Calculation of Higher-Order Cross-moments How can I calculate standardized central cross-moments for 2 time-series?
The 4th-order standardized central moment, kurtosis, is;
 kurt = mean((x-mean(x)).^4)./mean(x.^2).^2; 

It should be something like the kurt, for x and y.
I also want to know the 3rd order cross-moment (something like skewness).
I am trying to write a code in MATLAB.
 A: You are in essence looking for a multivariate measure of skew and kurtosis. There are many. I would start with the most establish ones, which are the multivariate skew and kurtosis measures of Mardia 1977 [0].
It seems to me you are more asking for an implementation than about the measures themselves. I don t know of any matlab implementation, but the R code below (from the R library psych) should be fairly easy to translate in matlab:
mardia <- function(x, na.rm=TRUE, plot=TRUE) {
  cl <- match.call()
  x <- as.matrix(x)     # in case it was a dataframe
  if(na.rm) x <- na.omit(x)
  n <- dim(x)[1]
  p <- dim(x)[2]
  x <- scale(x,scale=FALSE)  # zero center
  S <- cov(x)
  S.inv <- solve(S)
  D <- x %*% S.inv %*% t(x)
  b1p <- sum(D^3)/n^2
  b2p <- tr(D^2)/n 
  chi.df <- p*(p+1)*(p+2)/6
  k <- (p+1)*(n+1)*(n+3)/(n*((n+1)*(p+1) -6))

  small.skew <- n*k*b1p/6
  M.skew <- n*b1p/6
  M.kurt <- (b2p - p * (p+2))*sqrt(n/(8*p*(p+2)))
  p.skew <- 1-pchisq(M.skew,chi.df)
  p.small <- 1 - pchisq(small.skew,chi.df)
  p.kurt <- 2*(1- pnorm(abs(M.kurt)))
  d = sqrt(diag(D))
  if(plot) {qqnorm(d)
            qqline(d)}
  results <- list(n.obs=n, n.var=p, b1p=b1p,b2p= b2p, skew=M.skew, 
                  small.skew=small.skew, p.skew=p.skew, p.small=p.small, 
                  kurtosis=M.kurt, p.kurt=p.kurt, d=d, Call=cl)
  class(results) <- c("psych", "mardia")
  return(results)
}



*

*[0] K.V. Mardia (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3):pp. 519-30, 1970.

