Financial stress and the probability of sovereign default

Rho, Caterina; Saenz, Manrique

doi:10.1016/j.jimonfin.2020.102305

Cited by 12 publications

(11 citation statements)

References 44 publications

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“…Using 22 years of credit ratings data, Pesaran et al [19] compared the confidence intervals around estimated PD and found that it is impossible to distinguish notch-level PDs for investment-grade ratings. Rho and Saenz [20] found that GDP has a stronger impact on default risk in periods of financial stress versus in tranquil times. They also discovered that financial stress strengthens the impact of GDP on sovereign PD.…”

Section: Literature Reviewmentioning

confidence: 99%

Goodness-of-Fit of Logistic Regression of the Default Rate on GDP Growth Rate and on CDX Indices

Lin

Ching³

et al. 2021

Mathematics

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Under the Basel II and Basel III agreements, the probability of default (PD) is a key parameter used in calculating expected credit loss (ECL), which is typically defined as: PD × Loss Given Default × Exposure at Default. In practice or in regulatory requirements, gross domestic product (GDP) has been adopted in the PD estimation model. Due to the problem of excessive fluctuation and highly volatile ECL estimation, models that produce satisfactory PD and thus ECL estimations in the context of existing risk management techniques are lacking. In this study, we explore the usage of the credit default swap index (CDX), a market’s expectation of future PD, as a predictor of the default rate (DR). By comparing the goodness-of-fit of logistic regression, several conclusions are drawn. Firstly, in general, GDP has considerable explanatory power for the default rate which is consistent with current models in practice. Secondly, although both GDP and CDX fit the DR well for rating B class, CDX has a significantly better fit of DR for ratings [A, Baa, Ba]. Thirdly, compared with low-rated companies, the relationship between the DR and GDP is relatively weak for rating A. This phenomenon implies that, in addition to using macroeconomic variables and firm-specific explanatory variables in the PD estimation model, high-rated companies exhibit a greater need to use market supplemental information, such as CDX, to capture the changes in the DR.

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Section: Literature Reviewmentioning

confidence: 99%

Goodness-of-Fit of Logistic Regression of the Default Rate on GDP Growth Rate and on CDX Indices

Lin

Ching³

et al. 2021

Mathematics

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“…Связь средняя (умеренная) 0,7-0, 95 Связь сильная (тесная) 0,95-1 Практически функциональная зависимость Общее сокращение численности населения Мурманской обл. отразилось на показателях плотности населения и численности занятых в экономике.…”

Section: индекс корреляцииunclassified

“…2.8). 95 Среди арктических субъектов РФ Ненецкий автономный округ характеризуется наиболее высоким объемом ВРП в расчете на душу населения (6,9 млн руб. ), 75 % которого формируется в результате добычи нефти.…”

Section: таблица 24unclassified

“…Сокращение предприятий в последнее десятилетие можно расценивать как один из признаков снижения предпринимательской активности в регионе.Внешнеторговый оборот Мурманской обл. в течение исследуемого периода увеличивался прежде всего за счет экспорта95 . Экспорт товаров к 2018 г. составил свыше 3 600 млн долл., в его структуре по-прежнему преобладали минеральные удобрения и уголь (свыше 80 %).…”

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Financial solvency of the regions of the Russian Arctic.

Dyadik¹,

Chapargina²

2021

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The monograph is devoted to assessing the financial solvency of the regions of the Russian Arctic by means of correlation analysis aimed at studying the forms of communication between indicators that allow us to identify general and specific features of the financial development of regions. The work selected and analyzed statistical indicators that characterize directly or indirectly the financial solvency of the regions, calculated the correlations of these indicators, taking into account their dynamics over the past fifteen years. A matrix of correlation pairs was created, the most significant of them for assessing the financial solvency of the region were identified. Comparative analysis of paired correlation coefficients for the Arctic regions made it possible to assess the relationship between certain areas of their regional development. A comprehensive analysis of the financial solvency of the Arctic territories is presented, and based on the results obtained, trajectories of regional financial development are developed, considering background conditions. The results of the monograph are intended for a wide range of specialists in the field of regional finance and may be of interest to everyone who is interested in the problems of financial development, as well as to regional and federal authorities for forecasting and strategic planning of the development of the Arctic regions in conditions of limited financial resources.

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“…A study by Chen et al [ 6 ] revealed that during a time of high financial stress or when foreign currency observes appreciation, exporting firms would increase export prices and upgrade quality. Research by Rho and Saenz [ 40 ] demonstrated that financial stress substantially amplified the effect of debt-to-GDP ratio, stock of international reserves, and GDP per capita on the probability of occurrence of sovereign debt. Qin [ 38 ] deployed structural vector autoregression (VAR) to evaluate the impact of structural oil shocks on stress in financial markets and the interaction of different markets.…”

Section: Introductionmentioning

confidence: 99%

Can financial stress be anticipated and explained? Uncovering the hidden pattern using EEMD-LSTM, EEMD-prophet, and XAI methodologies

Ghosh

Pamučar

2022

Complex Intell. Syst.

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Global financial stress is a critical variable that reflects the ongoing state of several key macroeconomic indicators and financial markets. Predictive analytics of financial stress, nevertheless, has seen very little focus in literature as of now. Futuristic movements of stress in markets can be anticipated if the same can be predicted with a satisfactory level of precision. The current research resorts to two granular hybrid predictive frameworks to discover the inherent pattern of financial stress across several critical variables and geography. The predictive structure utilizes the Ensemble Empirical Mode Decomposition (EEMD) for granular time series decomposition. The Long Short-Term Memory Network (LSTM) and Facebook’s Prophet algorithms are invoked on top of the decomposed components to scrupulously investigate the predictability of final stress variables regulated by the Office of Financial Research (OFR). A rigorous feature screening using the Boruta methodology has been utilized too. The findings of predictive exercises reveal that financial stress across assets and continents can be predicted accurately in short and long-run horizons even at the time of steep financial distress during the COVID-19 pandemic. The frameworks appear to be statistically significant at the expense of model interpretation. To resolve the issue, dedicated Explainable Artificial Intelligence (XAI) methods have been used to interpret the same. The immediate past information of financial stress indicators largely explains patterns in the long run, while short-run fluctuations can be tracked by closely monitoring several technical indicators.

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Financial stress and the probability of sovereign default

Cited by 12 publications

References 44 publications

Goodness-of-Fit of Logistic Regression of the Default Rate on GDP Growth Rate and on CDX Indices

Goodness-of-Fit of Logistic Regression of the Default Rate on GDP Growth Rate and on CDX Indices

Financial solvency of the regions of the Russian Arctic.

Can financial stress be anticipated and explained? Uncovering the hidden pattern using EEMD-LSTM, EEMD-prophet, and XAI methodologies

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