Table A2

Out-of-sample forecast evaluation temperature anomaly [Temp]

Historical average versus temp
GlobalEuropeUSAsiaSolarWind
RMSEHATempHATempHATempHATempHATempHATemp
h = 104.37224.37594.63434.74173.75883.87556.35386.45348.10238.14805.45525.4948
h = 304.38674.39904.62404.73803.74393.86996.32556.42438.07028.11475.48315.5117
h = 605.07015.05844.95155.03864.31644.54996.67156.72878.47288.52995.43035.4614
h = 120
5.0428
5.0312
4.9345
5.0194
4.3053
4.5301
6.6474
6.7015
8.4365
8.4941
5.3843
5.4097
CW
HA
Temp
HA
Temp
HA
Temp
HA
Temp
HA
Temp
HA
Temp
h = 101.3772−0.14590.2770−0.5527−0.6728**2.2345
(1.2032)(0.9112)(0.8014)(1.1066)(0.3349)(2.3695)
h = 301.2904−0.20450.2302−0.5399−0.6497*2.3054
(1.1930)(0.9048)(0.7955)(1.0966)(0.3327)(2.3236)
h = 601.5045−0.0116−0.8591−0.0413−0.8990**2.2491
(1.1993)(0.9172)(1.3184)(1.1957)(0.4134)(2.2792)
h = 1201.48790.0074−0.74290.0040−0.9028**2.3088
(1.1864)(0.9093)(1.3118)(1.1859)(0.4097)(2.2366)

Note(s): The table presents the results for the forecast evaluation of the climate risk based models where temperature anomaly serves as the predictor. The forecast evaluation analysis compares the climate risk-based models with the HA model given that we are dealing with the returns series of the clean energy prices. The climate risk-based models and HA model are “nested” since the latter can be seen as a subset of the former, hence the choice of Clark and West (CW) as the forecast evaluation test. The rejection of the null hypothesis of CW test indicates the better performance of the preferred model (climate risk-based models). “*”, “**” and ”***” indicates statistical significance at 10%, 5% and 1% significance levels

Source(s): Table by authors

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