A Random Matrix Approach To Volatility In An Indian Financial Market

Kulkarni, Varsha S.; Deo, Nivedita

doi:10.1007/978-88-470-0502-0_4

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“…While it is generally believed that stock prices in emerging markets tend to be relatively more correlated than the developed ones [19], there have been very few studies of the former in terms of analysing the spectral properties of correlation matrices [20,21,22,23]. Most studies of correlated price movements in emerging markets have looked at the synchronicity which measures the incidence of similar (i.e., up or down) price movements across stocks [19,24].…”

Section: Introductionmentioning

confidence: 99%

Collective behavior of stock price movements in an emerging market

2007

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To investigate the universality of the structure of interactions in different markets, we analyze the cross-correlation matrix C of stock price fluctuations in the National Stock Exchange (NSE) of India. We find that this emerging market exhibits strong correlations in the movement of stock prices compared to developed markets, such as the New York Stock Exchange (NYSE). This is shown to be due to the dominant influence of a common market mode on the stock prices. By comparison, interactions between related stocks, e.g., those belonging to the same business sector, are much weaker. This lack of distinct sector identity in emerging markets is explicitly shown by reconstructing the network of mutually interacting stocks. Spectral analysis of C for NSE reveals that, the few largest eigenvalues deviate from the bulk of the spectrum predicted by random matrix theory, but they are far fewer in number compared to, e.g., NYSE. We show this to be due to the relative weakness of intra-sector interactions between stocks, compared to the market mode, by modeling stock price dynamics with a two-factor model. Our results suggest that the emergence of an internal structure comprising multiple groups of strongly coupled components is a signature of market development.

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