CDS-based implied probability of default estimation

Abid, Amira; Abid, Fathi; Kaffel, Bilel

doi:10.1108/jrf-05-2019-0079

Cited by 6 publications

(1 citation statement)

References 31 publications

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“…It is worth noting that the Vasicek model-related idea is used in other fields of financial analysis as well, mainly when the interest rate is stochastic (Yao et al 2016;Shevchenko and Luo 2017;Wu et al 2019;Chang and Chang 2017;Orlando 2020), and is also popular in continuous time models (Christensen et al 2016;Abid et al 2020;Xiao and Yu 2019).…”

Section: Introductionmentioning

confidence: 99%

Some properties of the maximum loss on loan portfolios

Vörös

2022

Cent Eur J Oper Res

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Estimating losses on portfolios both theoretically and technically is an interesting and hard issue because the comprehensive formulation of the problem results in a complicated task to solve. Simulation algorithms are among the most popular, currently available procedures used by financial institutions and striving to obtain closed form formulas to see the structure of the maximum loss requires the exploration of additional properties of the maximum loss problem. We prove that within a given Mahalanobis distance, under assumptions, the maximum loss on a loan portfolio is located at the frontier of this distance, and this property allow us to set up closed forms to define efficient initial solutions. The performance of these initial solutions is compared to the optimum solution when we have two variables indicating the state of the macro economy. The supposed advantage of this approach would appear in case of large number of macro variables, but this requires further research and investigation. We found that the initial solutions generated by the compact forms underestimates losses only with insignificant manner compared to the optimum solution for a particular set of maximum loss problems.

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Section: Introductionmentioning

confidence: 99%

Some properties of the maximum loss on loan portfolios

Vörös

2022

Cent Eur J Oper Res

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Climate Finance in the Wake of Covid-19: Connectedness of Clean Energy With Conventional Energy and Regional Stock Markets

et al. 2022

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Focusing on raising climate concerns and sustaining a clean ecosystem, the current study strives to examine the connectedness of clean energy markets with conventional energy markets and four regional stock markets of Asia, Pacific, Europe, and America for the period spanning January 1, 2004 to August 31, 2021. We employed the volatility connectedness methodology using dynamic conditional correlation (DCC-GARCH) estimates for analysis purposes. There is pronounced within class connectedness of all markets except conventional energy markets, which showed strong disconnection from the network. However, strong inter-class spillovers are reported between clean energy and regional stock markets. Time-varying analysis revealed that intense spillovers are shaped during the Global Financial Crisis, Shale Oil Crisis, and COVID-19 pandemic. Meanwhile, time-varying net connectedness estimates illuminate that world renewable energy and American stock markets are net transmitters, whereas leftover markets are net recipients of spillovers. Further analysis of sub-sample periods during GFC, SOR, and COVID-19 validate that intense spillovers are formed when markets experience unexpected financial, economic, and global health turmoil. We proposed significant implications for regional stock markets of Asia, Pacific, Europe and America to concentrate on the climate-friendly energy markets than conventional energy markets as they service the clean ecosystem motives more specifically.

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Sovereign CDS Spread and Term Structure Forecasting Based on Neural Network

Abid,

Suissi

2024

Global Business Review

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The article aims to forecast credit risk for BRICS countries using daily credit default swaps (CDS) spreads obtained from Datastream data base from 2018 to 2023. Our approach consists, first, of forecasting the CDS spread in order to estimate the forecasted CDS term structure. The general regression neural network (GRNN) is used to predict the CDS spread. By checking the accuracy of the prediction, the results show that the GRNN model can be recommended as an effective forecasting tool for CDS spread. Second, the predicted spreads are used to estimate the forecasted CDS term structure using the Nelson–Siegel model. The results show that for Russia, overall, the CDS spreads in the long term are less than those in the short term, which implies that the future outlook is more optimistic, given the events that occurred during the study period, but it still retains the highest level of credit risk compared to other countries. Unlike Brazil, India and South Africa, the future outlook is more pessimistic. For China, the term structure is unstable; in the short term, there is a tendency to reduce the risk, but for longer horizons, the risk will increase. Thus, BRICS countries have different risk profiles depending on investment horizons. The study’s findings help policymakers in developing tailored risk management strategies for BRICS countries and guide investors in making informed credit investment decisions. The use of advanced forecasting tools like GRNN and Nelson–Siegel models emphasizes the importance of sophisticated techniques in enhancing financial market resilience.

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CDS-based implied probability of default estimation

Cited by 6 publications

References 31 publications

Some properties of the maximum loss on loan portfolios

Some properties of the maximum loss on loan portfolios

Climate Finance in the Wake of Covid-19: Connectedness of Clean Energy With Conventional Energy and Regional Stock Markets

Sovereign CDS Spread and Term Structure Forecasting Based on Neural Network

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