Oil price risk and the Australian stock market

Faff, Robert W.; Brailsford, Tim

doi:10.1016/s1085-7443(99)00005-8

Cited by 301 publications

(211 citation statements)

References 26 publications

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“…Furthermore, the use of monthly data is in accordance with strong financial literature (e.g. see Bloom, 2009;Braun, Nelson, & Sunier, 1995;Chinzara, 2011;Doukas, Hall, & Lang, 2003;Faff & Brailsford, 1999;Hansson & Hordahl, 1998;Khan, Muneer, & Anuar, 2013;Lanne & Luoto, 2008;Manolis, Stelios, & Angelos, 2002;Sadorsky, 2001 andWest &Worthington, 2006). Then, as is practice in the financial literature, the return series will be expressed in logarithmic difference between the two successive prices acquiring the continuous compounding returns (i.e.…”

Section: Data and Descriptive Statisticsmentioning

confidence: 82%

Pricing of risk and volatility dynamics on an emerging stock market: evidence from both aggregate and disaggregate data

Khan

Rehman

Khan

et al. 2016

Economic Research-Ekonomska Istraživanja

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This study analyses risk-return trade-off and behaviour of various volatility dynamics including: volatility, its persistence, mean reversion and speed of mean reversion along with asymmetry and leverage effect on the Pakistani stock market by employing aggregate (aggregate market level) and disaggregate (sectoral level) monthly data for the period from 1998 to 2012. Three generalised autoregressive conditional heteroscedasticity models were applied: GARCH (1,1) for various volatility dynamics; EGARCH for asymmetric and leverage effect and GARCH-M for pricing of risk. The outcomes of the study are as follows: first, the volatility shocks are quite persistent but with varying degrees across the sectors. Both the ARCH effect (short-term effect) and GARCH effect (long-term effect) play their role in generating conditional future stock returns volatility. Further, overall the volatility process is mean reverting; however, the speed of mean reversion varies across the sectors. Secondly, the current study finds little evidence of asymmetry and leverage effect at both aggregate and disaggregates data. Thirdly, the pricing of risk (positive risk premium) is also evident, particularly at the disaggregate data in the Pakistani stock market. Finally, this research study sets the implications for both the policy makers and investors.

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Section: Data and Descriptive Statisticsmentioning

confidence: 82%

Pricing of risk and volatility dynamics on an emerging stock market: evidence from both aggregate and disaggregate data

Khan

Rehman

Khan

et al. 2016

Economic Research-Ekonomska Istraživanja

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“…The fact that annual data are employed for the analysis precludes the more detailed and comprehensive results that could be inferred from data of higher frequencies. Studies analyzing the impact of oil prices on stocks across the market show that stock prices rise when the oil price falls and vice versa (Huang et al, 1996;Sadorsky, 1999;Faff and Brailsford, 1999). Later studies that analyze the prices of stocks in related (oil, gas) industries show a positive link between those stock prices and the price of oil (Sadorsky, 2001;El-Sharif et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Gold, oil, and stocks: Dynamic correlations

Baruník

Kočenda

Vácha

2016

International Review of Economics & Finance

120

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We employ a wavelet approach and conduct a time-frequency analysis of dynamic correlations between pairs of key traded assets (gold, oil, and stocks) covering the period from 1987 to 2012. The analysis is performed on both intra-day and daily data. We show that heterogeneity in correlations across a number of investment horizons between pairs of assets is a dominant feature during times of economic downturn and financial turbulence for all three pairs of the assets under research. Heterogeneity prevails in correlations between gold and stocks. After the 2008 crisis, correlations among all three assets increase and become homogenous: the timing differs for the three pairs but coincides with the structural breaks that are identified in specific correlation dynamics. A strong implication emerges: during the period under research, and from a different-investment-horizons perspective, all three assets could be used in a well-diversified portfolio only during relatively short periods. Terms of use: Documents inJEL-Code: C010, C130, C580, F370, G110, G150.

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“…Specifically, there has been a wide range of previous studies confirming there is not really a negative impact of oil price changes on stock index returns; instead, it depends on different industries. For instance, Faff and Brailsford (1999) through utilizing augmented market model for investigating the relationship between oil prices and Australia industrial stocks returns point out the positive impacts of oil prices on stock index returns of oil, gas, and energy-related industries while displaying the negative effects of oil prices on related industries' stock index returns regarding papermaking, packing, and transportation. In addition, Nguyen and Bhatti (2012) through applying conditional extreme value theory (C-EVT) for examining the relationships between oil prices and China's and Vietnam's stock markets argue that oil prices significantly positively affect Vietnam's stock index returns (i.e., oil price rise will stimulate stock index returns) while causing China's stock index returns to decline significantly.…”

Section: Use Vector Auto Regression (Var) To Analyze Monthly Data Of mentioning

confidence: 99%

“…The more active stock market trade of a country shows the more prosperous on its economy. Due to this fact, numerous previous studies have explored the factors of affecting stock volatility, including stock trading value (Assogbavi et al, 1995;Saatcioglu and Starks, 1998;Chen et al, 2001;Lee and Rui, 2002;Statman et al, 2006;Xu et al, 2006;Rashid, 2007;Chuang et al, 2009;Chen, 2012) oil price (Jones and Kaul, 1996;Maghyereh and Kandari, 2007;Aloui and Jammazi, 2009;Qinbin et al, 2012;Mollick and Assefa, 2013) manufacturing industry production index (Mohanty et al, 2011) economic prosperity (Fan et al, 2003;Basher and Sadorsky, 2006;Driesprong et al, 2008;Tang et al, 2010) investment inclination (Faff and Brailsford, 1999;Hondroyiannis and Papapetrou, 2001;Henriques and Sadorsky, 2008;Mollick and Assefa, 2013) interest rate (Kagraoka and Moussa, 2013) the total value of import and export prices (Chen et al, 2001) exchange rate (Lyonnet and Werner, 2012) money supply (Eichengreen, 2013;Karras, 2013) price variation (Girardin and Moussa, 2011;Naifar and Dohaiman, 2013) unemployment rate (Nguyen and Bhatti, 2012;Schenkelberg and Watzka, 2013).…”

Section: Introductionmentioning

confidence: 99%