Related Experiment Video
Updated: May 14, 2026
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Cascading failures in bi-partite graphs: model for systemic risk propagation.
Xuqing Huang1, Irena Vodenska, Shlomo Havlin
1Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215, USA.
Scientific Reports
|February 7, 2013
Summary
Financial network interconnectedness can cause cascading failures. A new bi-partite banking model effectively predicts bank failures, identifying commercial real estate as a key risk factor.
Area of Science:
- Financial economics
- Network science
- Computational finance
Background:
- Increasingly interconnected financial entities amplify systemic risk.
- Financial network shocks can trigger widespread cascading failures.
Purpose of the Study:
- To develop and validate a model for assessing systemic risk in financial networks.
- To identify key drivers of bank failures during financial crises.
Main Methods:
- Creation of a bi-partite banking network model (banks and assets).
- Development of a cascading failure model to simulate risk propagation.
- Empirical testing using 2007 US commercial bank balance sheet data.
Main Results:
- The model accurately identified a significant portion of actual bank failures.
- Commercial real estate assets were identified as primary contributors to bank failures between 2008-2011.
- Over 350 US commercial banks failed during this period.
Conclusions:
- The proposed model is a valuable tool for systemic risk stress testing.
- Understanding asset-specific risks, particularly commercial real estate, is crucial for financial stability.
Related Concept Videos
01:10Propagation of Uncertainty from Systematic Error
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
00:59Propagation of Uncertainty from Random Error
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
01:14Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model
The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...
01:14Multicompartment Models: Overview
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
01:24BIBO stability of continuous and discrete -time systems
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
01:19Survival Tree
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a survival tree begins...
Building a Survival Tree
Constructing a survival tree begins...