TY - GEN
T1 - Estimation of value-at-risk for exchange risk via kernel based nonlinear ensembled multi scale model
AU - He, Kaijian
AU - Xie, Chi
AU - Lai, Kinkeung
PY - 2008
Y1 - 2008
N2 - Risk level in the exchange rate market is dynamically evolving with complicated structures. To further refine the analysis process and achieve more accurate measurement, this paper proposes a novel kernel based nonlinear ensembled multi scale Value at Risk methodology for evaluating the risk level in the exchange rate market. In the proposed algorithm, wavelet analysis is introduced to analyze the multi scale heterogeneous risk structures across different time scales. The Principle Component Analysis is used to extract principle components from the redundant forecast matrixes. Then the support vector regression technique is integrated into the modeling process to nonlinearly ensemble forecast matrixes and produce more stable and accurate results. Taking Euro market as a typical test case, empirical studies employing the proposed algorithm shows the superior performance than benchmark ARMA-GARCH and realized volatility based approaches. © 2008 Springer-Verlag Berlin Heidelberg.
AB - Risk level in the exchange rate market is dynamically evolving with complicated structures. To further refine the analysis process and achieve more accurate measurement, this paper proposes a novel kernel based nonlinear ensembled multi scale Value at Risk methodology for evaluating the risk level in the exchange rate market. In the proposed algorithm, wavelet analysis is introduced to analyze the multi scale heterogeneous risk structures across different time scales. The Principle Component Analysis is used to extract principle components from the redundant forecast matrixes. Then the support vector regression technique is integrated into the modeling process to nonlinearly ensemble forecast matrixes and produce more stable and accurate results. Taking Euro market as a typical test case, empirical studies employing the proposed algorithm shows the superior performance than benchmark ARMA-GARCH and realized volatility based approaches. © 2008 Springer-Verlag Berlin Heidelberg.
KW - Nonlinear ensemble
KW - Principle component analysis
KW - Support vector regression
KW - Value at risk
KW - Wavelet analysis
UR - https://www.scopus.com/pages/publications/59149083551
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-59149083551&origin=recordpage
U2 - 10.1007/978-3-540-87732-5_17
DO - 10.1007/978-3-540-87732-5_17
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 3540877312
SN - 9783540877318
VL - 5263 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 148
EP - 157
BT - Advances in Neural Networks - ISNN 2008
PB - Springer Verlag
T2 - 5th International Symposium on Neural Networks, ISNN 2008
Y2 - 24 September 2008 through 28 September 2008
ER -