lnu.sePublications
Change search
Link to record
Permanent link

Direct link
BETA
Sahamkhadam, Maziar
Publications (2 of 2) Show all publications
Uddin, G. S., Gencay, R., Bekiros, S. & Sahamkhadam, M. (2019). Enhancing the predictability of crude oil markets with hybrid wavelet approaches. Economics Letters, 182, 50-54
Open this publication in new window or tab >>Enhancing the predictability of crude oil markets with hybrid wavelet approaches
2019 (English)In: Economics Letters, ISSN 0165-1765, E-ISSN 1873-7374, Vol. 182, p. 50-54Article in journal (Refereed) Published
Abstract [en]

We explore the robustness, efficiency and accuracy of the multi-scale forecasting in crude oil markets. We adopt a novel hybrid wavelet auto-ARMA model to detect the inherent nonlinear dynamics of crude oil returns with an explicitly defined hierarchical structure. Entropic estimation is employed to calculate the optimal level of the decomposition. The wavelet-based forecasting method accounts for the chaotic behavior of oil series, whilst captures drifts, spikes and other non-stationary effects which common frequency-domain methods miss out completely. These results shed new light upon the predictability of crude oil markets in nonstationary settings. (C) 2019 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Wavelet decomposition, Forecasting, Crude oil
National Category
Economics and Business
Research subject
Economy
Identifiers
urn:nbn:se:lnu:diva-89244 (URN)10.1016/j.econlet.2019.05.041 (DOI)000481724400013 ()
Available from: 2019-09-24 Created: 2019-09-24 Last updated: 2019-09-24Bibliographically approved
Sahamkhadam, M., Stephan, A. & Östermark, R. (2018). Portfolio optimization based on GARCH-EVT-Copula forecasting models. International Journal of Forecasting, 34(3), 497-506
Open this publication in new window or tab >>Portfolio optimization based on GARCH-EVT-Copula forecasting models
2018 (English)In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 34, no 3, p. 497-506Article in journal (Refereed) Published
Abstract [en]

This study uses GARCH-EVT-copula and ARMA-GARCH-EVT-copula models to perform out-of-sample forecasts and simulate one-day-ahead returns for ten stock indexes. We construct optimal portfolios based on the global minimum variance (GMV), minimum conditional value-at-risk (Min-CVaR) and certainty equivalence tangency (CET) criteria, and model the dependence structure between stock market returns by employing elliptical (Student-t and Gaussian) and Archimedean (Clayton, Frank and Gumbel) copulas. We analyze the performances of 288 risk modeling portfolio strategies using out-of-sample back-testing. Our main finding is that the CET portfolio, based on ARMA-GARCH-EVT-copula forecasts, outperforms the benchmark portfolio based on historical returns. The regression analyses show that GARCH-EVT forecasting models, which use Gaussian or Student-t copulas, are best at reducing the portfolio risk. (C) 2018 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
GARCH models, Extreme value theory, Copula models, Conditional value-at-risk, Portfolio optimization
National Category
Economics and Business
Research subject
Economy
Identifiers
urn:nbn:se:lnu:diva-79013 (URN)10.1016/j.ijforecast.2018.02.004 (DOI)000449722000008 ()2-s2.0-85046680082 (Scopus ID)
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2019-08-29Bibliographically approved
Organisations

Search in DiVA

Show all publications