Energy markets volatility modelling using GARCH

By O. Efimova, Apostolos Serletis. 2014

219 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/0209db5b3c83fc6739e87f3ea915c3a4c2eed23f

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

1. George Filis
2. Stavros Degiannakis *
3. Rangan Gupta
4. Elie Bouri
5. Feng Ma
6. David Roubaud
7. Yu Wei
8. Vipin Arora

In Electrical and Electronic Engineering

1. Stavros Degiannakis *
2. George Filis
3. M. Wahab
4. Feng Ma
5. Rangan Gupta
6. L. Gil‐Alana
7. Weiju Xu
8. David Roubaud

Citations in the corpus

• Electricity prices, large-scale renewable integration, and policy implications (2017, background): “(2005), Karakatsani and Bunn (2010), and Efimova and Serletis (2014). Finally, mean reversion is another specific characteristic of electricity prices, mainly driven by weather conditions (Koopman et al.”
• Time-Varying Effect of Oil Market Shocks on the Stock Market (2015, unknown): “7 Efimova and Serletis (2014) provide a detailed examination of volatility in crude oil prices and in other energy prices.”
• Forecasting oil price realized volatility using information channels from other asset classes (2017, methodology): “A study that is quite distinct is this of Efimova and Serletis (2014), as it is the first paper to consider the inclusion of an additional asset class in their models in order to assess if this yields better forecasts for the oil price volatility.”
• Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence (2018, background): “On the other hand, in the oil price volatility literature Efimova and Serletis (2014) use multivariable GARCH models (such as BEKK and DCC), whereas Phan et al. (2016) use an EGARCH(1,1) model with and without exogenous variables.”
• Forecasting the realized range-based volatility using dynamic model averaging approach (2017, background): “…focus on modelling and forecasting volatility of financial and commodity markets using low-frequency data (e.g., Agnolucci, 2009; Cheong, 2009; Wei et al., 2010; Mohammadi and Su, 2010; Nomikos and Andriosopoulos, 2012; Charles and Darné, 2014; Efimova and Serletis, 2014; Lean and Smyth, 2015).”
• Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks (2018, methodology): “10 squared returns were used in Lobato and Savin (1998), Gil-Alana (2003), Cavalcante and Assaf (2004) and Cotter (2005).”
• Forecasting the realized volatility of the oil futures market: A regime switching approach (2017, methodology): “…oil price volatility are investigated based on the framework of the GARCH-class models (e.g., Agnolucci, 2009; Cheong, 2009; Kang et al., 2009; Mohammadi and Su, 2010; Wei et al., 2010; Nomikos and Pouliasis, 2011; Nomikos and Andriosopoulos, 2012; Wang and Wu, 2012; Efimova and Serletis, 2014).”