Modeling volatility and correlations between emerging market stock prices and the prices of copper, oil and wheat
By Perry Sadorsky. 2014
321 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/0fd7bb88eca471237ab105bcb2483083ee7fc805
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- George Filis
- Stavros Degiannakis *
- Ioannis Chatziantoniou
- Nikolaos Antonakakis
- Sang Hoon Kang
- Rustam Boldanov
- Walid Mensi
- Aktham Issa Maghyereh
In Finance
- George Filis
- Stavros Degiannakis *
- Rustam Boldanov
- Sang Hoon Kang
- Ioannis Chatziantoniou
- Walid Mensi
- K. Al-Yahyaee
- Seong‐Min Yoon
Citations in the corpus
- Volatility spillovers and cross-hedging between gold, oil and equities: Evidence from the Gulf Cooperation Council countries (2017, unknown): “…as follows: The conditional volatility estimates are then used to compute optimal hedge ratios , which can be considered as the proportion of risk of asset 𝑖 that can be hedged away by taking a short position in instrument (asset) 𝑗 , while minimising the variance of the formed portfolio.”
- Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets (2017, methodology): “1 Many studies have researched the oil–stock nexus (e.g. Hammoudeh et al., 2004; Filis et al., 2011; Arouri et al., 2012; Sadorsky, 2012, 2014; Mensi et al., 2014; Salisu and Oloko, 2015) and the gold–stock nexus (e.g. Baur and McDermott, 2010; Hood and Malik, 2013; Arouri et al., 2015).”
- Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH ☆ (2015, unknown): “They find that the optimal hedge ratio for Ghana varies between 0.51 and 0.40 while the optimal hedge ratio for Nigeria ranges between 0.56 and 0.50 Sadorsky (2014a) uses multivariate GARCH models to study the volatility dynamics between emerging market stock prices, oil prices, copper prices, and…”
- Forecasting oil price realized volatility using information channels from other asset classes (2017, unknown): “6 See, inter alia, Aloui and Jammazi (2009), Sari et al. (2010), Arouri et al. (2011), Aloui et al. (2013), Souček and Todorova (2013, 2014), Mensi et al. (2014), Antonakakis et al. (2014), Sadorsky (2014), Phan et al. (2015), IEA (2015), Ferraro et al. (2015).”
- Time-varying correlation between oil and stock market volatilities: Evidence from oil-importing and oil-exporting countries (2016, background): “…such as Byuksahin et al. (2010), Silvennoinen and Thorp (2010), Tang and Xiong (2010), Byuksahin and Robe (2011), Hamilton and Wu (2012) and Sadorsky (2014), among others, that the speculation and the financialisation of the oil market resulted in an increased correlation between the oil…”
- Oil shocks and stock markets: dynamic connectedness under the prism of recent geopolitical and economic unrest (2017, background): “Similar findings are reported by Sadorsky (2014) for various emerging stock markets.”
- Energy, precious metals, and GCC stock markets: Is there any risk spillover? (2019, methodology): “Using VARMA-AGARCH and DCC-AGARCH models, Sadorsky (2014) examines the volatility spillovers between emerging stock markets and various other commodities.”
- The co-movement between oil and clean energy stocks: A wavelet-based analysis of horizon associations (2019, result): “28 This model specification is in line with most of the previous studies (Sadorsky, 2012, 2014; Lin et al. 2014; Lin and Li, 2015; Basher and Sadorsky; 2016; Maghyereh et al., 2017; among many others) on MGARCH hedging.”