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Managing Risk with Energy Commodities using Value-at-Risk and Extreme Value Theory

Noshkov, Alexander LU and Demirtas, Zafer (2017) NEKN02 20171
Department of Economics
Abstract
Today’s society requires an endless supply of energy resources to keep functioning properly. The fluctuations in the prices of energy commodities are always a concern as it affects not only investors, but regular households as well. With the general turmoil the market is experiencing, the necessity for risk management has become of outmost importance. Thus, this paper provides an empirical study of the determination of the risk for four different energy commodities.

The focus is on crude oil (WTI), gasoline, natural gas and coal, as they represent most of the world’s energy consumption of today. The time periods from 2010 to 2016 will be analyzed as it represents a new period of increased volatility.

The empirical research... (More)
Today’s society requires an endless supply of energy resources to keep functioning properly. The fluctuations in the prices of energy commodities are always a concern as it affects not only investors, but regular households as well. With the general turmoil the market is experiencing, the necessity for risk management has become of outmost importance. Thus, this paper provides an empirical study of the determination of the risk for four different energy commodities.

The focus is on crude oil (WTI), gasoline, natural gas and coal, as they represent most of the world’s energy consumption of today. The time periods from 2010 to 2016 will be analyzed as it represents a new period of increased volatility.

The empirical research presented consists of calculating Value-at-Risk (VaR) for Value Weighted Historical Simulation (VWHS), Student’s t-distribution and the EVT Conditional Peaks over Threshold (POT) approaches together with three different volatility estimates, Generalized Autoregressive Conditional Heteroskedasticity (GARCH (1.1)), Exponentional GARCH (EGARCH) and Threshold GARCH (TGARCH). The different modeling approaches of volatility estimations will account for price asymmetries in the distributions.

Based of the empirical results, the study indicates that the Student’s t-distribution with the EGARCH volatility process is the preferred method for VaR estimation. (Less)
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author
Noshkov, Alexander LU and Demirtas, Zafer
supervisor
organization
course
NEKN02 20171
year
type
H1 - Master's Degree (One Year)
subject
keywords
Energy Commodities, Value-at-Risk (VaR), Extreme Value Theory (EVT), Peaks over Threshold (POT), Volatility Weighted Historical Simulation (VWHS), GARCH, EGARCH, TGARCH
language
English
id
8914486
date added to LUP
2017-06-13 15:19:02
date last changed
2017-06-13 15:19:02
@misc{8914486,
  abstract     = {Today’s society requires an endless supply of energy resources to keep functioning properly. The fluctuations in the prices of energy commodities are always a concern as it affects not only investors, but regular households as well. With the general turmoil the market is experiencing, the necessity for risk management has become of outmost importance. Thus, this paper provides an empirical study of the determination of the risk for four different energy commodities. 

The focus is on crude oil (WTI), gasoline, natural gas and coal, as they represent most of the world’s energy consumption of today. The time periods from 2010 to 2016 will be analyzed as it represents a new period of increased volatility. 

The empirical research presented consists of calculating Value-at-Risk (VaR) for Value Weighted Historical Simulation (VWHS), Student’s t-distribution and the EVT Conditional Peaks over Threshold (POT) approaches together with three different volatility estimates, Generalized Autoregressive Conditional Heteroskedasticity (GARCH (1.1)), Exponentional GARCH (EGARCH) and Threshold GARCH (TGARCH). The different modeling approaches of volatility estimations will account for price asymmetries in the distributions.

Based of the empirical results, the study indicates that the Student’s t-distribution with the EGARCH volatility process is the preferred method for VaR estimation.},
  author       = {Noshkov, Alexander and Demirtas, Zafer},
  keyword      = {Energy Commodities,Value-at-Risk (VaR),Extreme Value Theory (EVT),Peaks over Threshold (POT),Volatility Weighted Historical Simulation (VWHS),GARCH,EGARCH,TGARCH},
  language     = {eng},
  note         = {Student Paper},
  title        = {Managing Risk with Energy Commodities using Value-at-Risk and Extreme Value Theory},
  year         = {2017},
}