Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach

Energies

Kaijian He¹  Kin Keung Lai² 

[1] Business School, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China; E-Mail:;Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong; E-Mail:

关键词: Portfolio Value at Risk;    multivariate wavelet analysis;    Exponential Weighted Moving Average (EWMA) model;    DCC-GARCH model;    multivariate time series model;    heterogeneous market hypothesis;   

DOI  :  10.3390/en5041018

来源: mdpi

【 摘 要 】

In the increasingly globalized economy these days, the major crude oil markets worldwide are seeing higher level of integration, which results in higher level of dependency and transmission of risks among different markets. Thus the risk of the typical multi-asset crude oil portfolio is influenced by dynamic correlation among different assets, which has both normal and transient behaviors. This paper proposes a novel multivariate wavelet denoising based approach for estimating Portfolio Value at Risk (PVaR). The multivariate wavelet analysis is introduced to analyze the multi-scale behaviors of the correlation among different markets and the portfolio volatility behavior in the higher dimensional time scale domain. The heterogeneous data and noise behavior are addressed in the proposed multi-scale denoising based PVaR estimation algorithm, which also incorporates the mainstream time series to address other well known data features such as autocorrelation and volatility clustering. Empirical studies suggest that the proposed algorithm outperforms the benchmark Exponential Weighted Moving Average (EWMA) and DCC-GARCH model, in terms of conventional performance evaluation criteria for the model reliability.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.