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Multivariate asset‐pricing model based on subordinated stable processes

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  • Vladimir Panov
  • Evgenii Samarin

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.

Suggested Citation

  • Vladimir Panov & Evgenii Samarin, 2019. "Multivariate asset‐pricing model based on subordinated stable processes," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 35(4), pages 1060-1076, July.
    • Handle: RePEc:wly:apsmbi:v:35:y:2019:i:4:p:1060-1076
    DOI: 10.1002/asmb.2446
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    References listed on IDEAS

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    1. Andersen, Torben G, 1996. "Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility," Journal of Finance, American Finance Association, vol. 51(1), pages 169-204, March.
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    Cited by:

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    2. Morozova, Ekaterina & Panov, Vladimir, 2025. "Bitcoin price modelling via analysis of Google Trends data: Lévy-based approach," Finance Research Letters, Elsevier, vol. 86(PA).
    3. Matteo Gardini & Piergiacomo Sabino & Emanuela Sasso, 2021. "Correlating Lévy processes with self-decomposability: applications to energy markets," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 1253-1280, December.

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