Oil shocks and stock market volatility of the BRICS: A GARCH-MIDAS approach

Salisu, Afees A.; Gupta, Rangan

doi:10.1016/j.gfj.2020.100546

Cited by 77 publications

(36 citation statements)

References 44 publications

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“…The MIDAS method has wide applications for multiple forecasting domains, such as prediction of the financial market [31][32], energy market [33][34], and other macroeconomic issues, e.g., GDP [35][36] The MIDAS method can fully utilize high-frequency data without sustainable loss of sample information to directly reflect the dynamic relationships among variables by polynomial weights [37]. To realize the accurate forecast for steam coal price, and to address the misspecification of the individual MIDAS model [38], this paper constructs the combination-MIDAS regression model with the comprehensive driving factor systems.…”

Section: Methodsmentioning

confidence: 99%

Forecasting China’s Steam Coal Prices Using Dynamic Factors and Mixed-Frequency Data

Wang¹,

Kang²

2021

Pol. J. Environ. Stud.

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This paper investigates the dynamic relationship between steam coal price and its drivers sampling mixed frequencies to improve the prediction of weekly steam coal price. A novel hybrid method, combining the mixed data sampling (MIDAS) model with eXtreme Gradient Boosting (XGBoost) algorithm, is proposed to perform forecast of weekly steam coal prices by applying the latest mixed factors with high frequencies. The empirical evidences indicate that the daily natural gas prices, temperatures, and air quality index (AQI) have better predictive abilities for steam coal prices than the A-share index and crude oil prices. It's shown that the hybrid model has approximately 23.27% and 78.39% accuracy improvement over the combination-MIDAS and other benchmark models, respectively. The empirical results are helpful for the government to effectively capture the fluctuation and uncertainty of steam coal prices from the energy market and environmental conditions to make reasonable strategies in China.

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Section: Methodsmentioning

confidence: 99%

Forecasting China’s Steam Coal Prices Using Dynamic Factors and Mixed-Frequency Data

Wang¹,

Kang²

2021

Pol. J. Environ. Stud.

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“…They find significant volatility spillover between the gold and stock markets, oil and stock markets. Salisu and Gupta (2020) employed the GARCH-MIDAS (Generalized Autoregressive Conditional Heteroskedasticity variant of Mixed Data Sampling) model to investigate volatility transmission from BRICS to oil shocks, and found mixed responses of stock volatility to oil shocks. Meanwhile, Bonga-Bonga (2018) assessed the extent of financial contagion between South Africa and other BRICS countries with VAR-DCC-GARCH, and found evidence of cross-transmission and dependence between South Africa and Brazil.…”

Section: Introductionmentioning

confidence: 99%

“…This empirical study also provides a degree of robustness to capture outliers in the return series. Hence, the resulting information obtained could be used to drive policy recommendations, as well as to improve hedging strategies, portfolio risk management, and portfolio rebalancing strategies (see, for example, Ji et al (2018); Bouri et al (2018); Hernandez (2014) and Salisu and Gupta (2020)).…”

Section: Introductionmentioning

confidence: 99%

Assessing Market Risk in BRICS and Oil Markets: An Application of Markov Switching and Vine Copula

Mwamba¹,

Mwambi²

2021

IJFS

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This paper investigates the dynamic tail dependence risk between BRICS economies and the world energy market, in the context of the COVID-19 financial crisis of 2020, in order to determine optimal investment decisions based on risk metrics. For this purpose, we employ a combination of novel statistical techniques, including Vector Autoregressive (VAR), Markov-switching GJR-GARCH, and vine copula methods. Using a data set consisting of daily stock and world crude oil prices, we find evidence of a structure break in the volatility process, consisting of high and low persistence volatility processes, with a high persistence in the probabilities of transition between lower and higher volatility regimes, as well as the presence of leverage effects. Furthermore, our results based on the C-vine copula confirm the existence of two types of tail dependence: symmetric tail dependence between South Africa and China, South Africa and Russia, and South Africa and India, and asymmetric lower tail dependence between South Africa and Brazil, and South Africa and crude oil. For the purpose of diversification in these markets, we formulate an asset allocation problem using raw returns, MS GARCH returns, and C-vine and R-vine copula-based returns, and optimize it using a Particle Swarm optimization algorithm with a rebalancing strategy. The results demonstrate an inverse relationship between the risk contribution and asset allocation of South Africa and the crude oil market, supporting the existence of a lower tail dependence between them. This suggests that, when South African stocks are in distress, investors tend to shift their holdings in the oil market. Similar results are found between Russia and crude oil, as well as Brazil and crude oil. In the symmetric tail, South African asset allocation is found to have a well-diversified relationship with that of China, Russia, and India, suggesting that these three markets might be good investment destinations when things are not good in South Africa, and vice versa.

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“…Hence, the type of information obtained could be used to improve the hedging strategies, risk management and portfolio rebalancing, (see Ji, Liu, Zhao, and Fan (2018); Bouri, Shahzad, Raza, and Roubaud (2018); Hernandez (2014) and Salisu and Gupta (2020)). Asset allocation for loss-averse investors is tested by minimizing conditional value at risk (CVaR) risk measure.…”

Section: Introductionmentioning

confidence: 99%

Assessing Market Risk in BRICS and Oil Markets: An application of Markov Switching and Vine Copula

Mwamba¹,

Mwambi²

2021

Preprint

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This paper investigates the dynamic tail dependence risk between BRICS economies and world energy market in the context of the COVID-19 financial crisis of 2020, to determine optimal investment decisions based on risk metrics. For this purpose, the study employs a combination of novel statistical techniques ranging from Markov Switching, GARCH and Vine copula. Using a dataset consisting of daily stock and world crude oil prices; we find high probability of transition between lower and higher volatility regimes. Furthermore, our results based on the C-Vine copula confirm the existence of two types of tail dependence: - symmetric tail dependence between South Africa and China; South Africa and Russia; and lower tail dependence between South Africa and India; South Africa and Brazil; South Africa and Oil. For the purpose of diversification in these markets, we formulate an asset allocation problem using C-vine copula-based returns and optimize it using Particle Swarm algorithm with a rebalancing strategy. The results show an inverse relationship between the risk contribution and asset allocation of South Africa and oil market supporting the existence of lower tail dependence between them. This suggests that when South African stocks are in distress, investors tend to shift their holdings in oil market. Similar results are found between China and oil. In the upper tail, South African asset allocation is found to have an inverse relationship with that of Brazil, Russia and India suggesting that these three markets might be good investment destinations when things are not good in South Africa and vice-versa.

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Oil shocks and stock market volatility of the BRICS: A GARCH-MIDAS approach

Cited by 77 publications

References 44 publications

Forecasting China’s Steam Coal Prices Using Dynamic Factors and Mixed-Frequency Data

Forecasting China’s Steam Coal Prices Using Dynamic Factors and Mixed-Frequency Data

Assessing Market Risk in BRICS and Oil Markets: An Application of Markov Switching and Vine Copula

Assessing Market Risk in BRICS and Oil Markets: An application of Markov Switching and Vine Copula

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