Hostname: page-component-7b7567c8c-fvjsw Total loading time: 0 Render date: 2026-11-15T11:47:50.925Z Has data issue: false hasContentIssue false
  • English
  • Français

MODELING INTERNATIONAL STOCK PRICE COMOVEMENTS WITH HIGH-FREQUENCY DATA

Published online by Cambridge University Press:  21 November 2017

Hachmi Ben Ameur,
Fredj Jawadi,
Wael Louhichi and
Abdoulkarim Idi Cheffou
Hachmi Ben Ameur*
Affiliation:
INSEEC Business School
Fredj Jawadi
Affiliation:
University of Evry
Wael Louhichi
Affiliation:
ESSCA School of Management
Abdoulkarim Idi Cheffou
Affiliation:
EDC Paris Business School
*
Address correspondence to: Hachmi Ben Ameur, INSEEC Business School, 27 avenue Claude Vellefaux 75010 Paris, France; e-mail: hbenameur@inseec.com.
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

This paper studies stock price comovements in two key regions [the United States and Europe, which is represented by three major European developed countries (France, Germany, and the United Kingdom)]. Our paper uses recent high-frequency data (HFD) and investigates price comovements in the context of “normal times” and crisis periods. To this end, we applied a non-Gaussian Asymmetrical Dynamic Conditional Correlation (ADCC)-GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model and the Marginal Expected Shortfall (MES) approach. This choice has three advantages: (i) With the development of high-frequency trading (HFT), it is more appropriate to use HFD to test price linkages for overlapping and nonoverlapping data. (ii) The ADCC-GARCH model captures further asymmetry in price comovements. (iii) The use of the MES enables to measure systemic risk contributions around the distribution tails. Accordingly, we offer two interesting findings. First, while the hypothesis of asymmetrical and time-varying stock return linkages is not rejected, the MES approach indicates that both European and US indices make a considerable contribution to each other's systemic risk, with significant input from Frankfurt to the French and US markets, especially following the collapse of Lehman Brothers. Second, we show that the propagation of systemic risk is higher during the crisis period and overlapping trading hours than during nonoverlapping hours. Thus, the MES test is recommended as an indicator to help monitor market exposure to systemic risk and to gauge expected losses for other markets.

Keywords

Price ComovementsSystemic RiskHFDADCC-GARCHMES

Information

Type
Articles
Information
Macroeconomic Dynamics , Volume 22 , Special Issue 7: Recent Insights into Financial, Housing, and Monetary Markets , October 2018, pp. 1875 - 1903
Copyright
Copyright © Cambridge University Press 2017 

Footnotes

We would like to thank two anonymous referees and Marc Paolella for their constructive comments on an earlier version of this paper. We also thank Ramo Gencay, Esfandiar Maasoumi, Dennis Kristensen, Timo Teräsvirta, Ruey Tsay, and the participants of the Second International Workshop on “Financial Markets and Nonlinear Dynamics” (Paris, June 4 and 5, 2015) for their helpful comments.

