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20082023
most citedDGM: A deep learning algorithm for solving partial differential equations

2.3k citations · 2.7k across the 51 of their papers we have counts for

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6 papers · 1 filter

q-fin.RM2018

Network effects in default clustering for large systems

Konstantinos Spiliopoulos, Jia Yang

We consider a large collection of dynamically interacting components defined on a weighted directed graph determining the impact of default of one component to another one. We prov…

q-fin.RM2016

The effect of heterogeneity on flocking behavior and systemic risk

Fei Fang, Yiwei Sun, Konstantinos Spiliopoulos

The goal of this paper is to study organized flocking behavior and systemic risk in heterogeneous mean-field interacting diffusions. We illustrate in a number of case studies the e…

q-fin.RM2014

Systemic Risk and Default Clustering for Large Financial Systems

Konstantinos Spiliopoulos

As it is known in the finance risk and macroeconomics literature, risk-sharing in large portfolios may increase the probability of creation of default clusters and of systemic risk…

q-fin.RM2011

Large Portfolio Asymptotics for Loss From Default

Kay Giesecke, Konstantinos Spiliopoulos, Richard B. Sowers +1

We prove a law of large numbers for the loss from default and use it for approximating the distribution of the loss from default in large, potentially heterogenous portfolios. The…

q-fin.RM2011★ 53 cited

Default clustering in large portfolios: Typical events

Kay Giesecke, Konstantinos Spiliopoulos, Richard B. Sowers

We develop a dynamic point process model of correlated default timing in a portfolio of firms, and analyze typical default profiles in the limit as the size of the pool grows. In o…

q-fin.RM2010★ 14 cited

Recovery Rates in investment-grade pools of credit assets: A large deviations analysis

Konstantinos Spiliopoulos, Richard B. Sowers

We consider the effect of recovery rates on a pool of credit assets. We allow the recovery rate to depend on the defaults in a general way. Using the theory of large deviations, we…