Managing Risk with Energy Commodities using Value-at-Risk and Extreme Value Theory
(2017) NEKN02 20171Department 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)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8914486
- author
- Noshkov, Alexander LU and Demirtas, Zafer
- supervisor
- organization
- course
- NEKN02 20171
- year
- 2017
- 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}},
language = {{eng}},
note = {{Student Paper}},
title = {{Managing Risk with Energy Commodities using Value-at-Risk and Extreme Value Theory}},
year = {{2017}},
}