62 research outputs found
Post-Mortem Examination of the International Financial Network
As the recent crisis has forcefully suggested, understanding financial-market interconnectedness is of a paramount importance to explain systemic risk, stability and economic dynamics. In this paper, we address these issues along two related perspectives. First, we explore the statistical properties of the International Financial Network (IFN), defined as the weighted-directed multigraph where nodes are world countries and links represent debtor-creditor relationships in equities and short/long-run debt. We investigate whether the 2008 financial crisis has resulted in a significant change in the topological properties of the IFN. Our findings suggest that the crisis caused not only a reduction in the amount of securities traded, but also induced changes in the topology of the network and in the time evolution of its statistical properties. This has happened, however, without changing the disassortative, core-periphery structure of the IFN architecture. Second, we perform an econometric study to examine the ability of network-based measures to explain cross-country differences in crisis intensity. We investigate whether the conclusion of previous studies showing that international connectedness is not a relevant predictor of crisis intensity may be reversed, once one explicitly accounts for the position of each country within the IFN. We show that higher interconnectedness reduces the severity of the crisis, as it allows adverse shocks to dissipate quicker. However, the systemic risk hypothesis cannot be completely dismissed and being central in the network, if the node is not a member of a rich club, puts the country in an adverse and risky position in times of crises. Finally, we find strong evidence of nonlinear effects, once the high degree of heterogeneity that characterizes the IFN is taken into account.financial networks, crisis, early warning systems
Post-Mortem Examination of the International Financial Network
As the recent crisis has forcefully suggested, understanding financial-market interconnectedness is of a paramount importance to explain systemic risk, stability and economic dynamics. In this paper, we address these issues along two related perspectives. First, we explore the statistical properties of the International Financial Network (IFN), defined as the weighted-directed multigraph where nodes are world countries and links represent debtor-creditor relationships in equities and short/long-run debt. We investigate whether the 2008 financial crisis has resulted in a significant change in the topological properties of the IFN. Our findings suggest that the crisis caused not only a reduction in the amount of securities traded, but also induced changes in the topology of the network and in the time evolution of its statistical properties. This has happened, however, without changing the disassortative, core-periphery structure of the IFN architecture. Second, we perform an econometric study to examine the ability of network-based measures to explain crosscountry differences in crisis intensity. We investigate whether the conclusion of previous studies showing that international connectedness is not a relevant predictor of crisis intensity may be reversed, once one explicitly accounts for the position of each country within the IFN. We show that higher interconnectedness reduces the severity of the crisis, as it allows adverse shocks to dissipate quicker. However, the systemic risk hypothesis cannot be completely dismissed and being central in the network, if the node is not a member of a rich club, puts the country in an adverse and risky position in times of crises. Finally, we find strong evidence of nonlinear effects, once the high degree of heterogeneity that characterizes the IFN is taken into accountfinancial networks, crisis, early warning systems
Disentangled Multi-Fidelity Deep Bayesian Active Learning
To balance quality and cost, various domain areas of science and engineering
run simulations at multiple levels of sophistication. Multi-fidelity active
learning aims to learn a direct mapping from input parameters to simulation
outputs at the highest fidelity by actively acquiring data from multiple
fidelity levels. However, existing approaches based on Gaussian processes are
hardly scalable to high-dimensional data. Deep learning-based methods often
impose a hierarchical structure in hidden representations, which only supports
passing information from low-fidelity to high-fidelity. These approaches can
lead to the undesirable propagation of errors from low-fidelity representations
to high-fidelity ones. We propose a novel framework called Disentangled
Multi-fidelity Deep Bayesian Active Learning (D-MFDAL), that learns the
surrogate models conditioned on the distribution of functions at multiple
fidelities. On benchmark tasks of learning deep surrogates of partial
differential equations including heat equation, Poisson's equation and fluid
simulations, our approach significantly outperforms state-of-the-art in
prediction accuracy and sample efficiency. Our code is available at
https://github.com/Rose-STL-Lab/Multi-Fidelity-Deep-Active-Learning
Accelerating Stochastic Simulation with Interactive Neural Processes
Stochastic simulations such as large-scale, spatiotemporal, age-structured
epidemic models are computationally expensive at fine-grained resolution. We
propose Interactive Neural Process (INP), a Bayesian active learning framework
to proactively learn a deep learning surrogate model and accelerate simulation.
Our framework is based on the novel integration of neural process, deep
sequence model and active learning. In particular, we develop a novel
spatiotemporal neural process model to mimic the simulator dynamics. Our model
automatically infers the latent process which describes the intrinsic
uncertainty of the simulator. This also gives rise to a new acquisition
function based on the latent information gain. We design Bayesian active
learning algorithms to iteratively query the simulator, gather more data, and
continuously improve the model. We perform theoretical analysis and demonstrate
that our approach reduces sample complexity compared with random sampling in
high dimension. Empirically, we demonstrate our framework can faithfully
imitate the behavior of a complex infectious disease simulator with a small
number of examples, enabling rapid simulation and scenario exploration
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Estimating the effect of social inequalities on the mitigation of COVID-19 across communities in Santiago de Chile
We study the spatio-temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data from 1.4 million users, 22% of the whole population in the area, characterizing the effects of non-pharmaceutical interventions (NPIs) on the epidemic dynamics. We integrate these data into a mechanistic epidemic model calibrated on surveillance data. As of August 1, 2020, we estimate a detection rate of 102 cases per 1000 infections (90% CI: [95–112 per 1000]). We show that the introduction of a full lockdown on May 15, 2020, while causing a modest additional decrease in mobility and contacts with respect to previous NPIs, was decisive in bringing the epidemic under control, highlighting the importance of a timely governmental response to COVID-19 outbreaks. We find that the impact of NPIs on individuals’ mobility correlates with the Human Development Index of comunas in the city. Indeed, more developed and wealthier areas became more isolated after government interventions and experienced a significantly lower burden of the pandemic. The heterogeneity of COVID-19 impact raises important issues in the implementation of NPIs and highlights the challenges that communities affected by systemic health and social inequalities face adapting their behaviors during an epidemic
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