arXiv subjects

Zlata Tabachová

Publications and source records attributed to Zlata Tabachová.

4 recordsLinked to original sources

arXiv2026

On the Practical Estimation and Interpretation of Rényi Transfer Entropy

Rényi transfer entropy (RTE) is a generalization of classical transfer entropy that replaces Shannon's entropy with Rényi's information measure. This, in turn, introduces a new tunable parameter $α$, which accounts for sensitivity to low- or high-probability events. Although RTE shows strong potential for analyzing causal relations in complex, non-Gaussian systems, its practical use is limited, primarily due to challenges related to its accurate estimation and interpretation. These difficulties are especially pronounced when working with finite, high-dimensional, or heterogeneous datasets. In this paper, we systematically study the performance of a k-nearest neighbor estimator for both Rényi entropy (RE) and RTE using various synthetic data sets with clear cause-and-effect relationships inherent to their construction. We test the estimator across a broad range of parameters, including sample size, dimensionality, memory length, and Rényi order $α$. In particular, we apply the estimator to a set of simulated processes with increasing structural complexity, ranging from linear dynamics to nonlinear systems with multi-source couplings. To address interpretational challenges arising from potentially negative RE and RTE values, we introduce three reliability conditions and formulate practical guidelines for tuning the estimator parameters. We show that when the reliability conditions are met and the parameters are calibrated accordingly, the resulting effective RTE estimates accurately capture directional information flow across a broad range of scenarios. Results obtained show that the explanatory power of RTE depends sensitively on the choice of the Rényi parameter $α$. This highlights the usefulness of the RTE framework for identifying the drivers of extreme behavior in complex systems.

nlin.PS↗

arXiv2025

Combined climate stress testing of supply-chain networks and the financial system with nation-wide firm-level emission estimates

On the way towards carbon neutrality, climate stress testing provides estimates for the physical and transition risks that climate change poses to the economy and the financial system. Missing firm-level CO2 emissions data severely impedes the assessment of transition risks originating from carbon pricing. Based on the individual emissions of all Hungarian firms (410,523), as estimated from their fossil fuel purchases, we conduct a stress test of both actual and hypothetical carbon pricing policies. Using a simple 1:1 economic ABM and introducing the new carbon-to-profit ratio, we identify firms that become unprofitable and default, and estimate the respective loan write-offs. We find that 45% of all companies are directly exposed to carbon pricing. At a price of 45 EUR/t, direct economic losses of 1.3% of total sales and bank equity losses of 1.2% are expected. Secondary default cascades in supply chain networks could increase these losses by 300% to 4000%, depending on firms' ability to substitute essential inputs. To reduce transition risks, firms should reduce their dependence on essential inputs from supply chains with high CO2 exposure. We discuss the implications of different policy implementations on these transition risks.

q-fin.GN↗

arXiv2023

Estimating the impact of supply chain network contagion on financial stability

Realistic credit risk assessment, the estimation of losses from counterparty's failure, is central for the financial stability. Credit risk models focus on the financial conditions of borrowers and only marginally consider other risks from the real economy, supply chains in particular. Recent pandemics, geopolitical instabilities, and natural disasters demonstrated that supply chain shocks do contribute to large financial losses. Based on a unique nation-wide micro-dataset, containing practically all supply chain relations of all Hungarian firms, together with their bank loans, we estimate how firm-failures affect the supply chain network, leading to potentially additional firm defaults and additional financial losses. Within a multi-layer network framework we define a financial systemic risk index (FSRI) for every firm, quantifying these expected financial losses caused by its own- and all the secondary defaulting loans caused by supply chain network (SCN) shock propagation. We find a small fraction of firms carrying substantial financial systemic risk, affecting up to 16% of the banking system's overall equity. These losses are predominantly caused by SCN contagion. For every bank we calculate the expected loss (EL), value at risk (VaR) and expected shortfall (ES), with and without accounting for SCN contagion. We find that SCN contagion amplifies the EL, VaR, and ES by a factor of 4.3, 4.5, and 3.2, respectively. These findings indicate that for a more complete picture of financial stability and realistic credit risk assessment, SCN contagion needs to be considered. This newly quantified contagion channel is of potential relevance for regulators' future systemic risk assessments.

q-fin.ST↗

arXiv2022

Causal inference in time series in terms of Rényi transfer entropy

Uncovering causal interdependencies from observational data is one of the great challenges of nonlinear time series analysis. In this paper, we discuss this topic with the help of information-theoretic concept known as Rényi information measure. In particular, we tackle the directional information flow between bivariate time series in terms of Rényi transfer entropy. We show that by choosing Rényi $α$ parameter appropriately we can control information that is transferred only between selected parts of underlying distributions. This, in turn, provides particularly potent tool for quantifying causal interdependencies in time series, where the knowledge of "black swan" events such as spikes or sudden jumps are of a key importance. In this connection, we first prove that for Gaussian variables, Granger causality and Rényi transfer entropy are entirely equivalent. Moreover, we also partially extend this results to heavy-tailed $α$-Gaussian variables. These results allow to establish connection between autoregressive and Rényi entropy based information-theoretic approaches to data-driven causal inference. To aid our intuition we employ Leonenko et al. entropy estimator and analyze Rényi information flow between bivariate time series generated from two unidirectionally coupled Rössler systems. Notably, we find that Rényi transfer entropy not only allowed us to detect a threshold of synchronization but it also provided a non-trivial insight into the structure of a transient regime that exists between region of chaotic correlations and synchronization threshold. In addition, from Rényi transfer entropy we could reliably infer the direction of coupling - and hence causality, only for coupling strengths smaller that the onset value of transient regime, i.e. when two Rössler systems were coupled, but have not yet entered a synchronization.

cs.IT↗