Conventional Energy

The regression results in the previous chapter reveal that major renewable energy indexes tend to be more risky relative to conventional (fossil-fuelled) ones. In Table 15, I report regression results for the traditional tracking error from Equations (15) and (16) and for the downside tracking errors from Equations (17) and (18).

Across my full sample of conventional energy indexes, I observe that 13 out of 18 energy indexes have monthly pair-wise tracking errors of 9 percent or more. To interpret tracking error volatilities from the perspective of a conventional energy investor, rather than a renewable energy investors (see previous chapter), higher pair-wise tracking errors actually indicate lower investment risks for conventional energy investors. The reason is that my dependent variables are renewable energy index returns and my independent variable are conventional energy index returns. Higher pair-wise tracking error volatilities should therefore be interpreted as high for renewable energy indexes relative to conventional energy indexes. My finding supports the argument that major conventional energy indexes have lower investment risks relative to renewable energy indexes. Similarly, downside tracking error estimates support my finding of lower downside tracking error volatilities for conventional energy indexes, while being generally somewhat larger in magnitude than the traditional risk measure.

Based on my findings, there could be two plausible explanations why the return volatilities of conventional fossil-fuelled energy producers have been historically lower relative to the returns of renewable energy firms. First, lower return volatility in the conventional energy sector such as oil and gas producers is driven by the level of capital investment (Sadorsky, 2001). Historically, as Sadorsky (2001) notes, capital investments to develop more advanced energy technologies, have been much higher in the oil and gas sector than in the renewable energy sector. A direct relationship between capital investment and return volatility was documented by Haushalter et al. (2002), who find that increased capital investment reduces stock return volatility. This means, when capital investments to the oil and gas sector remain high, then this could reduce the return volatilities of oil and gas companies. Second, an influential driver that could lower return volatilities of conventional (fossil-fuelled) energy companies is a high price for crude oil. Several studies have found that crude oil positively impacts on the stock returns of conventional energy producers, whereby increasing oil prices result in higher profit margins/cash flows for conventional energy companies and ultimately materialise in a higher stock price (Mohamed, 2012; Scholtens and Yurtsever, 2012). It is evident that during my sample period the oil price was on a steady increase. An increasing oil price could have been driven by strong demand, but it seems likely that the enormous subventions to the coventional fossil-fuel sector (see Table 9 'Worldwide Energy Consumption Subsidies') have played its role as well. For example, since 2009, the oil and gas sector has received at least 5 times the amount of subsidies relative to the renewable energy sector. Another indication for conventional energies' lower return volatilities can be found in the volatility of the crude oil price. As the volatility of the firm is largely driven by changes in the crude oil price and the volatility of crude oil has been found to be around 25 percent, energy producers' stock return volatility is expected to be about the same (Boyer and Filion, 2007; Sadorsky, 2001).

I further partition Table 15 into four panels in order to group renewable and conventional energy indexes according to geographic characteristics. Panel A of Table 15 contrasts different geographic regions of renewable and conventional energy indexes. Panel B, C and D compare European, North American and Asian energy equity indexes, respectively. I find North American energy equity indexes to experience the lowest tracking and downside tracking errors. This implies that renewable and conventional energy indexes have more similar return volatility patterns in North America than in any other region. Global energy indexes have the second lowest risk profile. European and Asian conventional energy

and North American energy equity indexes. In particular, Euro Stoxx Oil & Gas and DJ Europe Oil & Gas, have the lowest investment risk compared to their European renewable energy peers, AGAE Europe and European Renewable Energy. Comparing average investment risks across all geographic regions and the full sample of 18 conventional energy indexes, I find four indexes to have the lowest return volatilities. In particular, these are Euro Stoxx Oil & Gas, HFRX EH Energy, DJ US Int. Oil & Gas, and Daxglobal Asia Oil & Gas.

