The International Price Transmission in Stock Index Futures Markets

Yang, Jian; Bessler, David A.

doi:10.2139/ssrn.433322

Cited by 6 publications

(12 citation statements)

References 34 publications

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“…There have been relatively few applications of graph‐theoretic search algorithms to economic data. Some examples include Sheffrin and Triest (1998), Akleman, Bessler and Burton (1999), Bessler and Loper (2001), Yang and Bessler (2004), and Haigh, Nomikos and Bessler (2004). Swanson and Granger (1997), Demiralp (2000, ch.…”

mentioning

confidence: 99%

A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression*

Demiralp¹,

Hoover²,

Perez³

2008

Oxf Bull Econ Stat

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of A Bootstrap Method for Identifying and Evaluating a Structural Vector AutoregressionGraph-theoretic methods of causal search based in the ideas of Pearl (2000), Spirtes, Glymour, and Scheines (2000), and others have been applied by a number of researchers to economic data, particularly by Swanson and Granger (1997) to the problem of finding a data-based contemporaneous causal order for the structural autoregression (SVAR), rather than, as is typically done, assuming a weakly justified Choleski order. Demiralp and Hoover (2003) provided Monte Carlo evidence that such methods were effective, provided that signal strengths were sufficiently high. Unfortunately, in applications to actual data, such Monte Carlo simulations are of limited value, since the causal structure of the true data-generating process is necessarily unknown. In this paper, we present a bootstrap procedure that can be applied to actual data (i.e., without knowledge of the true causal structure). We show with an applied example and a simulation study that the procedure is an effective tool for assessing our confidence in causal orders identified by graph-theoretic search procedures. These methods exploit patterns of conditional independence in the data. In cases in which unique identification is not possible, they may nonetheless reduce the class of admissible identifying assumptions considerably. 1 Demiralp and Hoover (2003) provide KeywordsMonte Carlo evidence that, provided signal-to-noise ratios are high enough, these graphtheoretic methods can recover effectively the true contemporaneous structure of SVARs.Monte Carlo results are too often specific to particular simulations and do not necessarily provide generic guidance. To remedy this problem, we develop a bootstrap method that 3 allows one to assess the reliability of a data-based identification scheme for arbitrary structures. We show that this technique allows us, in cases in which we do not know the true underlying structure, to present simulation evidence that closely mimics Monte Carlo simulations for which we know the underlying structure ex hypothesi. It therefore allows us to give reasonable assessments of the reliability of data-based, graph-theoretic identifications of SVARs.The SVAR can be written as: (2) is easily estimated, the covariance matrix, Λ = E(UU′) in general will not be diagonal, so that it will be impossible to evaluate the effects of shocks to particular variables. The identification problem reduces to this: if we know A 0 , then it is easy to recover equation (1) from our estimates of (2); but how do we know A 0 ?Identification schemes typically start with the property that Σ, the covariance matrix of E t , is diagonal. True identification would permit us to transform equation (2) into (1) and recover the diagonal Σ. There are a large number of N × N matrices, P i such that the covariance matrix ) )' ( ( There are, in general, n! such matrices, each corresponding to one Wold-or recursive causal ordering of the variables in Y.We too restrict ourselves ...

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mentioning

confidence: 99%

A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression*

Demiralp¹,

Hoover²,

Perez³

2008

Oxf Bull Econ Stat

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“…This paper extends this literature by adopting the techniques in Bessler and Yang (2004) to provide evidence of structural change in stock market linkages and price transmission in response to the 2007-2012 financial crisis. We first divide post-2000 weekly stock index data from ten prominent European markets into three periods representing pre-crisis (2000)(2001)(2002)(2003)(2004)(2005)(2006), crisis (2007)(2008)(2009)(2010)(2011)(2012), and post-crisis (2013)(2014)(2015)(2016).…”

Section: Introductionmentioning

confidence: 90%

“…Following Yang and Bessler (2004) and Refalo (2009), we first apply a cointegrated VAR model to evaluate the data. Letting denote a vector of ten indexes (k=10), the corresponding vector ECM is specified as:…”

Section: Error Correction Modellingmentioning

confidence: 99%

“…The method is extensively discussed in science literature in Spirtes et al (2000) and Glymour and Cooper (1999). DAG has been applied to studying financial data in a number of other papers including Bessler and Yang (2003), Yang and Bessler (2004), Haigh et al (2004), and Li et al (2008), and Refalo (2009). The latter two papers provide a detailed overview of the algorithm (Li et al 2008 illustrate how the algorithm works graphically).…”

Section: Directed Acyclic Graphsmentioning

confidence: 99%

“…However, virtually all of these models rely on some form of temporal causality. Yang and Bessler (2004) extended the literature by using the method of directed acyclic graphs (DAGs) in combination with error correction modelling to explore evidence of contemporaneous causal patterns in international equity market data.…”

Section: Introductionmentioning

confidence: 99%

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The Price Transmission in European Stock Markets

Feng

Refalo

2018

AFL

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We investigate the dynamic price relationships among ten major stock indexes in Europe before, during and after the recent financial crisis. Using an error-correction model we find that the stock markets are cointegrated with three cointegrating vectors before the crisis and that the markets are cointegrated with only one cointegrating vector during and after the crisis. We further apply directed acyclic graph (DAG) analysis on the contemporaneous correlations innovation matrix to explore the instantaneous transmission pattern. The results show that France and Spain appear to share leadership roles before the crisis while leadership role is less obvious during and after the crisis. We also find a decreasing number of instantaneous casual relationships between the markets after the crisis, indicating that the markets are becoming more independent.

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