TY - GEN
T1 - Market risk measurement for crude oil
T2 - 2006 International Joint Conference on Neural Networks (IJCNN '06)
AU - LAI, Kin Kernig
AU - HE, Kaijian
AU - XIE, Chi
AU - CHEN, Shou
PY - 2006
Y1 - 2006
N2 - With the development of technology and financial engineering tools, oil markets are more competitive and volatile than ever before. This places the accurate and reliable measurement of market risks in the crucial position for both investment decision and hedging strategy designs. This paper tackles the measurement of risks from a Value at Risk (VaR) perspective. Since traditional ARMA-GARCH approach doesn't suffice, this paper proposes ex-ante based approach for hybrid algorithm design and further applies this methodology with a wavelet approach to VaR estimates. Empirical studies of the proposed Wavelet Decomposed Value at Risk (WDVaR) have been conducted on two major oil markets (I.e. WTI & Brent). Experiment results suggest that the performance of WDVaR improves upon ARMA-GARCH model at higher confidence levels. Meanwhile, WDVaR offer considerable flexibility during modeling process. WDVaR can be tailored to specific market characteristics and its performance can be further improved with more careful parameter tuning.
AB - With the development of technology and financial engineering tools, oil markets are more competitive and volatile than ever before. This places the accurate and reliable measurement of market risks in the crucial position for both investment decision and hedging strategy designs. This paper tackles the measurement of risks from a Value at Risk (VaR) perspective. Since traditional ARMA-GARCH approach doesn't suffice, this paper proposes ex-ante based approach for hybrid algorithm design and further applies this methodology with a wavelet approach to VaR estimates. Empirical studies of the proposed Wavelet Decomposed Value at Risk (WDVaR) have been conducted on two major oil markets (I.e. WTI & Brent). Experiment results suggest that the performance of WDVaR improves upon ARMA-GARCH model at higher confidence levels. Meanwhile, WDVaR offer considerable flexibility during modeling process. WDVaR can be tailored to specific market characteristics and its performance can be further improved with more careful parameter tuning.
UR - http://www.scopus.com/inward/record.url?scp=40649125830&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-40649125830&origin=recordpage
U2 - 10.1109/IJCNN.2006.246984
DO - 10.1109/IJCNN.2006.246984
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0780394909
SN - 9780780394902
SP - 2129
EP - 2136
BT - The 2006 IEEE International Joint Conference on Neural Network Proceedings
PB - IEEE
Y2 - 16 July 2006 through 21 July 2006
ER -