A dynamic spillover analysis of crude oil effects on the sovereign credit risk of exporting countries

Pavlova, Ivelina; Boyrie, Maria E. de; Parhizgari, Ali M.

doi:10.1016/j.qref.2018.03.003

Cited by 41 publications

(22 citation statements)

References 50 publications

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“…After oil markets show signs of recovery, KAZ enters into the low oil dependency regime and becomes indifferent to the higher credit risks in RUS, SA and TUR indices, which in 2018 can be mainly articulated due to the looming pressure from "sanctions", "quantitative tightening", and interest rate decisions under an abruptly devalued currency, respectively. Therefore, in addition to the conclusions by Pavlova et al (2018) for a pool of the oil exporting countries, we find that in the more recent cycle of the oil and developing markets, the debt securitization of KAZ is lower than for other considered CDS and may be attractive for the investors interested in realising potential of the recovering oil markets, 8 since KAZ seems to be less sensitive to other volatility sources we consider if it is in the low oil dependency regime.…”

Section: Resultssupporting

confidence: 51%

The relationship between oil and financial markets in emerging economies: The significant role of Kazakhstan as the oil exporting country

Semeyutin

Lau

et al. 2020

Finance Research Letters

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This study evaluated volatility spillovers among oil price, volatility index and a pool of the credit default swaps for emerging market economies. A special role was ascribed to the time-varying interdependencies and connectedness from the perspectives of Kazakhstan, an oil exporting country. The result shows that Kazakhstan may be more resistant to the volatility, which originated from the other emerging countries. However, Kazakhstan is more sensitive to the global "fear index barometer" of volatility index and oil price volatility. The results might be appealing for portfolio diversification strategies because Kazakhstan's credit default swaps are in the low oil dependency regime.

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Section: Resultssupporting

confidence: 51%

The relationship between oil and financial markets in emerging economies: The significant role of Kazakhstan as the oil exporting country

Semeyutin

Lau

et al. 2020

Finance Research Letters

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“…[73] investigate the dependence between OVX and BRICS sovereign CDS spreads from July 2009 to March 2017. [74] investigate the dynamic spillover of the OVX and volatilities on sovereign credit default swap (CDS) spreads of ten oil-exporting countries.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The OVX time series for the two sub-periods are illustrated in Fig 2 with the breakpoint. This sub-period analysis is often carried out in other studies ( [9,27,74,111]).…”

Section: Sub-period Analysismentioning

confidence: 99%

Relationship between uncertainty in the oil and stock markets before and after the shale gas revolution: Evidence from the OVX, VIX, and VKOSPI volatility indices

Choi¹,

Hong²

2020

PLoS ONE

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We investigate the relationship between crude oil prices and stock markets. Unlike prior studies, we use implied volatility indices and evaluate the change in the relationship between the volatility indices through a sub-period analysis. Specifically, we examine the causal relationships among the crude oil, S&P 500 index, and KOSPI 200 index volatilities by using the autoregressive distributed lag (ARDL) bounds and the Toda-Yamamoto Granger causality tests. In addition, a BEKK-GARCH model is employed to enhance the robustness of the causality test results. These experiments indicate that the OVX and VIX show bi-directional causality in the period that includes the shale gas revolution and no causality in the period that does not. Further, the OVX Granger causes the VKOSPI in the former period, but there is no causality between them in the latter period. Finally, we find strong unidirectional causality from the VIX to the VKOSPI in both sub-periods. These results have important implications for the analysis of portfolio risk management and for assisting energy policymakers and traders in making effective decisions and investments, respectively.

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“…rough this subperiod analysis, we investigate the change in the forecasting performance of all methods in both high-variance and relatively low-variance data. is kind of subperiod analysis is common in other studies [48][49][50][51].…”

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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A dynamic spillover analysis of crude oil effects on the sovereign credit risk of exporting countries

Cited by 41 publications

References 50 publications

The relationship between oil and financial markets in emerging economies: The significant role of Kazakhstan as the oil exporting country

The relationship between oil and financial markets in emerging economies: The significant role of Kazakhstan as the oil exporting country

Relationship between uncertainty in the oil and stock markets before and after the shale gas revolution: Evidence from the OVX, VIX, and VKOSPI volatility indices

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

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