Is the international propagation of financial shocks non0linear

The recent Great Recession has been particularly remarkable not only for its unprecedented severity, but also for the exceptional degree of global interdependence in financial and real variables.

Yet, existing evidence on the role of exposure is mixed at best. This paper argues that under financial integration, the fact that leveraged investors face the same returns across internationally traded assets, would tend to equalize their borrowing cost across countries. Model simulations show that an unexpected increase in credit spreads in one country generates a similar increase in credit spreads in other financially integrated countries bringing about a global contraction, quite independently of the exposure to foreign assets in the balance sheet of leveraged investors.

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Don't already have an Oxford Academic account? Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account. Sign In. Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume Financial frictions, financial integration and the international propagation of shocks Luca DedolaLuca Dedola.

Oxford Academic. Google Scholar. Giovanni Lombardo. Select Format Select format. Permissions Icon Permissions. Abstract The recent Great Recession has been particularly remarkable not only for its unprecedented severity, but also for the exceptional degree of global interdependence in financial and real variables. Issue Section:. You do not currently have access to this article. Download all slides. Sign in.

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You could not be signed in.As the access to this document is restricted, you may want to search for a different version of it. Uhlig, Harald, Results from an agnostic identification procedure ," Journal of Monetary EconomicsElsevier, vol. Uhlig, H. Discussion Papers. Roberto Rigobon, Hausman, Jerry, Hausman, Jerry A, Hausman, Hausman, Jerry A.

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is the international propagation of financial shocks non0linear

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Is the international propagation of financial shocks non-linear? Giavazzi, Francesco. Registered: Carlo Favero Francesco Giavazzi. Favero, Carlo A. Handle: RePEc:eee:inecon:vyip as.

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is the international propagation of financial shocks non0linear

Help us Corrections Found an error or omission? RePEc uses bibliographic data supplied by the respective publishers.Javier Bianchi, Bianchi, Javier, Jeffrey A. Rose, Discussion Papers. Markus K. Brunnermeier, Longstaff, Francis A. Mehl, A global perspective ," European Economic ReviewElsevier, vol. Candelon, B.

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Eswar S. Prasad, Bagliano, Fabio C. Fabio C. Prato", University of Torino. Gert Peersman, Peersman, Ben S. Muller, Juan F.

Goldberg, Jean Imbs, Imbs, Jean, Nicholas Bloom, Michael B. Devereux, Lane, Philip R. Philip R. Fratzscher, Marcel, Hendry, David F.

Eric van Wincoop, The international financial crisis has caused broad impact and serious consequences to international economic order and the economic development of every country. Therefore, making modeling research on the effect of crisis contagion between some economic markets in order to take timely measures to prevent the further spread of contagion is of great significance for maintaining a countrys economic security, and the stability of global economic and financial system.

To prevent economy from being destroyed by financial crisis contagion, this paper puts forward a new testing approach on the contagion effect of financial crisis. This approach is to test the contagion effect of financial crisis by examining whether the conditional variances of different countries financial markets in crisis period are correlated through Generalized Autoregressive Conditional Heteroskedasticity model.

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Empirical study shows that, this new approach is effective and practical in testing the contagion effect of financial crisis.

Request Permissions. Measuring financial contagion: a copula approach. Journal of Empirical Finance. Modeling asymmetric exchange rate dependence. International Economic Review. Contagion test based on guidance and interaction [J].

Contagion of currency crisis: theories and models [J]. Journal of Finance. Journal of International Economic. Evidence from the ERM[J].

Distress Propagation in Complex Networks: The Case of Non-Linear DebtRank

Journal of International Economics. Review of Financial Studies. Analysis of economic forecasting methods and their application [J]. Financial Contagion. Journal of Political Economy. All Rights Reserved. Registration Log In. Paper Titles. Article Preview. Abstract: The international financial crisis has caused broad impact and serious consequences to international economic order and the economic development of every country. Add to Cart. Advanced Materials Research Volume Edited by:.

Dehuai Zeng. Cite this paper. Online since:. August DOI: Cited by.We consider a dynamical model of distress propagation on complex networks, which we apply to the study of financial contagion in networks of banks connected to each other by direct exposures. The model that we consider is an extension of the DebtRank algorithm, recently introduced in the literature.

