This paper examines the dynamics of the linkages between Shanghai and Hong Kong stock indices. While the volatility linkage is analysed by a multivariate GARCH framework, the dependence of returns is examined by a copula approach. Eight different copula functions are applied in this study including two time varying ones which capture the time varying process of the linkage. The result shows sig...

One approach for constructing copula functions is by multiplication. Given that products of cumulative distribution functions (CDFs) are also CDFs, an adjustment to this multiplication will result in a copula model, as discussed by Liebscher (J Mult Analysis, 2008). Parameterizing models via products of CDFs has some advantages, both from the copula perspective (e.g., it is well-defined for any...

Estimating the probability of extreme temperature events is difficult because of limited records across time and the need to extrapolate the distributions of these events, as opposed to just the mean, to locations where observations are not available. Another related issue is the need to characterize the uncertainty in the estimated probability of extreme events at different locations. Although...

This paper develops a novel approach to modeling and forecasting realized volatility (RV) measures based on copula functions. Copula-based time series models can capture relevant characteristics of volatility such as nonlinear dynamics and long-memory type behavior in a flexible yet parsimonious way. In an empirical application to daily volatility for S&P500 index futures, we find that the copu...

In this paper we address the problem of option ranking in qualitative evaluation models. Current approaches make the assumptions that when qualitative data are suitably mapped into discrete quantitative data, they form monotone or closely linear tabular value functions. Although the power of using monotone and linear functions to model decision maker’’s preferences is impressive, there are many...

Parametric conditional copulamodels allow the copula parameters to vary with a set of covariates according to an unknown calibration function. Flexible Bayesian inference for the calibration function of a bivariate conditional copula is introduced. The prior distribution over the set of smooth calibration functions is built using a sparse Gaussian process (GP) prior for the single index model (...

Abstract The Pearson product-moment correlation commonly used as statistical dependence measure was developed assuming normal marginal and addresses only linear dependence. In most applications, the distribution is assumed to be a multivariate normal or lognormal for tractable calculus even if the assumption may not be appropriate. A copula based approach couples marginal distributions to form ...

We tackle the challenge of efficiently learning the structure of expressive multivariate realvalued densities of copula graphical models. We start by theoretically substantiating the conjecture that for many copula families the magnitude of Spearman’s rank correlation coefficient is monotonic in the expected contribution of an edge in network, namely the negative copula entropy. We then build o...

In this paper, we propose a score fusion method using a mixture copula that can consider complex dependencies between multiple relevance scores in order to improve the effectiveness of information retrieval. The combination of multiple relevance scores has been shown to be effective in comparison with a single score. Widely used score fusion methods are linear combination and learning to rank. ...