Short- and long-term links among European and US stock markets

Gerrits, Robert-Jan; Yüce, Ayşe

doi:10.1080/096031099332483

Cited by 54 publications

(29 citation statements)

References 22 publications

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“…In existing literature, the following methods are usually used to measure the level of stock market return (or price) comovement and spillovers: (1) correlation coeffi cients (e.g., Koedijk et al, 2002;Longin and Solnik, 1995) to measure comovement; (2) Vector Autoregressive (VAR) models (Malliaris and Urrutia, 1992;Gilmore and McManus, 2002) to estimate spillover effects between stock markets; (3) cointegration analysis (Gerrits and Yuce, 1999;Patev et al, 2006) to fi nd long-term comovements between stock markets; (4) GARCH models (Tse and Tsui, 2002;Bae et al, 2003;Égert and Kočenda, 2010;Mazin et al, 2010) and regime switching models to model spillovers (Garcia and Tsafack, 2009;Schwender, 2010). A novel, but very promising approach, is wavelet analysis, which can be used to investigate both, stock market comovements and spillovers.…”

Section: Introductionmentioning

confidence: 99%

Interdependence Between Some Major European Stock Markets - A Wavelet Lead/Lag Analysis

Dajčman¹

2013

Prague Economic Papers

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Abstract:This paper investigates multiscale interdependence between the stock markets of Germany, Austria, France, and the United Kingdom. Wavelet energy additive decomposition was analyzed to investigate which scales capture the most energy (volatility), whereas a wavelet cross-correlation estimator was used to analyze comovement and lead/lag relationship between stock markets' return dynamics on a scale-by-scale basis. The main fi ndings of the paper are as follows. First, major fi nancial market crises had a signifi cant impact on return volatility of investigated stock markets. Among them, the global fi nancial crisis of 2007-2008 had the greatest and the most durable impact. Second, the lowest scale (associated with stock markets' return dynamics over a 2-4 days horizon) and the second lowest scale (associated with stock markets' return dynamics over 4-8 days horizon) MODWT (maximal overlap discrete wavelet transform) decompositions of stock markets' returns captured the greatest share (together about 70-80%) of indices' returns volatility. Third, comovement between stock market returns is a scale-dependent phenomenon. Fourth, a strong comovement between stock market returns of Germany, France, and the United Kingdom exists at all scales, while the Austrian stock market is less correlated with the three biggest stock markets in Europe. Fifth, the dynamics of stock market returns seems to be well time-synchronized at daily (raw returns) and the lowest scale (scale ) return decomposition as most of the return innovations are transmitted between stock markets intraday. Sixth, at the highest investigated scale (associated with stock markets' return dynamics over a 64-128 days horizon), signifi cant leads and lags between dynamics of stock markets' returns were detected. The time-synchronization of the stock markets' return dynamics for investments of 64 to 128 days horizon is less perfect than for investments of shorter investment horizons.

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Section: Introductionmentioning

confidence: 99%

Interdependence Between Some Major European Stock Markets - A Wavelet Lead/Lag Analysis

Dajčman¹

2013

Prague Economic Papers

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“…A better econometrically understanding of assets returns may be achieved by applying some methods: vector autoregressive (VAR) models proposed by Malliaris and Urrutia (1992), and Gilmore and McManus (2002), cointegration analysis introduced by Gerrits and Yuce (1999), and Patev et al (2006), generalised autoregressive conditional heteroskedasticity (GARCH) models proposed by Engle (2001), Tse and Tsui (2002), Bae et al, (2003), Égert and Kočenda (2010), and Cho and Parhizgari (2008) and regime switching models (Schwender, 2010).…”

Section: Literature Reviewmentioning

confidence: 99%

Athen's game of chicken or the conditional dependence between the Greek banks

Derbali

Hallara

2016

IJEA

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Abstract:In this paper, we investigate empirically evidence to examine the conditional dependence between the Grecian banks. Then, we use, first, the methodology GARCH-DCC based on the dynamic process of dependence and, second, we use the methodology GARCH-DECO based on the constant process of dependence. The two methodologies DCC and DECO proposed, respectively, by Engle (2002), and Engle and Kelly (2009)

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“…In the literature the following methods are often used to measure the level of stock market comovement: correlation coefficients (e.g. [28], [31]), Vector Autoregressive (VAR) models ( [20], [33]), cointegration analysis ( [19], [36]), GARCH models ( [1], [5], [10], [50]) and regime switching models ( [13], [45]). A novel but promising approach is a wavelet analysis of stock market comovement.…”

Section: Introductionmentioning

confidence: 99%