A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The study assesses how financial networks resist simultaneous price shocks and calculates the worst-case loss.
The aim of this paper is to quantify and manage systemic risk caused by default contagion in the interbank market. We model the market as a random directed network, where the vertices represent financial institutions and the weighted edges monetary exposures between them. Our model captures the strong degree of heterog…
One of the most defining features of the global financial network is its inherent complex and intertwined structure. From the perspective of systemic risk it is important to understand the influence of this network structure on default contagion. Using sparse random graphs to model the financial network, asymptotic met…
We study insolvency cascades in an interbank system when banks are allowed to insure their loans with credit default swaps (CDS) sold by other banks. We show that, by properly shifting financial exposures from one institution to another, a CDS market can be designed to rewire the network of interbank exposures in a way…
Regulator allocates buffers to prevent financial contagion in networks with common assets.
Propagation of balance-sheet or cash-flow insolvency across financial institutions may be modeled as a cascade process on a network representing their mutual exposures. We derive rigorous asymptotic results for the magnitude of contagion in a large financial network and give an analytical expression for the asymptotic …
Study applies Gai-Kapadia framework to global equity markets to assess systemic risk and default cascades.
The study shows that limited liability can make banks more stable by choosing less risky assets.
In the last years, increasing efforts have been put into the development of effective stress tests to quantify the resilience of financial institutions. Here we propose a stress test methodology for central counterparties based on a network characterization of clearing members, whose links correspond to direct credits …
Study optimizes interbank lending and borrowing to reduce systemic risk.
We propose a new model of the liquidity driven banking system focusing on overnight interbank loans. This significant branch of the interbank market is commonly neglected in the banking system modeling and systemic risk analysis. We construct a model where banks are allowed to use both the interbank and the securities …
Model shows how banks' hidden-to-maturity accounting can mask run risk and lead to financial instability.
Paper introduces a new index to measure financial and workplace resilience of firms.
We provide a methodology, resilient feature engineering, for creating adversarially resilient classifiers. According to existing work, adversarial attacks identify weakly correlated or non-predictive features learned by the classifier during training and design the adversarial noise to utilize these features. Therefore…
Measures financial resilience using BSDEs and their properties.
We consider a model of financial contagion in a bipartite network of assets and banks recently introduced in the literature, and we study the effect of power law distributions of degree and balance-sheet size on the stability of the system. Relative to the benchmark case of banks with homogeneous degrees and balance-sh…
Python tool assesses European agricultural production resilience.
This paper measures financial market resilience in China and identifies key uncertainties.
The resilience of low-degree Rademacher chaos is studied, providing probabilistic lower bounds.
New model shows negative resilience can improve trading efficiency.
Proposes resilience metrics for large blackout costs with logarithmic resilience.
Stocks of more resilient firms outperformed during the pandemic, reflecting disaster risk.
Financial markets can be seen as complex systems that are constantly evolving and sensitive to external disturbance, such as systemic risks and economic instabilities. Analysis of resilient market performance, therefore, becomes useful for investors. From a systems perspective, this paper proposes a novel function-base…
Study extends Gai-Kapadia framework to assess systemic risk in global equity markets.
ResiliNet improves distributed neural network inference resilience.
New method quantifies resilience of electric distribution systems from historical data.
New framework for resilient bi-criteria optimization under noisy feedback.
Paper tackles Byzantine resilience in distributed multi-task learning.
We provide analytical pricing formula of corporate defaultable bond with both expected and unexpected default in the case with stochastic default intensity. In the case with constant short rate and exogenous default recovery using PDE method, we gave some pricing formula of the defaultable bond under the conditions tha…
This paper uses robust optimization to analyze supply chain resilience.
Improved deep neural network generalization through noise resilience.
CyBeR-0 optimizes federated learning with Byzantine resilience and reduced communication costs.
Firms' collaboration networks can decline but remain resilient.
This paper investigates the resilience and robustness of Deep Reinforcement Learning (DRL) policies to adversarial perturbations in the state space. We first present an approach for the disentanglement of vulnerabilities caused by representation learning of DRL agents from those that stem from the sensitivity of the DR…
We present a large-scale study of commonality in liquidity and resilience across assets in an ultra high-frequency (millisecond-timestamped) Limit Order Book (LOB) dataset from a pan-European electronic equity trading facility. We first show that extant work in quantifying liquidity commonality through the degree of ex…
Study reveals clusters of resilient and vulnerable Spanish agri-food firms post-Ukraine-Russia war.
New framework boosts neural network performance and resilience.
We show that wealth processes in the block-shaped order book model of Obizhaeva/Wang converge to their counterparts in the reduced-form model proposed by Almgren/Chriss, as the resilience of the order book tends to infinity. As an application of this limit theorem, we explain how to reduce portfolio choice in highly-re…
Outlier detection is a fundamental task in data mining and has many applications including detecting errors in databases. While there has been extensive prior work on methods for outlier detection, modern datasets often have sizes that are beyond the ability of commonly used methods to process the data within a reasona…
This paper defines resilience in knowledge graph embeddings and surveys existing works.
Temporal coarse-graining of latent default paths explains effective correlation in corporate defaults.
We compare observed corporate cumulative default probabilities to those calculated using a stochastic model based on an extension of the work of Black and Cox and find that corporations default as if via diffusive dynamics. The model, based on a contingent-claims analysis of corporate capital structure, is easily calib…
This paper generalizes Moody's correlated binomial default distribution for homogeneous (exchangeable) credit portfolio, which is introduced by Witt, to the case of inhomogeneous portfolios. As inhomogeneous portfolios, we consider two cases. In the first case, we treat a portfolio whose assets have uniform default cor…
We propose a novel credit default model that takes into account the impact of macroeconomic information and contagion effect on the defaults of obligors. We use a set-valued Markov chain to model the default process, which is the set of all defaulted obligors in the group. We obtain analytic characterizations for the d…
Study finds ESG investments more resilient than traditional equity indices during market turmoil.
TensorFI injects faults in TensorFlow programs to assess their reliability.
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 our model, a firm defaults at a stochastic intensity that is influenced by an idiosyncratic risk process, a systematic risk process common to…
Ranger improves DNNs' fault resilience without re-computation.