Estimation of Value-at-Risk for Exchange Risk Via Kernel Based Nonlinear Ensembled Multi Scale Model

Authors:
Kaijian He;Chi Xie;Kinkeung Lai
Affiliations:
College of Business Administration, Hunan University, Changsha, China 410082 and Department of Management Sciences, City University of Hong Kong, Hong Kong,;College of Business Administration, Hunan University, Changsha, China 410082;College of Business Administration, Hunan University, Changsha, China 410082 and Department of Management Sciences, City University of Hong Kong, Hong Kong,
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Year:
2008

Citing 3
Cited 0

A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates

Computers and Operations Research
A hybrid model for exchange rate prediction

Decision Support Systems
Knowledge discovery in financial investment for forecasting and trading strategy through wavelet-based SOM networks

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.