Spillover across Eurozone credit market sectors and determinants

Shahzad, Syed Jawad Hussain; Bouri, Elie; Arreola-Hernandez, Jose; Roubaud, David; Bekiros, Stelios

doi:10.1080/00036846.2019.1619014

Cited by 26 publications

(10 citation statements)

References 47 publications

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“…Furthermore, it is likely that there exists the so-called sectoral risk-taking sharing, which is further amplified by the default intensities of corporations. A similar outcome has been described by Hussain Shahzad et al ( 2019 ), showing that short-run spillover among credit market sectors intensifies during global and Eurozone crisis periods.…”

Section: Resultssupporting

confidence: 87%

Systematic and Unsystematic Determinants of Sectoral Risk Default Interconnectedness

Awijen¹,

Zaied

Hunjra

2022

Comput Econ

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Assessing the financial stability of the banking industry, particularly in credit risk management, has become extremely crucial in times of uncertainty. Given that, this paper aims to investigate the determinants of the interconnectedness of sectoral credit risk default for developing countries. To that purpose, we employ a dynamic credit risk model that considers a variety of macroeconomic indicators, bank-specific variables, and household characteristics. Moreover, the SURE model is used to analyze empirical data. We find the connection between macroeconomic, bank-specific, and household characteristics, and sectoral default risk. The outcomes of macroeconomic factors demonstrate that few macroeconomic determinants significantly influence the sector’s default risk. The empirical results of household components reveal that educated households play a substantial role in decreasing sectoral loan defaults interconnectedness and vice versa. While for bank-specific characteristic, we find that greater bank profitability and specialization have substantially reduced loan defaults.

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Section: Resultssupporting

confidence: 87%

Systematic and Unsystematic Determinants of Sectoral Risk Default Interconnectedness

Awijen¹,

Zaied

Hunjra

2022

Comput Econ

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“…Some studies consider the transmission across assets using various methods such as conditional correlation (Dua and Tuteja, 2016;Öztek and Öcal, 2017), Granger-causality (e.g., Billio et al, 2012;Zhang and Broadstock, 2018), copulas (Philippas and Siriopoulos, 2013;Ji et al, 2018), or conditional value-atrisk (Ji et al, 2018). However, following the influential studies of Diebold and Yılmaz (2009, a growing literature highlights the importance of uncovering spillovers of shocks in a predetermined network via the connectedness approach (e.g., Zhang, 2017;Hussain Shahzad et al, 2019). Notably, higher inter-connectedness of the network, which is an indication of higher market risk, conveys both benefits and risks.…”

Section: Literature Reviewmentioning

confidence: 99%

Return connectedness across asset classes around the COVID-19 outbreak

Bouri

Çepni

Gabauer

et al. 2021

International Review of Financial Analysis

Self Cite

444

174

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In this paper, we show evidence of a dramatic change in the structure and time-varying patterns of return connectedness across various assets (gold, crude oil, world equities, currencies, and bonds) around the COVID-19 outbreak. Using the TVP-VAR connectedness approach, the results show that the dynamic total connectedness across the five assets was moderate and quite stable until early 2020. After that, the total connectedness spikes and the structure of the network of connectedness alters, which concurs with the COVID-19 outbreak. The equity and USD indices are the primary transmitters of shocks before the outbreak, whereas the bond index becomes the main transmitters of shocks during the COVID-19 outbreak. However, the USD index is a net receiver of shocks to other assets during the outbreak period. Furthermore, using a recently developed newspaper-based index of uncertainty in financial markets due to infectious diseases to capture the recent impact of Covid-19, we find that connectedness is positively related to this index, and increases at higher levels (conditional quantiles) of connectedness. Overall, our results reflect the speedy disturbing effects of the COVID-19 outbreak, which matters to the formulations of policies seeking to achieve financial stability. The results also indicate a possibility to threaten investors' portfolios and fade the benefits of diversification.

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“…However, single-name CDS spreads are much less liquid than indices [17][18][19]. In several studies, the creditworthiness of individual industries was investigated using CDS sector data [19][20][21][22]. e CDS term structure is important because it integrates the future risk expectations of both markets and companies by offering CDS spreads over time.…”

Section: Introductionmentioning

confidence: 99%

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

2020

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In this study, we analyze the term structure of credit default swaps (CDSs) and predict future term structures using the Nelson–Siegel model, recurrent neural network (RNN), support vector regression (SVR), long short-term memory (LSTM), and group method of data handling (GMDH) using CDS term structure data from 2008 to 2019. Furthermore, we evaluate the change in the forecasting performance of the models through a subperiod analysis. According to the empirical results, we confirm that the Nelson–Siegel model can be used to predict not only the interest rate term structure but also the CDS term structure. Additionally, we demonstrate that machine-learning models, namely, SVR, RNN, LSTM, and GMDH, outperform the model-driven methods (in this case, the Nelson–Siegel model). Among the machine learning approaches, GMDH demonstrates the best performance in forecasting the CDS term structure. According to the subperiod analysis, the performance of all models was inconsistent with the data period. All the models were less predictable in highly volatile data periods than in less volatile periods. This study will enable traders and policymakers to invest efficiently and make policy decisions based on the current and future risk factors of a company or country.

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Spillover across Eurozone credit market sectors and determinants

Cited by 26 publications

References 47 publications

Systematic and Unsystematic Determinants of Sectoral Risk Default Interconnectedness

Systematic and Unsystematic Determinants of Sectoral Risk Default Interconnectedness

Return connectedness across asset classes around the COVID-19 outbreak

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

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