The mechanics of distress propagation is very simple: When a bank suffers a loss, distress propagates to its creditors, who in turn suffer losses, and so on.

The original DebtRank assumes that losses are propagated linearly between connected banks. Here we relax this assumption and introduce a one-parameter family of non-linear propagation functions. As a case study, we apply this algorithm to a data-set of European banks, and we study how the stability of the system depends on the non-linearity parameter under different stress-test scenarios.

We find that the system is characterized by a transition between a regime where small shocks can be amplified and a regime where shocks do not propagate, and that the overall stability of the system increases between and Complex networks [ 1 — 3 ] have proved useful to describe systems characterised by pair-wise interactions.

Properties of dynamical processes on networks can be strongly affected by the underlying topology [ 4 ]. Examples include spread of news [ 5 ], rumours [ 6 ], diseases [ 7 ], financial distress [ 8 ], random walkers travelling the graph [ 9 — 11 ], and avalanches [ 1213 ]. In these cases stylized models, despite their apparent simplicity, can give meaningful indications on the large scale dynamics of the system [ 7 ], also helping to shed light on the importance of the network topology [ 14 ].

Credit constraints and the international propagation of US financial shocks

For example, models of epidemic contagion such as SIS or SIR [ 15 ] display dramatically different behaviors depending weather they take place on regular lattices or on complex networks. Similarly, also the spread of distress [ 16 — 19 ] in financial networks is deeply dependent on the pattern of connections among financial institutions.

is the international propagation of financial shocks non0linear

In particular, it is not clear yet if a single topology can be considered robust with respect to different types of shocks [ 20 ] or not [ 2122 ]. Financial institutions are strongly interconnected in a variety of ways e. Here we focus on a single layer of interconnectedness, namely that associated with interbank loans. To cope with fluctuations of liquidity, banks constantly lend money to each other, at different maturities.

Hence, lenders are subject to counterparty riski. This, in turn, could lead to the default of lenders, resulting in a further wave of distress. A bank with a negative equity would not be able to pay back its debtors, even assuming that it could sell all of its assets.

Therefore, usually a negative equity is considered a good proxy for the default of a bank. An interbank loan extended by bank i to bank j is an asset for bank i and a liability for bank j.The range of successive instances to build the models of the deepnet.

The final deepnet returned by the search is a compromise between the top n networks found in the search. Example: true seed optional String A string to be hashed to generate deterministic samples.

Dataset sampling doesn't apply to evaluations for time series. BigML has learned some general rules about what makes one network structure better than another for a given dataset.

Given your dataset, BigML will automatically suggest a structure and a set of parameter values that are likely to perform well for your dataset.

This option only builds one network. Example: true tags optional Array of Strings A list of strings that help classify and retrieve the deepnet. The theory is that these engineered features will linearize obvious non-linear dependencies before training begins, and so make learning proceed more quickly.

Example: "000005" Depending on the descent algorithm chosen and the topology of the network, certain other parameters may apply. You can also use curl to customize a new deepnet from the command line.

For example, to create a new deepnet named "my deepnet" using descent algorithm "adam". Once a deepnet has been successfully created it will have the following properties. Creating a deepnet is a process that can take just a few seconds or a few days depending on the size of the dataset used as input and on the workload of BigML's systems.

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Empirical Research on the Contagion Effect of Financial Crisis

By default, only the 20 most recent deepnets will be returned. You can get your list of deepnets directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your deepnets. When you create a new prediction using a model, BigML.

If you create a new prediction using an ensemble using the bagging or random decision forests technique, the same process is repeated for each model in the ensemble. Then all the predictions from the individual models in the ensemble are combined to return a final prediction using one of the strategies described below. If the ensemble is using the gradient tree boosting technique, the prediction result will be additive meaning each tree modifies the predictions of the previously grown tree.

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It is possible to create a prediction using the filtered decision tree model by specifying filter parameters in the query string of the request parameters. Two useful parameters are support and value, as described in the Filtering a Model section. Once a prediction has been successfully created it will have the following properties. This is the date and time in which the prediction was created with microsecond precision.

is the international propagation of financial shocks non0linear

Each entry includes the column number in original source, the name of the field, the type of the field, and the specific datatype. Not available for ensembles with boosted trees. Even if this an array the current version of BigML.We hope you found this message to be useful. Please note that product prices and availability are limited time offers and are subject to change.

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