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Nonlinear Correlation of Stock and Commodity Indices in Emerging and Developed Market
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Author(s): Kousik Guhathakurta (Indian Institute of Management Kozhikode, India), Sharad Nath Bhattacharya (Army Institute of Management, India & University of Calcutta, India), Santo Banerjee (Institute for Mathematical Research, University Putra Malaysia, Malaysia & International Science Association, Turkey)and Basabi Bhattacharya (Jadavpur University, India & Professional Risk Managers’ International Association Kolkata Chapter, India)
Copyright: 2013
Pages: 25
Source title:
Chaos and Complexity Theory for Management: Nonlinear Dynamics
Source Author(s)/Editor(s): Santo Banerjee (Politecnico di Torino, Italy)
DOI: 10.4018/978-1-4666-2509-9.ch004
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Abstract
The interrelationship between stock and commodity markets has been an issue of interest for both the academia and practitioners in the field of investment and wealth management. Traditionally, commodity has been a popular avenue for diversification in a mixed portfolio. However, this works well as long as there is little or no correlation between the two markets. This chapter presents an empirical investigation of the daily movement of stock and commodity index of two different countries to throw some light on the interrelationship between stock and commodity market. The uniqueness of this study lies in the choice of markets as also the methodology. The authors have chosen a developed market, viz., the US market, and an emerging market, viz., the Indian market. This study uses the major stock and commodity indices respectively for both countries for a period of three years. For analysis the authors have used the tools from nonlinear dynamics like recurrence analysis, power spectrum analysis, and delay based cross-correlation function. The investigation revealed that the dynamics of the time path of daily movement of Indian stock and commodity exchanges are much similar in nature while those of the US market are quite different. This chapter also models the respective time series using Geometric Brownian Motion and finds that the Indian data set performed much better than the US ones. This has a strong impact on strategy for designing mixed portfolios in Indian market.
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