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Sahamkhadam, Maziar
Publikasjoner (2 av 2) Visa alla publikasjoner
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
Åpne denne publikasjonen i ny fane eller vindu >>Enhancing the predictability of crude oil markets with hybrid wavelet approaches
2019 (engelsk)Inngår i: Economics Letters, ISSN 0165-1765, E-ISSN 1873-7374, Vol. 182, s. 50-54Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Elsevier, 2019
Emneord
Wavelet decomposition, Forecasting, Crude oil
HSV kategori
Forskningsprogram
Ekonomi
Identifikatorer
urn:nbn:se:lnu:diva-89244 (URN)10.1016/j.econlet.2019.05.041 (DOI)000481724400013 ()
Tilgjengelig fra: 2019-09-24 Laget: 2019-09-24 Sist oppdatert: 2019-09-24bibliografisk kontrollert
Sahamkhadam, M., Stephan, A. & Östermark, R. (2018). Portfolio optimization based on GARCH-EVT-Copula forecasting models. International Journal of Forecasting, 34(3), 497-506
Åpne denne publikasjonen i ny fane eller vindu >>Portfolio optimization based on GARCH-EVT-Copula forecasting models
2018 (engelsk)Inngår i: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 34, nr 3, s. 497-506Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Elsevier, 2018
Emneord
GARCH models, Extreme value theory, Copula models, Conditional value-at-risk, Portfolio optimization
HSV kategori
Forskningsprogram
Ekonomi
Identifikatorer
urn:nbn:se:lnu:diva-79013 (URN)10.1016/j.ijforecast.2018.02.004 (DOI)000449722000008 ()2-s2.0-85046680082 (Scopus ID)
Tilgjengelig fra: 2018-12-06 Laget: 2018-12-06 Sist oppdatert: 2019-08-29bibliografisk kontrollert
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