References

REFERENCES

Acharya, V. V., Pedersen, L. H., Philippon, T., and Richardson, M. P. (2010) Measuring systemic risk. AFA 2011 Denver Meetings paper. Available at SSRN: http://ssrn.com/abstract=1573171.Google Scholar
Adrian, T. and Brunnermeier, M. (2016) “CoVaR”. American Economic Review 106 (7), 1705–1741.Google Scholar
Ait-Sahalia, Y. and Yu, J. (2009) High frequency market microstructure noise estimates and liquidity measures. Annals of Applied Statistics 3 (1), 422–457.Google Scholar
Anderson, T., Bollerslev, T., and Meddahi, N. (2011) Realized volatility forecasting and market microstructure noise. Journal of Econometrics 160 (1), 220–234.Google Scholar
Arouri, M. E. H., Jawadi, F., and Nguyen, D. K. (2012) Modeling nonlinear and heterogeneous dynamic links in international monetary markets. Macroeconomic Dynamics 16, 232–251.Google Scholar
Artzner, P., Delbaen, F., Eber, J. M., and Heath, D. (1999) Coherent measures of risk. Mathematical Finance 3, 203–228.Google Scholar
Baele, L. (2005) Volatility spillover effects in European equity markets. Journal of Financial and Quantitative Analysis 40, 373–401.Google Scholar
Baur, D. and Jung, R. C. (2006) Return and volatility linkages between the US and the German stock market. Journal of International Money and Finance 25, 598–613.Google Scholar
Bauwens, L., Omrane, W. Ben, and Giot, P. (2005) News announcements, market activity and volatility in the Euro/Dollar foreign exchange market. Journal of International Money and Finance 24, 1108–1125.Google Scholar
Bauwens, L., Laurent, S., and Rombouts, J. V. K. (2006) Multivariate GARCH models: A survey. Journal of Applied Econometrics 21, 79–109.Google Scholar
Bekaert, G. and Harvey, C. R. (1995) Time-varying world market integration. Journal of Finance 1 (2), 403–444.Google Scholar
Bekaert, G. and Wu, G. (2000) Asymmetric volatility and risk in equity markets. Review of Financial Studies 13 (1), 1–42.Google Scholar
Ben Ameur, H., Jawadi, F., and Louhichi, W. (2013) Do the US trends drive the UK–French market linkages? Empirical evidence from a threshold intraday analysis. Applied Economics Letters 20 (5), 499–503.Google Scholar
Bollerslev, T. (1986) Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31, 307–327.Google Scholar
Bollerslev, T. and Zhang, B. (2003) Measuring and modeling systematic risk in factor pricing models using high-frequency data. Journal of Empirical Finance 10, 533–558.Google Scholar
Brownlees, C. T. and Engle, R. F. (2012) Volatility, correlation and tails for systemic risk measurement. Available from https://bfi.uchicago.edu/research/working-paper/volatility-correlation-and-tails-systemic-risk-measurement.Google Scholar
Calvo, S. and Reinhart, C. M. (1996) Capital flows to Latin America: Is there evidence of contagion effects? In Calvo, Guillermo A., Goldstein, Morris, and Hochreiter, Eduard (eds.), Private Capital Flows to Emerging Markets After the Mexican Crisis, pp. 151–171. Washington, DC: Peterson Institute for International Economics.Google Scholar
Cao, Z. (2012) Multi-CoVaR and Shapley Value: A Systemic Risk Measure. Working paper, Banque de France.Google Scholar
Cappiello, L., Engle, R. F., and Sheppard, K. (2006) Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics 4 (4), 537–572.Google Scholar
Chang, C. C., Chen, S. S., Chou, R. K., and Hsin, C. W. (2011) Intraday return spillovers and its variations across trading sessions. Review of Quantitative Finance and Accounting 36, 355–390.Google Scholar
Chiang, T. C., Jeon, B. N., and Li, H. (2007) Dynamic correlation analysis of financial contagion: Evidence from Asian markets. Journal of International Money and Finance 26, 1206–1228.Google Scholar
Chlibi, S., Jawadi, F., and Sellami, M. (2015) Analyzing heterogeneous stock price comovements through hybrid approaches. Open Economies Review 27 (3), 541–559.Google Scholar
Chlibi, S., Jawadi, F., and Sellami, M. (2016) Modeling threshold effects in stock price co-movements: A vector nonlinear cointegration approach. Studies in Nonlinear Dynamics and Econometrics 21 (1), 1–17.Google Scholar
Dobrev, D. and Szerszen, P. (2010) The Information Content of High-Frequency Data For Estimating Equity Return Models and Forecasting Risk. Finance discussion papers No. 1005, FRB International.Google Scholar
Engle, R. F. (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica 50, 987–1008.Google Scholar
Engle, R. F. and Kroner, K. F. (1995) Multivariate simultaneous generalized ARCH. Econometric Theory 11, 122–150.Google Scholar
