Non-linearities in the dynamics of oil prices

Kisswani, Khalid M.; Nusair, Salah A.

doi:10.1016/j.eneco.2012.09.007

Cited by 18 publications

(4 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results are not reported here for the sake of parsimony. (Nonlinearity in real oil price time series, ascertained by means of univariate LSTAR/ESTAR tests is to be found also in Kisswani and Nusair, 2013. ) 14.…”

Section: Robustness Analysis Of the Model Specificationmentioning

confidence: 82%

Navigating the Oil Bubble: A Non-linear Heterogeneous-agent Dynamic Model of Futures Oil Pricing

Cifarelli¹,

Paesani²

2021

The Energy Journal

View full text Add to dashboard Cite

We analyze short-term futures oil pricing over the 2003-2016 time-period in order to analyze the bubble-like dynamics, which characterizes the 2007-2009 years according to a large body of recent literature. Our investigation, based on a flexible three-agent model (hedgers, fundamentalist speculators and chartists), confirms the presence of a bubble price pattern, which we attribute to the strong destabilizing behaviour of fundamentalist speculators (e.g. hedge funds). The inclusion of the 2009-2016 sub-period, in spite of sharp and unexpected fluctuations in oil prices and a significant increase in the influence of geopolitical factors, fails to invalidate our financial interpretation.

show abstract

Section: Robustness Analysis Of the Model Specificationmentioning

confidence: 82%

Navigating the Oil Bubble: A Non-linear Heterogeneous-agent Dynamic Model of Futures Oil Pricing

Cifarelli¹,

Paesani²

2021

The Energy Journal

View full text Add to dashboard Cite

show abstract

“…Moreover, in a nonlinear structure, the reaction of the stock prices to the positive shocks of the economy may differ from that to the negative shocks. In this section, the nonlinear short- and long-run term relationship between cryptocurrencies on gold and on the G7 and the BRICS index has been examined by empirical literature (Kisswani and Nusair, 2013).…”

Section: Methodsmentioning

confidence: 99%

The impact of cryptocurrencies on the gold, WTI, VIX index, G7 and BRICS index before and during COVID-19: a quantile regression and NARDL analysis

Aloui,

Hamdi,

Tiwari

et al. 2023

IJLMA

View full text Add to dashboard Cite

Purpose This study aims to explore the impact of cryptocurrencies (Bitcoin, Ethereum, Monero and Ripple) on the gold, WTI, VIX index, G7 and the BRICS index before and during COVID-19. Design/methodology/approach This research analyzes the impact of cryptocurrencies (Bitcoin, Ethereum, Monero and Ripple) on the gold, WTI, VIX index, G7 and the BRICS index before and during COVID-19, using the quantile regression approach for the 2016–2020 period. In addition, to catch long- and short-run asymmetries of cryptocurrencies on aforementioned dependent variables, an asymmetric nonlinear co-integration (nonlinear autoregressive distributed lag [NARDL]) approach is applied. Findings The result of the quantile regression shows that in a high market, which corresponds to the 90th quantile, the FTSE MIB, CAC40, SSE, BSE 30, and BVSP stock market showed a statistically insignificant negative coefficient, on the Bitcoin price. In a middle and low markets, which correspond to the 0.2, 0.3 and 0.5th quantiles, the BVSP, FTSE MIB, S&P/TSX, SSE and Nikkei stock markets show statistically significant and positive on Bitcoin. Evidence from the NARDL shows a statistically significant positive impact of cryptocurrencies on the gold, WTI, VIX index, G7 and BRICS indices before and during COVID-19 pandemic. Originality/value These results can provide investors with valuable analysis and information and help them make the best decisions and adopt the best strategies. Therefore, future investigations may concentrate and examine the monetary and governmental policies to be adapted to face the COVID-19 pandemic’s dangerous effects on both the society and the economy. For this reason, investors should take this into account when making their asset allocation decisions. Moreover, the portfolio managers, such as index funds, may consider few eligible cryptocurrencies for their inclusion into the portfolio. However, the speculators present in both stock and crypto markets may opt for a spread strategy to improve their portfolio returns.

show abstract

“…Huang et al [31] and Hou [28] presented superior performance of non-parametric GARCH models relative to parametric GARCH models (in-sample and out-of-sample volatility forecasts). Researchers concluded that non-linear dynamical approach is more appropriate for characterizing and predicting crude oil prices than linear approach [32,33]. The parameters of forecasting models for crude oil prices have been estimated by either Least Square Method [12,23,[34][35][36][37][38], Full Information Likelihood Method [16], Kalman Filter [23,24] or under Bayesian Framework [39].…”

Section: Time Series Modelmentioning

confidence: 99%

Artificial intelligence methods for oil price forecasting: a review and evaluation

Sehgal

Pandey

2015

Energy Syst

View full text Add to dashboard Cite

Artificial intelligent methods are being extensively used for oil price forecasting as an alternate approach to conventional techniques. There has been a whole spectrum of artificial intelligent techniques to overcome the difficulties of complexity and irregularity in oil price series. The potential of AI as a design tool for oil price forecasting has been reviewed in this study. The following price forecasting techniques have been covered: (i) artificial neural network, (ii) support vector machine, (iii) wavelet, (iv) genetic algorithm, and (v) hybrid systems. In order to investigate the state of artificial intelligent models for oil price forecasting, thirty five research papers (published during 2001 to 2013) had been reviewed in form of table (for ease of comparison) based on the following parameters: (a) input variables, (b) input variables selection method, (c) data characteristics (d) forecasting accuracy and (e) model architecture. This review reveals procedure of AI methods used in complex oil price related studies. The review further extended above overview into discussions regarding specific shortcomings that are associated with feature selection for designing input vector, and then concluded with future insight on improving the current state-of-the-art technology.

show abstract

Non-linearities in the dynamics of oil prices

Cited by 18 publications

References 63 publications

Navigating the Oil Bubble: A Non-linear Heterogeneous-agent Dynamic Model of Futures Oil Pricing

Navigating the Oil Bubble: A Non-linear Heterogeneous-agent Dynamic Model of Futures Oil Pricing

The impact of cryptocurrencies on the gold, WTI, VIX index, G7 and BRICS index before and during COVID-19: a quantile regression and NARDL analysis

Artificial intelligence methods for oil price forecasting: a review and evaluation

Contact Info

Product

Resources

About