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Co-movements and contagion between international stock index futures markets

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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.

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Notes

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

  2. 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).

  3. 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.

  4. 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.

  5. 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.

  6. 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).

  7. 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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Authors and Affiliations

  1. Management Department, Politehnica University of Timisoara, 2, P-ta. Victoriei, 300006, Timisoara, Romania

    Claudiu Tiberiu Albulescu

  2. CRIEF, University of Poitiers, 2, Rue Jean Carbonnier, Bat. A1, BP 623, 86022, Poitiers, France

    Daniel Goyeau

  3. Faculty of Management, IBS Hyderabad, IFHE University, Dontanpalli, Hyderabad, 501203, India

    Aviral Kumar Tiwari

Authors
  1. Claudiu Tiberiu Albulescu
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  2. Daniel Goyeau
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  3. Aviral Kumar Tiwari
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Corresponding author

Correspondence to Claudiu Tiberiu Albulescu.

Appendices

Appendix 1: Returns and volatility for selected stock index futures

See Fig. 7.

Fig. 7
figure 7

Stock index futures returns and volatility

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Appendix 2: Futures indexes: additional results (returns)

See Figs. 8 and 9.

Fig. 8
figure 8

Wavelet coherence—additional results (returns)

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Fig. 9
figure 9

Rolling wavelet correlation—additional results (returns)

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Appendix 3: Futures indexes: additional results (volatility)

See Figs. 10 and 11.

Fig. 10
figure 10

Wavelet coherence—additional results (volatility)

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Fig. 11
figure 11

Rolling wavelet correlation—additional results (volatility)

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Albulescu, C.T., Goyeau, D. & Tiwari, A.K. Co-movements and contagion between international stock index futures markets. Empir Econ 52, 1529–1568 (2017). https://doi.org/10.1007/s00181-016-1113-5

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  • DOI: https://doi.org/10.1007/s00181-016-1113-5

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