id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
dem-8594	Ratuszny, Ewa	Risk Modeling of Commodities using CAViaR Models, the Encompassing Method and the Combined Forecasts	2016	28	.pdf	application/pdf	10088	387	54	We try to verify the following hypothesis: The encompassing method or combining forecast methods based on CAViaR models and implied quantile model improve accuracy of VaR for commodities. Estimated parameters of linear combination method Model Long position Short position 0.01 0.05 0.01 0.05 1γ 2γ 3γ 1γ 2γ 3γ 1γ 2γ 3γ 1γ 2γ 3γ Gold SAV –0.002 0.826 0.265 –0.005 1.153 0.104 –0.009 2.121 –0.584 –0.001 1.624 –0.403 AS –0.001 1.091 –0.025 –0.005 1.187 0.090 –0.011 1.571 0.020 –0.001 1.598 –0.381 Indirect GARCH –0.001 1.120 –0.057 –0.005 1.057 0.178 –0.008 2.253 –0.746 0.000 1.622 –0.468 AD 0.028 1.215 –0.956 0.005 1.294 –0.460 0.027 1.637 –1.287 0.005 1.188 –0.378 Oil SAV –0.010 0.650 0.572 –0.008 0.581 0.639 –0.011 0.593 0.601 –0.013 0.907 0.454 AS –0.006 0.561 0.575 –0.006 0.296 0.863 –0.008 0.198 0.950 –0.007 0.447 0.758 Indirect GARCH –0.014 0.617 0.686 –0.007 0.201 0.969 –0.010 0.492 0.704 –0.012 0.943 0.417 AD –0.016 1.338 0.035 –0.015 1.314 0.162 –0.029 1.235 0.236 –0.123 1.476 3.111 Note: 0.01; 0.05 – α-significance level of VaR; bolded values indicate models with higher value of parameter for forecasts derived on the basis of implied quantile model For determining the coefficient λ the EWQR method is applied.	cache/dem-8594.pdf	txt/dem-8594.txt