Scalar Measures of Volatility and Dependence for the Multivariate Models with Applications to Asian Financial Markets

The variance–covariance matrix is a multi-dimensional array of numbers, containing information about the individual variabilities and the pairwise linear dependence of a set of variables. However, the matrix itself is difficult to represent in a concise way, particularly in the context of multivariate autoregressive conditional heteroskedastic models. The common practice is to report the plots of k ( k − 1 ) / 2 time-varying pairwise conditional covariances, where k is the number of markets (or assets) considered; thus, when k = 10 , there will be 45 graphs. We suggest a scalar measure of overall variabilities (and dependences) by summarizing all the elements in a variance–covariance matrix into a single quantity. The determinant of the covariance matrix Σ , called the generalized variance , can be used as a measure of overall spread of the multivariate distribution. Similarly, the positive square root of the determinant | R | of the correlation matrix, called the scatter coefficient , will be a measure of linear independence among the random variables, while collective correlation + ( 1 − | R | ) 1 / 2 will be an overall measure of linear dependence. In an empirical application to the six Asian market returns, these statistics perform the intended roles successfully. In addition, these are shown to be able to reveal and explain the empirical facts that cannot be uncovered by the traditional methods. In particular, we show that both the contagion and interdependence (among the national equity markets) are present and could be quantitatively measured in contrast to previous studies, which revealed only market interdependence.