Table 15: Tracking Error and Downside Tracking Error Regressions

_{D1 D2 D3 D4} Global Europe _{D5 D12 D13 D17 D10 D11 D6 D7} North _D8 America _{D9 D18 D14} Asia _{D15 D16}

Panel A: Global C1 TE 0.0923 0.0899 0.0903 0.0880 0.0870 0.0745 0.0811 0.0729 0.0972 0.0929 0.0955 0.0991 0.0940 0.0861 0.0743 0.0827 0.0902 0.0945 TE Down 0.0931 0.0897 0.0903 0.0878 0.0846 0.0819 0.0903 0.0905 0.1030 0.0984 0.0969 0.1035 0.0952 0.0903 0.0867 0.1010 0.0981 0.0963 C2 TE 0.0932 0.0907 0.0908 0.0888 0.0876 0.0772 0.0843 0.0767 0.0973 0.0924 0.0972 0.1012 0.0947 0.0846 0.0773 0.0840 0.0928 0.0959 TE Down 0.0941 0.0913 0.0941 0.0905 0.0852 0.0852 0.0947 0.0948 0.1065 0.0999 0.0994 0.1051 0.0970 0.0954 0.0880 0.0974 0.1066 0.0981 C5 TE 0.1179 0.1152 0.1131 0.1131 0.1130 0.1265 0.1332 0.1251 0.1316 0.1175 0.1259 0.1353 0.1175 0.1259 0.1261 0.1338 0.1333 0.1129 TE Down 0.1200 0.1142 0.1146 0.1138 0.1146 0.1500 0.1539 0.1432 0.1520 0.1230 0.1269 0.1445 0.1191 0.1396 0.1342 0.1541 0.1411 0.1151 C6 TE 0.0549 0.0528 0.0526 0.0520 0.0547 0.0635 0.0680 0.0563 0.0649 0.0564 0.0580 0.0622 0.0550 0.0617 0.0594 0.0689 0.0695 0.0577 TE Down 0.0602 0.0566 0.0584 0.0562 0.0539 0.0638 0.0698 0.0626 0.0680 0.0623 0.0604 0.0655 0.0588 0.0671 0.0642 0.0717 0.0740 0.0630 C7 TE 0.0926 0.0909 0.0903 0.0900 0.0909 0.0952 0.0939 0.0900 0.0944 0.0942 0.0945 0.0989 0.0915 0.0987 0.0925 0.0948 0.1028 0.0919 TE Down 0.0987 0.0988 0.0977 0.0970 0.0887 0.1054 0.0992 0.1113 0.1005 0.1028 0.0996 0.1038 0.0961 0.1108 0.1063 0.1074 0.1143 0.0992 C8 TE 0.0481 0.0466 0.0448 0.0455 0.0422 0.0572 0.0592 0.0551 0.0606 0.0476 0.0551 0.0613 0.0501 0.0556 0.0588 0.0668 0.0624 0.0443 TE Down 0.0480 0.0456 0.0470 0.0452 0.0416 0.0615 0.0719 0.0651 0.0694 0.0515 0.0591 0.0661 0.0496 0.0631 0.0637 0.0791 0.0639 0.0419 C10 TE 0.0724 0.0703 0.0683 0.0687 0.0653 0.0810 0.0883 0.0820 0.0870 0.0717 0.0790 0.0858 0.0733 0.0786 0.0843 0.0886 0.0862 0.0671 TE Down 0.0770 0.0730 0.0731 0.0721 0.0674 0.0931 0.1145 0.1063 0.0996 0.0796 0.0870 0.0948 0.0754 0.0975 0.0988 0.1085 0.0975 0.0698 C12 TE 0.0677 0.0665 0.0636 0.0651 0.0652 0.0720 0.0823 0.0757 0.0805 0.0695 0.0745 0.0843 0.0669 0.0744 0.0724 0.0846 0.0821 0.0660 TE Down 0.0758 0.0757 0.0717 0.0745 0.0706 0.0789 0.0962 0.0909 0.0927 0.0776 0.0822 0.0941 0.0719 0.0799 0.0806 0.1051 0.0874 0.0667 Panel B: Europe C4 TE 0.0778 0.0753 0.0739 0.0734 0.0721 0.0941 0.1004 0.0862 0.0967 0.0760 0.0852 0.0927 0.0792 0.0903 0.0893 0.1026 0.0890 0.0719 TE Down 0.0776 0.0752 0.0747 0.0709 0.0748 0.1068 0.1146 0.1067 0.1129 0.0792 0.0891 0.0982 0.0798 0.1057 0.1018 0.1203 0.0921 0.0687 C13 TE 0.0837 0.0815 0.0808 0.0799 0.0815 0.0977 0.1043 0.0952 0.1004 0.0822 0.0889 0.0949 0.0832 0.0939 0.1014 0.1043 0.0973 0.0779 TE Down 0.0818 0.0772 0.0790 0.0773 0.0797 0.1070 0.1241 0.1142 0.1098 0.0820 0.0920 0.1042 0.0795 0.1029 0.1130 0.1255 0.1071 0.0759

