Spatial linkages in international financial markets

Fernández, Viviana

doi:10.1080/14697680903127403

Cited by 63 publications

(42 citation statements)

References 12 publications

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“…The recent surge of interest in spatial modelling has resulted in the application of spatial econometrics in a wide range of empirical investigations in more traditional fields of economics including, among others, growth regressions , financial markets (Fernandez, 2011) and housing prices (Gerkman, 2010). Particularly, growth regression in the lens of spatial econometrics has been a growing area of interest.…”

Section: Introductionmentioning

confidence: 99%

On the Link between Volatility and Growth: A Spatial Econometrics Approach

Abate¹

2015

Spatial Economic Analysis

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This paper examines the link between macro volatility and economic growth in the lens of spatial econometrics. We present an unconstrained spatial Durbin Ramey-Ramey model. We test the extended model in a panel of 78 countries to investigate all the possible dimensions along which spatial interactions can affect the link between macro volatility and growth. In contrast to previous literature, we split the effects of volatility on growth into direct and indirect effects using partial derivative impacts approach. We found that both the direct and indirect effects of volatility on growth are negative; the latter effect suggesting the transmission of volatility shocks to neighbouring countries. Growth rates observed in neighbouring countries has a positive effect on growth rate of a particular country.Du lien entre volatilité et croissance: une méthode d'économétrie spatiale RÉSUMÉ la présente communication examine le lien entre la macro-volatilité et la croissance économique dans l'optique de l'économétrie spatiale. Nous présentons un modèle spatial sans contrainte de Durbin Ramey-Ramey. Nous soumettons le modèle élargi à un groupe de 78 pays pour examiner toutes les dimensions possibles conjointement avec des interactions spatiales pouvant affecter le lien entre la macrovolatilité et la croissance. Contrairement à des recherches précédentes, nous subdivisons les effets de la volatilité sur la croissance entre effets directs et indirects, en adoptant une méthode d'impacts à dérivée partielle. Nous relevons que les effets tant directs qu'indirects de la volatilité sur la croissance sont négatifs, dans le deuxième cas, l'effet pouvant indiquer une transmission de chocs de volatilité à des pays avoisinants. Les taux de croissance observés dans des pays voisins a un effet positif sur le taux de croissance d'un certain pays.Sobre la interrelación entre la volatilidad y el crecimiento: un enfoque econométrico espacial RESUMEN este estudio analiza la interrelación entre la macrovolatilidad y el crecimiento económico bajo una perspectiva econométrica espacial. Presentamos un modelo espacial no limitado de Durbin Ramey-Ramey. Probamos el modelo ampliado en un panel de 78 países para investigar todas las dimensiones posibles junto con las interacciones espaciales que pueden afectar la interrelación entre la macrovolatilidad y el crecimiento. En contraste con los estudios anteriores, dividimos los efectos de la volatilidad sobre el crecimiento en efectos directos e indirectos utilizando el enfoque de los efectos derivados parciales. Hemos encontrado que los efectos directos e indirectos de la volatilidad sobre el crecimiento son negativos y en el caso de los indirectos, se sugiere que la transmisión de la volatilidad afecta a los países vecinos. Las tasas de crecimiento observadas en los países vecinos tienen un efecto sobre la tasa de crecimiento de un país en particular. 波动性与增长的关联：一个空间计量经济学方法摘要：本文从计量经济学的视角审视了宏观波动性与经济增长之间的关联。我们提出了一个无约束空间 Durbin Ramey-Ramey 模型。我们在 78 个国家进行了这项扩展模型试验，以调查所有可能对宏观波动性与增长之间的关联产生影响的空间交互作用维度。与以往的文...

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Section: Introductionmentioning

confidence: 99%

On the Link between Volatility and Growth: A Spatial Econometrics Approach

Abate¹

2015

Spatial Economic Analysis

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“…One interesting application of a spatial econometric model in this field has been presented by Fernandez (2011). She develops a spatial capital asset pricing model (S-CAPM) and shows the implications of spatial autocorrelation in asset return series for risk management by deriving a value at risk from the S-CAPM formulation.…”

Section: Literaturementioning

confidence: 99%

“…Other variants have been proposed (see for example Fernandez, 2011) We do mean to imply that far too much effort has gone into "fine-tuning" spatial weight matrices that depend on highly parameterized functions of distance, lengths of common borders, and so forth. However, due to the number of common elements in these weight matrices and selection of parameters that give the best fit for each W, good fitting models using these different forms of W are not likely to produce estimates and inferences that materially differ.…”

