayton","Frank", "Gaussian"),
      pars = list(0.5, 1, 0),
      rotation = c(180, 0, 0)
    )
    copfit <- fit(copspec, data1,
                  tsoptions = list(hessian = TRUE),
                  control = list(maxit = 2000))
    copfit
    #> object class: dvinecopula
    #> name: d-vine(3)
    #> copula family:  clayton180 frank gaussian
    #> _____________________
    #> Summary of estimates:
    #>        cop1.p1   cop2.p1    cop3.p1
    #> par 1.06144147 1.9009764 0.15965799
    #> se  0.05300061 0.1252102 0.02115241
    #> convergence status: 0, log-likelihood: 448.3769
    coef(copfit)
    #>  cop1.p1  cop2.p1  cop3.p1
    #> 1.061441 1.900976 0.159658
    coef(copmod)
    #> cop1.p1 cop2.p1 cop3.p1
    #>    1.20    2.00    0.15

### Plotting

Various plots are generated by the generic command `plot`.

    plot(copfit)
    plot(copfit, plottype = "kendall")

Here is the generalized lag plot.

    plot(copfit, plottype = "glag")

## 3. D-Vine Copula Processes (type 2)

### Construction

We construct a model using the Joe copula and the Kendall partial autocorrelation function (KPACF) of a Gaussian ARMA process with autogressive (ar) parameter 0.9 and moving average (ma) parameter -0.85. The KPACF is truncated at lag 20, so this is a process of finite order. We can also set \(\text{maxlag} = \inf\) but this leads to much slower simulation.

    copmod <- dvinecopula2(family = "joe",
                           kpacf = "kpacf_arma",
                           pars = list(ar = 0.9, ma = -0.85),
                           maxlag = 20)
    copmod
    #> object class: dvinecopula2
    #> name: type2-d-vine
    #> copula family: joe
    #> KPACF: kpacf_arma with max lag 20
    #> parameters:
    #> [1]  0.90 -0.85

### Simulation

A realization can be generated with the generic command `sim`.

    set.seed(13)
    data1 <- sim(copmod, n = 2000)
    hist(data1)
    ts.plot(data1)

### Estimation

A model spec can be fitted to data with the generic command `fit`.

    copspec_Gauss <- dvinecopula2(family = "gauss",
                                  pars = list(ar = 0, ma = 0),
                                  maxlag = 20)
    fitGauss <- fit(copspec_Gauss, data1)
    fitGauss
    #> object class: dvinecopula2
    #> name: type2-d-vine
    #> copula family: gauss
    #> KPACF: kpacf_arma with max lag 20
    #> _____________________
    #> Summary of estimates:
    #>         ar         ma
    #>  0.9234119 -0.8695245
    #> convergence status: 10, log-likelihood: 21.26753

    copspec_Joe <- dvinecopula2(family = "joe",
                                pars = list(ar = 0, ma = 0),
                                maxlag = 20)
    fitJoe <- fit(copspec_Joe, data1)
    fitJoe
    #> object class: dvinecopula2
    #> name: type2-d-vine
    #> copula family: joe
    #> KPACF: kpacf_arma with max lag 20
    #> _____________________
    #> Summary of estimates:
    #>         ar         ma
    #>  0.9052847 -0.8535974
    #> convergence status: 0, log-likelihood: 48.98484

    AIC(fitGauss, fitJoe)
    #>          df       AIC
    #> fitGauss  2 -38.53506
    #> fitJoe    2 -93.96967

### Plotting

Various plots are generated by the generic command `plot`.

    plot(fitJoe)
    plot(fitJoe, plottype = "kendall")