Document Type
Article
Publication Date
1-2010
Publication Title
Computational Economics
Volume
35
Issue
1
Pages
1-23
Abstract
Comovements among asset prices have received a lot of attention for several reasons. For example, comovements are important in cross-hedging and cross-speculation; they determine capital allocation both domestically and in international mean-variance portfolios and also, they are useful in investigating the extent of integration among financial markets. In this paper we propose a new methodology for the non-linear modelling of bivariate comovements. Our approach extends the ones presented in the recent literature. In fact, our methodology, outlined in three steps, allows the evaluation and the statistical testing of non-linearly driven comovements between two given random variables. Moreover, when such a bivariate dependence relationship is detected, our approach creates a polynomial approximation. We illustrate our three-step methodology to the time series of energy related asset prices. Finally, we exploit this dependence relationship and its polynomial approximation to obtain analytical approximations of the Greeks for the European call and put options in terms of an asset whose price comoves with the price of the underlying asset.
Recommended Citation
Malliaris, Anastasios G.; Corazza, Marco; and Scalco, Elisa. Nonlinear Bivariate Comovements of Asset Prices: Theory and Tests. Computational Economics, 35, 1: 1-23, 2010. Retrieved from Loyola eCommons, School of Business: Faculty Publications and Other Works, http://dx.doi.org/10.1007/s10614-009-9186-2
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Copyright Statement
© Springer International Publishing AG 2010
Comments
Author Posting. © Springer International Publishing AG 2010. This is the author's version of the work. It is posted here by permission of Computational Economics for personal use, not for redistribution. The definitive version was published in Computational Economics, vol. 35, no.1, 2010, https://link.springer.com/article/10.1007/s10614-009-9186-2