Multivariate asset‐pricing model based on subordinated stable processes

Abstract

In this paper, we consider a multidimensional time‐changed stochastic process in the context of asset‐pricing modeling. The proposed model is constructed from stable processes, and its construction is based on two popular concepts: multivariate subordination and Lévy copulas. From a theoretical point of view, our main result is Theorem 1, which yields a simulation method from the considered class of processes. Our empirical study shows that the model represents the correlation between asset returns quite well. Moreover, we provide some evidence that this model is more appropriate for describing stock prices than classical time‐changed Brownian motion, at least if the cumulative amount of transactions is used for a stochastic time change.

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