Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory

By Walid Chkili, Shawkat Hammoudeh, Duc Khuong Nguyen. 2014

234 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/80e6f483b2dc892c553618b690bf25ccac3a59b6

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. Stavros Degiannakis *
2. George Filis
3. A. B. Rejeb
4. Tony Klein
5. Vipin Arora
6. D. Broadstock
7. Ioannis Chatziantoniou
8. Perry Sadorsky

In Finance

1. George Filis
2. Stavros Degiannakis *
3. A. B. Rejeb
4. Tony Klein
5. R. Brooks

Citations in the corpus

• Bitcoin is not the New Gold – A comparison of volatility, correlation, and portfolio performance (2018, unknown): “Many types of financial time series are reported to attribute long memory in their variance; e.g. individual stocks, stock indices, commodities, and foreign exchange rates (i.a. Baillie, 1996, Bollerslev & Mikkelsen, 1996, Chkili et al., 2014, Walther et al., 2017).”
• Oil, Gold, US dollar and Stock market interdependencies: A global analytical insight (2017, background): “exchange rate affects oil price via demand and supply on international markets and support recent results of Fratzscher et al. (2014) for bidirectional causality between oil and USD and results of Le and Chang (2012) about the close link between prices of oil and gold.”
• Dynamic correlation of precious metals and flight-to-quality in developed markets (2017, background): “However, it is well known from literature that precious metals feature distinct properties such as asymmetry or long memory in variance ( Arouri et al., 2012; Chkili et al., 2014; Chkili, 2016; Hammoudeh et al., 2010 ).”
• Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence (2018, unknown): “(2015), Sevi (2014), Chkili et al .”
• Forecasting oil price realized volatility using information channels from other asset classes (2017, methodology): “Similarly, several other authors model the conditional volatility of oil prices and forecast these volatilities, using univariate models such as the FIAPARCH, HYGARCH, EGARCH, FIGARCH, APARCH, as well as multivariate models such as the BEKK, VAR and Risk Metrics (see, Agnolucci, 2009; Wei et al., 20”