A wavelet analysis of mean and volatility spillovers between oil and BRICS stock markets

Relationship between Stock and the Sovereign CDS markets: A panel VAR based analysis

“…The contradiction might exists due to the high volatility and underdevelopment of the stock market of china during the analysis period of these authors. Our results partially negate the findings of Chan et al (2009);Boubaker and Raza (2017). These authors document leading role of the SCDS market for five out of seven countries.…”

Relationship between uncertainty in the oil and stock markets before and after the shale gas revolution: Evidence from the OVX, VIX, and VKOSPI volatility indices

“…This is because increased crude oil price volatility can negatively affect economic growth, causing greater economic uncertainty. Empirical test results indicating that crude oil prices and economic activity are very much related are already seen in many studies ([2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12]).…”

Testing mean-reversion in agricultural commodity prices: Evidence from wavelet analysis

“…Our results are in line with that Areal et al, (2016), Fatima and Ahmed (2019). See also (Bahmani-oskooee et al, 2016); (Li et al, 2014); (Jammazi & Reboredo, 2016); (Yang et al, 2017); (Fernández-macho, 2012); (Boubaker & Ali, 2017); (Lawal, Nwanji, Adama & Otekunrin, 2017) The results of the structural breaks show the existence of heterogeneous structural breaks points (Ti). It is appropriate to question whether or not the break points as presented are realistic or coincide with significant changes in the global political and socio-economic space.…”

“…Commodity market is presumed to be more volatile than stock and other financial markets instruments ; (Taiwo & Studies, 2012) ; (El, Arouri, Jouini, & Khuong, 2011); (Panella, Barcellona, & Ecclesia, 2012) as it enjoy less trading frequency than stocks, it is characterized by considerable transaction and opportunity cost leading to higher levels of fluctuation with relative low levels of efficiency. The impact of unsystematic factors like weather conditions, political and economic instability, consumer expectations and business fundamentals influences the multi-scale nonlinear features of the market, thus making prediction using conventional time domain estimation techniques that focuses on linearity, autocorrelation, heteroscedasticity unfit to explain the behaviour of agricultural commodity prices (Fam, Hennani, & Huchet, 2017); (Spencer, Bredin, & Conlon, 2018); (Boubaker & Ali, 2017); (Joëts, Mignon, & Razafindrabe, 2017); (Harvey et al, 2017) (Wang, Wu, & Yang, 2014); (Babajide, Lawal, & Somoye, 2015); (Yang, Ce, & Lian, 2017); (Martín-barragán et al, 2015); (Fatih & Öcal, 2017) . To overcome this challenge, the current study employed a high frequency wavelet based unit root tests techniques.…”

“…It explains that due to the inherent features of commodities, unlike stock prices, commodity prices are heterogonous in nature, more volatile and characterized by high level of fluctuation with some level inefficiency. This implies that shocks to commodity prices are temporary, as prices will definitely return to their trend path, thus investors can forecast future fluctuation of commodity prices using past information (Wang & Mcphail, 2014); (Alzahrani, Masih, & Al-titi, 2014); (Li, Yao, & Duchesne, 2014); (Bai et al, 2016); (Boubaker & Ali, 2017); (Boubaker & Sghaier, 2015) (Wang et al, 2015); ; (Ftiti, Kablan, & Guesmi, 2016).…”