78 R.W. Faff, T.J. Brailsford Journal of Energy Finance and Development 4 1999 69–87
Table 2 Estimation of the Market Model Augmented by an Oil Factor: 1983:07 to 1996:03
ASX Industry a
i
b
i
g
i
w
i a
R
2
DW 1. Gold
2 0.0093
1.3977 0.0759
0.2098 0.571
1.939 21.23
14.19 1.21
2.62 2. Other Metals
2 0.0069
1.3031 2
0.0358 —
0.700 1.778
21.65 18.50
20.84 3. Solid Fuels
0.0031 0.6648
2 0.0117
— 0.427
1.732 0.81
10.47 20.31
4. Oil and Gas 2
0.0048 0.9772
0.2349 0.1891
0.736 2.006
21.27 19.07
7.23 2.35
5. Diversified 0.0052
1.0275 0.1276
— 0.739
1.716 Resources
1.72 20.49
4.21 6. Developers and
2 0.0018
1.0628 2
0.0249 —
0.797 1.927
Contractors 20.67
24.01 20.93
7. Building Materials 0.0011
0.8157 2
0.0033 —
0.767 2.056
0.50 22.07
20.15 8. Alcohol and
0.0065 0.8649
2 0.0197
0.1740 0.498
1.954 Tobacco
1.25 11.99
20.43 2.14
9. Food and 0.0053
0.8029 2
0.0706 —
0.693 1.929
Household Goods 1.96
17.82 22.60
10. Chemicals 0.0082
0.8017 2
0.0424 —
0.619 2.052
2.63 15.31
21.34 11. Engineering
0.0014 0.7511
2 0.0196
— 0.629
1.890 0.47
15.78 20.68
12. Paper and Packaging 0.0029
0.7772 2
0.0884 —
0.678 1.994
1.06 17.04
23.21 13. Retail
0.0006 0.8581
2 0.0439
— 0.696
1.737 0.23
18.20 21.54
14. Transport 2
0.0004 1.1098
2 0.0959
— 0.742
1.734 20.13
20.13 22.88
continued
Research in Finance CRIF at the Australian Graduate School of Management. The proxy for the market portfolio used is a value-weighted domestic index supplied by
CRIF and a value-weighted global index supplied by Morgan Stanley. The oil price data are obtained from Equinet. Of note is that the oil price displayed considerable
volatility over the period, with a major price fall occurring in late 1985 and a major price spike during 1990, around the time of the Gulf War.
4. Results
First, consider some basic descriptive statistics for the oil price return series. Over the full sample period, the average monthly rate of return is about 20.10 measured
in Australian dollars, compared to about 20.18 when measured in U.S. dollars. A time series analysis of the oil return reveals a strong predictable structure, as reflected
R.W. Faff, T.J. Brailsford Journal of Energy Finance and Development 4 1999 69–87 79
Table 2 continued ASX Industry
a
i
b
i
g
i
w
i a
R
2
DW 15. Media
0.0068 1.1810
2 0.0850
— 0.522
1.720 1.20
12.48 21.49
16. Banks 0.0059
0.7691 2
0.0873 —
0.585 1.949
1.78 13.94
22.62 17. Insurance
0.0012 0.8821
0.0886 0.2049
0.535 2.059
0.23 12.73
1.98 2.50
18. Entrepreneurial 2
0.0129 1.5372
0.0021 —
0.632 1.938
Investors 22.24
15.95 0.04
19. Investment and 0.0005
0.7486 0.0526
0.1710 0.692
1.962 Financial Services
0.17 17.60
1.96 2.12
20. Property Trusts 0.0062
0.4436 2
0.0051 —
0.538 1.897
3.07 13.10
20.25 21. Miscellaneous
0.0078 0.6336
2 0.0682
— 0.438
1.814 Services
2.15 10.39
21.85 22. Miscellaneous
2 0.0016
0.6892 0.0119
0.2276 0.648
2.007 Industrials
20.47 15.89
0.42 2.82
23. Diversified 0.0019
0.9943 2
0.0229 —
0.823 2.000
Industrials 0.82
26.10 20.99
24. Tourism and Leisure 0.0064
0.5746 2
0.0052 —
0.407 1.960
1.87 10.07
20.15 Number of Significant
Coefficients at 1 2
24 5
2 —
— This table reports the output from the following regression:
R
it
5 a
i
1 b
i
R
mt
1 g
i
OILRAUD
t
1 e
it
1 where R
it
is the return on the ith asset or portfolio in month t, R
mt
is the return on the market index in month t and OILRAUD
t
is the return on the oil price in month t expressed in Australian dollars. Coefficient estimate is significantly different from zero at the 1 level t statistics in parentheses.
a
The coefficient w is an estimate of the first-order autoregressive coefficient produced by the Cochrane Orcutt procedure for those instances in which significant autocorrelation is detected according
to the Durbin-Watson test in the original regression.
in the correlogram. A first-order autoregression for the AUD oil return substantially eliminates evidence of serial correlation in the correlogram.
7
However, the empirical analysis is qualitatively robust to whether the raw or the adjusted return series is
used.
