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Empirical Economics

Erschienen in:

11.07.2016

Co-movements and contagion between international stock index futures markets

verfasst von: Claudiu Tiberiu Albulescu, Daniel Goyeau, Aviral Kumar Tiwari

Erschienen in: Empirical Economics | Ausgabe 4/2017

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Abstract

In this paper, we explore the co-movements and contagion between six international stock index futures markets. In contrast to the empirical studies which dominate the literature and focus on the case of spot markets, relatively little is known about the returns and the volatility dynamics of the futures markets. To address this deficiency, we employ a time–frequency approach and discover that the co-movements between the international markets manifest especially in the long run. Nevertheless, the contagion phenomenon associated with the very short-run horizon is present in particular in the case of the European markets, due to their higher level of integration. The rolling wavelet correlation increases after severe turbulence episodes, but fluctuates over time and across frequencies. Our findings can guide the international investors in stock index futures markets to accurately diversify their portfolio in crisis periods.

Vorheriger Artikel Is stock investment contagious among siblings?

Nächster Artikel The evolving nature of intraday price discovery in the Chinese CSI 300 index futures market

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We use the theoretical distribution of the wavelet power spectrum for computing the significance levels.

The choice of the length of the window is no straightforward task. It is influenced by the data sample and by the previous works. A longer window implies the loss of time information, and a shorter window implies the loss of frequency information. The choice of the window’s length is based on the previous works of Benhmad (2013) and Ranta (2013).

Alternatively, one can also test the stability of the relation before and after a crisis event through the wavelet detail coefficients. However, the t test can be considered as a robustness analysis, which is easier to interpret.

The use of daily data is common in wavelet analysis applied to financial data, due to their accessibility. Moreover, the number of observations in our sample is adequate for wavelet analysis. As Rua (2013) shows, the wavelet approach compensates the small-frequency data problem and can be applied even on annual data, by applying a tighter resolution.

We have retained this simple volatility measure for two reasons. First, the well-known volatility estimators of Garman and Klass (1980) and Rogers and Satchell (1991) are focused on the variance and not on the volatility, which represent the obvious interest for financial applications. Second, the weights assigned to the quadratic unbiased variance estimators in the Garman and Klass (1980) model are often criticized in the literature. In addition, comparing different technique for volatility estimation is out of the purpose of the present paper.

The data are widely disseminated by data vendors and market makers and can truly be viewed as public information available to all investors (for a discussion about the benefits of using freely available data, see Giot 2005).

After computing the rolling correlation, this crisis event remains located at the beginning of our sample. Thus, we cannot compare the correlation before and after May 2, 2010.

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Titel: Co-movements and contagion between international stock index futures markets
verfasst von: Claudiu Tiberiu Albulescu
Daniel Goyeau
Aviral Kumar Tiwari
Publikationsdatum: 11.07.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 4/2017
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-016-1113-5

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