Panel C: North America

C3 TE 0.1022 0.1002 0.1006 0.0986 0.0989 0.0730 0.0805 0.0671 0.1038 0.1031 0.1043 0.1071 0.1036 0.0899 0.0659 0.0813 0.0932 0.1052 TE Down 0.1024 0.0996 0.1007 0.1010 0.0984 0.0757 0.0830 0.0796 0.1107 0.1087 0.1057 0.1108 0.1034 0.0829 0.0718 0.0958 0.0968 0.1066 C11 TE 0.0326 0.0325 0.0316 0.0321 0.0268 0.0335 0.0398 0.0335 0.0394 0.0330 0.0376 0.0419 0.0348 0.0358 0.0361 0.0456 0.0387 0.0308 TE Down 0.0366 0.0373 0.0354 0.0356 0.0252 0.0370 0.0442 0.0399 0.0480 0.0399 0.0414 0.0457 0.0412 0.0418 0.0404 0.0572 0.0406 0.0302 C14 TE 0.0608 0.0587 0.0574 0.0574 0.0510 0.0628 0.0673 0.0635 0.0699 0.0609 0.0652 0.0707 0.0615 0.0644 0.0680 0.0699 0.0752 0.0571 TE Down 0.0664 0.0641 0.0644 0.0623 0.0523 0.0694 0.0740 0.0808 0.0769 0.0679 0.0730 0.0818 0.0651 0.0735 0.0727 0.0829 0.0844 0.0569 Panel D: Asia C9 TE 0.0903 0.0896 0.0891 0.0899 0.0788 0.0937 0.0921 0.0930 0.0989 0.0924 0.0929 0.0940 0.0909 0.0937 0.0939 0.0940 0.0831 0.0869 TE Down 0.0916 0.0917 0.0910 0.0937 0.0876 0.1042 0.0958 0.1035 0.1050 0.0979 0.0965 0.0949 0.0943 0.1079 0.1010 0.1028 0.0748 0.1015 Notes: This table reports monthly pair-wise tracking errors and downside tracking errors between my sample of fourteen renewable energy and eighteen conventional energy indexes from the first observation of each renewable energy index until February 2013. I compute pair-wise tracking errors from equations 10 and 11 in two stages. First, we regress excess renewable energy returns on excess conventional energy index returns. Second, I save the regression residual and compute the standard deviation for each of the 252 pairs. I obtain downside tracking errors by repeating the beforementioned procedure. Instead of computing standard deviations of regression residuals, I compute semi-standard deviations of regression residuals. I estimate linear OLS regressions with standard errors robust to autocorrelation and heteroskedasticity (Newey and West, 1987). Renewable and conventional energy indexes are grouped according to geographic regions. Boxed and shaded areas indicate energy indexes belonging to the same geographic investment region. All returns are denominated in US dollars.