Engle, R. F. and Sheppard, K. (2001) Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH. Working paper no. 8554, National Bureau of Economic Research.Google Scholar
Engle, R. F., Shephard, N., and Sheppard, K. (2008) Fitting and Testing Vast Dimensional Time-Varying Covariance Models. NYU working paper no. FIN-07-046.Google Scholar
Forbes, K. and Rigobon, R. (2002) No contagion, only interdependence: Measuring stock market co-movements. Journal of Finance 57, 2223–2261.Google Scholar
Girardin, E. and Liu, Z. (2007) The financial integration of China: New evidence on temporally aggregated data for the A-share market. China Economic Review 18, 354–371.Google Scholar
Hamao, Y. R., Masulis, R. W., and Ng, V. K. (1990) Correlation in price changes and volatility across international stock markets. Review of Financial Studies 3, 281–307.Google Scholar
Härdle, W., Hautsch, N., and Pigorsch, U. (2008) Measuring and modeling risk using high-frequency data. In Härdle, W. K., Hautsch, N., and Overbeck, L. (eds.), Applied Quantitative Finance, pp. 275–293. Berlin: Springer.Google Scholar
Harris, Lawrence (1986) A transaction data study of weekly and intradaily patterns in stock returns. Journal of Financial Economics 16, 99–117.Google Scholar
Hendershott, T. and Riordan, R. (2009) Algorithmic Trading and Information. Working paper # 09–08, Net Institute.Google Scholar
Hendershott, T., Jones, C. M., and Menkveld, A. J. (2011) Does algorithmic trading improve liquidity? Journal of Finance 66, 1–33.Google Scholar
Jaffe, J. and Westerfield, R. (1985) The weekend effect in common stock returns: The international evidence. Journal of Finance 40, 433–454.Google Scholar
Jawadi, F., Arouri, M. H., and Million, N. (2009) Stock market integration in the Latin American markets: Further evidence from nonlinear modeling. Economics Bulletin 29, 162–168.Google Scholar
Jawadi, F., Idi Cheffou, A., and Louhichi, W. (2015a) Testing and modeling jump contagion across international stock markets: A nonparametric intraday approach. Journal of Financial Markets 26, 64–84.Google Scholar
Jawadi, F., Louhichi, W., and IdiCheffou, K. (2015b) Intraday bidirectional volatility spillover across international stock markets: Does the global financial crisis matter? Applied Econonomics 45, 3633–3650.Google Scholar
Jeong, G. J. (1999) Cross-border transmission of stock price volatility: Evidence from the overlapping trading hours. Global Finance Journal 10, 54–70.Google Scholar
Kalev, P. S., Liu, W. M., Pharm, P. K., and Jarnecic, E. (2004) Public information arrival and volatility of intraday stock returns. Journal of Banking and Finance 28, 1441–1467.Google Scholar
Koutmos, G. and Booth, G. (1995) Asymmetric volatility transmission in international stock markets. Journal of International Money and Finance 14, 747–762.Google Scholar
Koutmos, G. (1996) Modelling the dynamic interdependence of major European stock markets. Journal of Business Finance and Accounting, 23, 975–988.Google Scholar
Krause, J. and Paolella, M. S. (2014) A fast, accurate method for value-at-risk and expected shortfall. Econometrics 2, 98–122.Google Scholar
Kuester, K., Mittnik, S., and Paolella, M. S. (2006) Value-at-risk prediction: A comparison of alternative strategies. Journal of Financial Econometrics 4 (1), 53–89.Google Scholar
Lee, S. J. (2009) Volatility spillover effects among six Asian countries. Applied Economics Letters 16, 501–508.Google Scholar
Louhichi, W. (2011) What drives the volume-volatility relationship on Euronext Paris? International Review of Financial Analysis 20, 200–206.Google Scholar
Nam, J. H., Yuhn, K. H., and Kim, S. B. (2008) What happened to pacific-basin emerging markets after the 1997 financial crisis? Applied Financial Economics 18, 639–658.Google Scholar
Ng, A. (2000) Volatility spillover effects from Japan and the US to the Pacific–Basin. Journal of International Money and Finance 19, 207–233.Google Scholar
Scaillet, O. (2005) Nonparametric estimation of conditional expected shortfall. Insurance and Risk Management Journal 74, 639–660.Google Scholar
Schwert, G. W. (1998) Stock Market Volatility: Ten Years After the Crash. NBER working paper no. 6381.Google Scholar
Susmel, R. and Engle, R. F. (1994) Hourly volatility spillovers between international equity markets. Journal of International Money and Finance 13, 3–25.Google Scholar
Taylor, S. J. and Xu, X. (1997) The incremental volatility information in one million foreign exchange quotations. Journal of Empirical Finance 4, 317–340.Google Scholar