The effects of crude oil shocks on stock market shifts behaviour: A regime switching approach

Aloui, Chaker; Jammazi, Rania

doi:10.1016/j.eneco.2009.03.009

Cited by 208 publications

(96 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Existing studies present different results; some find that there is no significant effect of oil price shocks on stock prices [Cong et al 2008;Hammoudeh, Choi 2006;Mohanty, Nandha, Bota 2010;Maghyereh 2004;Sari, Soytas 2006], others find a significant positive impact of oil prices on stock prices [Narayan, Narayan 2010;Nguyen, Bhatti 2012;Broadstock, Cao, Zhang 2012;Mohanty et al 2011], while still others find a significant negative effect [Aloui, Jammazi 2009;Park, Ratti 2008;Cifarelli, Paladino 2010]. Hence, the evidence from the existing literature on the significance and sign of the impact of oil price changes on stock prices is still inconclusive.…”

Section: Causality In Distribution Between European Stock Markets Andmentioning

confidence: 99%

Causality in distribution between European stock markets and commodity prices: using independence test based on the empirical copula

Wanat¹,

Papież²,

Śmiech³

2015

Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu

View full text Add to dashboard Cite

Section: Causality In Distribution Between European Stock Markets Andmentioning

confidence: 99%

Causality in distribution between European stock markets and commodity prices: using independence test based on the empirical copula

Wanat¹,

Papież²,

Śmiech³

2015

Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu

View full text Add to dashboard Cite

“…Billio and Pelizzon (2000) found that MRS-GARCH model is superior to GARCH-t models in estimating volatility. Aloui and Jammazi (2009) show that MRS-GARCH can more precisely estimate the volatility in energy markets by substantially capturing volatility clustering and the leverage effect. Giot and Laurent (2003), in their out-of-sample investigation on crude oil, heating oil, propane and conventional gasoline regular show that the skew student APARCH models provides the best estimates of volatility.…”

Section: Literature Reviewmentioning

confidence: 98%

Best Fitting Fat Tail Distribution for the Volatilities of Energy Futures: Gev, Gat and Stable Distributions in GARCH and APARCH Models

Günay

Khaki

2018

JRFM

View full text Add to dashboard Cite

Precise modeling and forecasting of the volatility of energy futures is vital to structuring trading strategies in spot markets for risk managers. Capturing conditional distribution, fat tails and price spikes properly is crucial to the correct measurement of risk. This paper is an attempt to model volatility of energy futures under different distributions. In empirical analysis, we estimate the volatility of Natural Gas Futures, Brent Oil Futures and Heating Oil Futures through GARCH and APARCH models under gev, gat and alpha-stable distributions. We also applied various VaR analyses, Gaussian, Historical and Modified (Cornish-Fisher) VaR, for each variable. Results suggest that the APARCH model largely outperforms the GARCH model, and gat distribution performs better in modeling fat tails in returns. Our results also indicate that the correct volatility level, in gat distribution, is higher than those suggested under normal distribution with rates of 56%, 45% and 67% for Natural Gas Futures, Brent Oil Futures and Heating Oil Futures, respectively. Implemented VaR analyses also support this conclusion. Additionally, VaR test results demonstrate that energy futures display riskier behavior than S&P 500 returns. Our findings suggest that for optimum risk management and trading strategies, risk managers should consider alternative distributions in their models. According to our results, prices in energy markets are wilder than the perception of normal distribution. In this regard, regulators and policy makers should enhance transparency and competitiveness in the energy markets to protect consumers.

show abstract

“…In general, investors are more pessimistic to bad news when the stock market stays under a worse performance. Reference [22] show that rising oil price will significantly cause the volatility of stock returns and the probability of transition across different time.…”

Section: B Literature Reviewmentioning

confidence: 99%

Recursive Cointegration of Energy and Stock Prices in Indonesia

Setyowati¹

2016

IJTEF

View full text Add to dashboard Cite

show abstract

The effects of crude oil shocks on stock market shifts behaviour: A regime switching approach

Cited by 208 publications

References 53 publications

Causality in distribution between European stock markets and commodity prices: using independence test based on the empirical copula

Causality in distribution between European stock markets and commodity prices: using independence test based on the empirical copula

Best Fitting Fat Tail Distribution for the Volatilities of Energy Futures: Gev, Gat and Stable Distributions in GARCH and APARCH Models

Recursive Cointegration of Energy and Stock Prices in Indonesia

Contact Info

Product

Resources

About