A time-varying copula approach to oil and stock market dependence: The case of transition economies

By Riadh Aloui, Shawkat Hammoudeh, Duc Khuong Nguyen. 2013

230 citations recorded by the source corpus.

Subfields: Economics and Econometrics, General Energy

View Paper: https://www.semanticscholar.org/paper/1764dfa6676fb0fd0858e5df6badf26867789d06

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Citations in the corpus

Extreme dependence and risk spillovers between oil and Islamic stock markets (2017, methodology): “…or shocks and Islamic and/or conventional equity returns presents results based on different methodologies for examples of copula dependence (Aloui et al., 2013), co-spectral analysis (Creti et al., 2014), wavelet analysis (Ftiti et al., 2015), linear and non-linear portfolio optimization…”
Forecasting: theory and practice (2020, background): “from Alquist et al. (2020) who link global economic activity with commodity price co-movement. The impact of energy shocks on US agricultural productivity was investigated by Wang and McPhail (2014) while Koirala et al. (2015) explore the non-linear correlations of energy and agricultural prices wit”
Dependences and volatility spillovers between the oil and stock markets: New evidence from the copula and VAR-BEKK-GARCH models (2020, background): “, 2014); and strength of tail dependence and contagion risk in transition economies (Aloui et al., 2013).”
A new time-varying optimal copula model identifying the dependence across markets (2017, methodology): “The rolling window analysis is the common approach in a time-series analysis, and many papers utilize the rolling window analysis to research the dynamic characteristics of economies or markets (Swanson, 1998; Fan and Gu, 2003; Hill, 2007; Aloui et al. 2013). Similarly, the rolling window is applied”
Time-Varying Effect of Oil Market Shocks on the Stock Market (2015, unknown): “Aloui et al. (2013) find positive conditional dependence between crude oil price and stock markets in the transition economies of Central and Eastern Europe. The literature has provided a wide range of possible connections between oil price and stock markets. Jiménez-Rodríguez and Sanchez (2005) arg”
Uncertainty and Crude Oil Returns (2016, methodology): “Following Aloui et al. (2013b), we estimate the copula parameters based on a rolling window of 250 days.10 Again, we apply the empirical cumulative distribution function (ECDF) for the marginal distributions and estimate the copula dependence parameters.”