Relationship between oil, stock prices and exchange rates: A vine copula based GARCH method

Aloui, Riadh; Aïssa, Mohamed Safouane Ben

doi:10.1016/j.najef.2016.05.002

Cited by 75 publications

(35 citation statements)

References 38 publications

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“…Moreover, its influence and control power on oil price increases gradually and further strengthens after the global financial crisis in 2008 [62]. Many studies demonstrated a negative relationship between oil prices and the US dollar exchange rates [60][61][62][63][64][65][66]. Possible linkages between the dollar index and oil price are suggested by Chai et al [62].…”

Section: Data Descriptionmentioning

confidence: 98%

“…Other studies also found that movements in US dollar exchange rates influence commodity price changes [57][58][59] and oil prices are no exception. While researchers have focused on various factors that influence international oil prices, the US dollar index is included as a major variable and is confirmed as an important factor for oil price changes [60][61][62][63][64][65][66]. Moreover, its influence and control power on oil price increases gradually and further strengthens after the global financial crisis in 2008 [62].…”

Section: Data Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors

Lee

Huh

2017

Sustainability

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Abstract:In the long-term, crude oil prices may impact the economic stability and sustainability of many countries, especially those depending on oil imports. This study thus suggests an alternative model for accurately forecasting oil prices while reflecting structural changes in the oil market by using a Bayesian approach. The prior information is derived from the recent and expected structure of the oil market, using a subjective approach, and then updated with available market data. The model includes as independent variables factors affecting oil prices, such as world oil demand and supply, the financial situation, upstream costs, and geopolitical events. To test the model's forecasting performance, it is compared with other models, including a linear ordinary least squares model and a neural network model. The proposed model outperforms on the forecasting performance test even though the neural network model shows the best results on a goodness-of-fit test. The results show that the crude oil price is estimated to increase to $169.3/Bbl by 2040.

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Section: Data Descriptionmentioning

confidence: 98%

Section: Data Descriptionmentioning

confidence: 99%

Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors

Lee

Huh

2017

Sustainability

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show abstract

“…An artificial neural network (ANN) is an interconnected group of nodes, akin to the vast network of neurons in a brain 1 . The nodes (neurons) are the processing elements of 1 https://en.wikipedia.org/wiki/Artificial\_neural\_network.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Since the contagion effect of subprime mortgage crisis began in 2007 has caused severe damaging on global economy, considerable attention has been paid to complex transmissions and co-movements between different financial markets. In particular, the correlations of stock market and commodity market in financial crisis is a crucial research area since both market indexes are intrinsically linked with the economy [1]. In the literature, there is robust evidence documenting information transmission between the two markets and which leading to "market fluctuations" [2], which means that the transmission is the key driver of market indexes changes (e.g.…”

Section: Introductionmentioning

confidence: 99%

Predictability of Financial Crisis via Pair Coupling of Commodity Market and Stock Market

Cao¹

2019

JFA

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The complex interactions between stock market and commodity market in financial crisis has been investigated by many researchers, but there is less known about how useful the pair coupling of the two markets for predicting financial crisis, where the pair coupling is the hidden essence of market interactions. This article investigates three kinds of couplings, namely time coupling, frequency coupling and space coupling, which are the different aspects of the pair coupling. In addition, a two-layer model, namely CHMM-ANN, is proposed to investigate the couplings and evaluate the predicting abilities based on the couplings. Coupled Hidden Markov Model (CHMM) is adopted at the bottom level to capture the hidden couplings, and then the couplings are put as input to classical Artificial Neural Network (ANN) at the top level to predict financial crisis. The experiment results on real financial data confirm the advantages of the pair coupling in predicting financial crisis.

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“…By employing the wavelet approach, Martín-Barragán et al [40] found several correlational breakdowns caused by financial and oil shocks, both at lower and higher frequencies. Aloui and Aïssa [41] also provided evidence that the dynamics between stocks and oil prices are not constant over time, and the dependence structure of this was significantly affected by the 2007-2009 financial crisis. Cai et al [42] analyzed the interdependence between oil and East Asian stock returns using wavelet techniques.…”

Section: Introductionmentioning

confidence: 99%

Determinants of the Long-Term Correlation between Crude Oil and Stock Markets

Yang

et al. 2019

Energies

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This study employed a dynamic conditional correlation–mixed-data sampling (DCC–MIDAS) approach and panel data analysis to examine the factors that influence the long-term correlation between crude oil and stock markets. Our study shows that there is a positive long-term conditional correlation between oil prices and stock markets, except during the 2008 global financial crisis and the 2011 European debt crisis. We also found that macroeconomic factors have a significant impact on this correlation. Specifically, risk-free rate has a positive effect, whereas economic activity and credit risk has a negative effect. Our results provide useful information for investors and monetary authorities.

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Relationship between oil, stock prices and exchange rates: A vine copula based GARCH method

Cited by 75 publications

References 38 publications

Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors

Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors

Predictability of Financial Crisis via Pair Coupling of Commodity Market and Stock Market

Determinants of the Long-Term Correlation between Crude Oil and Stock Markets

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