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confidence: 99%

CDS Spreads and Systemic Risk - A Spatial Econometric Approach

Keiler

Eder²

2012

SSRN Journal

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In the current crisis, credit risk not only evolved from a financial institution's fundamentals but also from a system-inherent mechanism of risk propagation. Therefore, the credit event of a single entity spread risk across the whole network of institutions. This contagion effect has been demonstrated dramatically in the ongoing economic and financial crisis.We apply to the field of finance a -to our knowledge -novel way of measuring, quantifying and modelling the degree of systemic risk and the magnitude of risk spill over effects by introducing a specific weighting scheme in a regression that relates observations to each other. We measure contagion effects in CDS levels as well as CDS changes. This approach originally stems from spatial econometrics. The methodology allows for a decomposition of the total risk charge (i.e. the credit spread) into a systemic, systematic and idiosyncratic risk charge. While the systemic component measures the degree of risk spillovers due to the interconnectedness of the financial institutions, the systematic part accounts for the risk stemming from institutions fundamentals and macro-economic fundamentals.The idiosyncratic risk compontent reflects an instition's specfic and diversifiable risk. We apply this methodology to a sample of 15 banks and insurance companies over the period from 2004 to 2009. This research uses equity correlations as a measure of economic distance and CDS spreads as a measure of a financial entity's credit risk. We find considerable risk spill overs due to the interconnectedness of the fifteen systemically important banks and insurance companies. Depending on the state of the economy, up to a fifth of the total predicted CDS spread changes are due to financial infection, highlighting the need for macro-prudential supervision and serving as an alternative explanation for the nonlinear relationship between a debtor's theoretical probability of default and observed credit spreads -known as the "credit spread puzzle". However, we find that this systemic risk charge spreads heterogeneously across geographical regions and financial institutions due to varying integration into the financial system. Nicht-technische Zusammenfassung AbstractThis study applies a novel way of measuring, quantifying and modelling the systemic risk within the financial system. The magnitude of risk spill over effects is gauged by introducing a specific weighting scheme. This approach originally stems from spatial econometrics. The methodology allows for a decomposition of the credit spread into a systemic, systematic and idiosyncratic risk premium. We identify considerable risk spill overs due to the interconnectedness of the financial institutes in the sample. In stress tests, up to one fifth of the CDS spread changes are owing to financial contagion. These results also give an alternative explanation for the nonlinear relationship between a debtor's theoretical probability of default and the observed credit spreads -known as the "credit spread puzzle". This contradicts the hypo...

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“…First, we contribute to the applied spatial econometrics literature. Spatial models are widely used in applied geographic and regional science studies, and have recently also been applied in empirical finance; see Fernandez (2011) for a CAPM model augmented by spatial dependencies, Wied (2013), Arnold et al (2013), and Asghar-ian et al (2013) for analyses of spatial dependencies in stock markets, Denbee et al (2013) for a network approach to assess interbank liquidity, and Saldias (2013) for a spatial error model to identify sector risk determinants. The study closest to ours is Keiler and Eder (2013).…”

Section: Introductionmentioning

confidence: 99%

Spillover dynamics for systemic risk measurement using spatial financial time series models

Blasques

Koopman

Schaumburg

2016

Journal of Econometrics

107

106

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe introduce a new model for time-varying spatial dependence. The model extends the wellknown static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the static parameters is consistent and asymptotically normal. We also study the information theoretic optimality of the updating steps for the time-varying spatial dependence parameter. We adopt the model to empirically investigate the spatial dependence between eight European sovereign CDS spreads over the period [2009][2010][2011][2012][2013][2014], which includes the European sovereign debt crisis. We construct our spatial weight matrix using cross-border lending data and include country-specific and Europe-wide risk factors as controls. We find a high, time-varying degree of spatial spillovers in the sovereign CDS spread data. There is a downturn in spatial dependence after the first half of 2012, which is consistent with policy measures taken by the European Central Bank. The findings are robust to a wide range of alternative model specifications.

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Spatial linkages in international financial markets

Cited by 63 publications

References 12 publications

On the Link between Volatility and Growth: A Spatial Econometrics Approach

On the Link between Volatility and Growth: A Spatial Econometrics Approach

CDS Spreads and Systemic Risk - A Spatial Econometric Approach

Spillover dynamics for systemic risk measurement using spatial financial time series models

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