8
Accordingly, only the raw return results are reported in this paper. In Table 2, the regression results of the two-factor model outlined in Eq. 1 are
presented for the full period from July 1983 to March 1996. First, all industries have a significant positive sensitivity to the market factor, consistent with traditional notions
of asset pricing. Second, five out of the 24 industries have a statistically significant sensitivity to the oil price factor, at the 1 level of significance.
9
The industries that have a statistically significant positive sensitivity to the oil price factor are Oil and Gas, and Diversified Resources. In comparison to our predictions,
the significant finding for these two industries are as expected. We also predicted that the Gold and Solid Fuels industries may have significant positive sensitivities to the
80 R.W. Faff, T.J. Brailsford Journal of Energy Finance and Development 4 1999 69–87
oil factor. As the empirical evidence does not support this prediction, we might conclude the effect of oil prices in these industries is relatively minor.
Conversely, the industries that have a statistically significant negative sensitivity to the oil price factor are Paper and Packaging, Transport, and Banks. The significant
negative sensitivity to the oil price factor of the Paper and Packaging, and Transport industries follows our earlier prediction, except that it may be possible for companies
in this industry to pass on higher fuel costs to their customers. Table 2 does not support a significant negative sensitivity for the Chemicals industry in contrast to the
prediction based upon its high value of the RDRC. The sign of the point estimate for this industry is as predicted but is insignificant. Perhaps this industry is one where
oil price changes can be readily passed on, or perhaps some form of hedging behavior is pursued to largely insulate companies within the industry from this risk. This latter
issue is beyond the current research parameters but would be an interesting area for future research.
The significant finding on the Banks industry is contrary to expectations. However, given that the majority of industries have a negative oil price sensitivity albeit in
many cases insignificantly so, the significant negative sensitivity of Banks may not be totally unexpected. Moreover, the success or profitability of bank customers will
have a strong impact on the volume and profitability of the banking business. But we recognize that this is a tenuous explanation.
The finding of both positive and negative effects on an industry basis implies that analysis at the aggregate market level may hide industry sector effects. As shown
here, different industry sensitivities are possible. Thus, markets with different concen- trations of particularly natural resources and industrial sectors may experience differ-
ential aggregate effects. For instance, Canada has a relatively high proportion of natural resource companies, and Jones and Kaul 1996 found that the overall negative
relationship between oil price shocks and stock returns is weakest for Canada. Con- versely, Japan has a very low natural resources base, and Jones and Kaul 1996 found
that the overall negative relationship between oil price shocks and stock returns is strongest for Japan.
As discussed earlier, the price of oil is determined in U.S. dollars in an international market, which suggests that the domestic price is made up of two components, namely,
a pure oil price return denominated in U.S dollars and an exchange rate factor “return.” A further issue is to assess the extent to which the results reflect these two
components. Consequently, Table 3 presents the estimation and analysis of the model as outlined in Eq. 7, which incorporates the return on the market, the return on the
oil price in U.S. dollars, and the “return” on the Australian dollar foreign exchange rate expressed relative to the U.S. dollar.
Table 3 presents the following information. The first and second columns reveal the estimates of g
i
and d
i
obtained from a regression of the model in Eq. 7. The next two columns provide Wald test statistics for the two hypotheses involving these
coefficients, namely 1 H : g
i
5 d
i
and 2 H : g
i
5 d
i
5 0.
In general, from column 1 we see that five of the industries have a significant sensitivity estimate to the oil price factor denominated in U.S. dollars in Eq. 7. In
R.W. Faff, T.J. Brailsford Journal of Energy Finance and Development 4 1999 69–87 81
comparison to Eq. 1, the Oil and Gas, and Diversified Resources industries have a consistent positive sensitivity see Table 2. Similarly, the Paper and Packaging, and
Transport industries have a consistent negative sensitivity. In none of these cases is the exchange rate factor also significant.
10
Furthermore, the Wald test rejects the null hypothesis that the two coefficients are jointly equal to zero, although we do not
reject the null hypothesis that the two coefficients are equal. The additional sensitivity is observed for the Food and Household Goods industry, which has a negative sign.
However, we note that the Wald test does not reject the null hypothesis that the two coefficients are jointly equal to zero.
Table 3 offers an insight into the anomalous result earlier observed for the Banks industry. Recall that the Banks industry experienced a significant negative sensitivity
to the oil price factor, contrary to expectations. We now observe that this sensitivity disappears with the inclusion of the foreign exchange factor and that the coefficient
on this factor is significant. Recall that rejection of the null hypothesis that the two coefficients are equal implies that the exchange rate factor offers additional explanatory
information over and above the oil price factor implying that Eq. 1 is mis-specified. In Table 3, we observe that this is the case for the Banks industry, implying that there
is an omitted variable that is in part attributable to the foreign exchange factor. Similarly, the Wald test also rejects the null hypothesis for the Building Materials and
Engineering industry. These results suggest that only the exchange rate factor should be included to augment the market model for these industries. We do not explore
these results further given the focus of the paper but note that they offer insight for more general multi-factor tests. However, it should be noted that in these cases the
AUD oil price sensitivity is of very similar magnitude and statistical significance.
5. Further analysis
