Dependence and risk assessment for oil prices and exchange rate portfolios: A wavelet based approach

Chaker Aloui and Rania Jammazi

Physica A: Statistical Mechanics and its Applications, 2015, vol. 436, issue C, 62-86

Abstract: In this article, we propose a wavelet-based approach to accommodate the stylized facts and complex structure of financial data, caused by frequent and abrupt changes of markets and noises. Specifically, we show how the combination of both continuous and discrete wavelet transforms with traditional financial models helps improve portfolio’s market risk assessment. In the empirical stage, three wavelet-based models (wavelet-EGARCH with dynamic conditional correlations, wavelet-copula, and wavelet-extreme value) are considered and applied to crude oil price and US dollar exchange rate data. Our findings show that the wavelet-based approach provides an effective and powerful tool for detecting extreme moments and improving the accuracy of VaR and Expected Shortfall estimates of oil–exchange rate portfolios after noise is removed from the original data.

Keywords: Wavelet analysis; Extreme value theory; Copulas; DCC-eGARCH; VaR; Oil–exchange rate portfolios (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:436:y:2015:i:c:p:62-86

DOI: 10.1016/j.physa.2015.05.036

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