P. Sadorsky r Energy Economics 23 2001 17]28 22
Fig. 1. TSE oil and gas index and crude oil prices.
R is the market portfolio excess return which is the return to the TSE 300
mt
value weighted common stock price index in excess of the 1-month T-bill rate. The market return is a proxy for changes in aggregate economic wealth that affects risk
Ž .
premia and expected returns Fama and French, 1989; Ferson and Harvey, 1991 . The variable R
is the monthly growth rate of the Canada]US exchange rate
et
Ž .
Ž .
USrC . This is the exchange rate CrUS most relevant to Canadian compa- nies. The exchange rate variable is a proxy for foreign exchange risk. Exchange rate
Ž .
risk may be particularly important for multinationals Jorion, 1990 or natural Ž
. Ž
. resource companies Louden, 1993; Khoo, 1994 . For example, Louden 1993 and
Ž .
Khoo 1994 both found that Australian gold stock movements were related to exchange rate movements. Overall there has been little work done on the impact of
foreign exchange risk exposure to oil and gas companies. A priori, it is expected that oil price and exchange rate factors each have a
positive impact on oil and gas stock returns, while the term premium has a negative impact on oil and gas stock returns. If oil and gas stocks are useful for hedging
Ž .
inflation then the market beta should be negative Pring, 1991 .
4. Empirical results
Summary statistics for the data are presented in Table 1. The t-statistics indicate that oil and gas share price returns, market returns, crude oil price returns, and
exchange rate returns do not have significant means while the term premium does have a significant mean.
The correlation matrix for the data indicates that market returns and oil price returns are each positively correlated with oil and gas share price returns, while
P. Sadorsky r Energy Economics 23 2001 17]28 23
Table 1 Summary statistics for the data
Variable Mean
S.D. t
-statistic R
y 0.003026
0.067366 y
0.62
i
R y
0.000423 0.017868
y 0.33
m
R y
0.003372 0.095950
y 0.49
o
R 0.209688
0.284750 10.20
tp
R 0.000980
0.010388 1.31
e
Table 2 Correlation matrix
R R
R R
R
i m
o tp
e
R 1.000
0.207 0.439
y 0.083
y 0.207
i
R 1.000
0.023 y
0.069 y
0.019
m
R 1.000
0.103 y
0.062
o
R 1.000
0.146
tp
R 1.000
e
exchange rate returns and the term premium are each negatively correlated with Ž
. oil and gas share price returns Table 2 . Multicollinearity is not present among the
explanatory variables. Visual inspection of the data indicates none of the series exhibit trends.
Consequently, all unit root test regressions were run with a constant but no trend Ž
. term. Table 3 reports the results from Dickey and Fuller 1979 and Phillips and
Ž .
Perron 1988 unit root tests. Both the Dickey and Fuller test results and the Ž
. Phillips and Perron 1988 test results indicate that each variable is stationary in
first differences at the 1 level of significance. Table 4 presents regression equation results for the single factor model and the
Ž . Ž .
multifactor models 1 and 2 . Standard errors for the parameter estimates are
Table 3
a
Unit root tests Variable
ADF l
PP k
UUU UUU
R y
6.58 4
y 13.81
4
i UUU
UUU
R y
4.62 4
y 4.53
4
m UUU
UUU
R y
8.18 4
y 11.33
4
o UUU
UUU
R y
4.81 4
y 8.19
4
tp UUU
UUU
R y
6.25 4
y 11.75
4
e a
Ž .
UUU UU
U
Critical values from Hamilton 1994 . ,
, denotes a statistic is significant at the 1, 5 and
10 level of significance, respectively. The parameter, l, is a truncated lag parameter used in the non-parametric correction for serial correlation and is set according to the sample size. The parameter
Ž .
k is set using the t-sig criterion of Perron 1997 .
P. Sadorsky r Energy Economics 23 2001 17]28 24
Table 4
a
Parameter estimates Information
Dependent variable variable
R R
R
i i
i
Intercept y
0.003 y
0.001 0.004
0.54 0.60
0.38 R
0.779 0.741
0.705
m
0.00 0.00
0.00 R
0.305 0.305
o
0.00 0.00
R y
0.021
tp
0.06 R
y 1.055
e
0.05
2
Adjusted R 0.04
0.22 0.25
Ž .
Residual diagnostic tests P values are reported Ž .
AR 1 0.62
0.30 0.26
Ž . AR 6
0.53 0.05
0.08 Ž .
ARCH 1 0.00
0.01 0.06
Ž . ARCH 6
0.07 0.23
0.38 Ž .
RESET 2 0.07
0.52 0.91
Ž . RESET 3
0.00 0.63
0.56
a
Ž .
Newey and West 1987 HAC standard errors are used to compute estimated coefficient P-values. Ž .
Ž . P
values are shown below coefficient estimates. AR 1 and AR 6 are Breusch and Godfrey lagrange Ž
. Ž .
multiplier test statistics for residual serial correlation at lags 1 and 6 Greene, 1990 . ARCH 1 and Ž .
Ž .
ARCH 6 are Engle 1982 lagrange multiplier test statistics for residual ARCH effects at lags 1 and 6. Ž
. RESET is a Ramsey 1969 miss-specification test with degrees of freedom in parentheses.
Ž .
computed using the Newey and West 1987 heteroskedasticity and autocorrelation coefficient consistent covariance matrix. Results for the single factor model indi-
cate that the estimated coefficient on the market return is statistically significant. This is not too surprising given the large literature on the capital asset pricing
Ž .
model CAPM . The estimated beta of 0.78 indicates that the oil and gas industry has been less risky than the market.
4
The positive market beta indicates that oil and gas stocks were not a good hedging tool over the period 1983:05]1999:04. The
adjusted R
2
value is low and the regression diagnostic tests indicate evidence of Ž
. autoregressive conditional heteroskedasticity ARCH effects and miss-specified
w Ž
. x
functional form as measured by Ramsey 1969 RESET tests . Ž .
Results for model 1 indicate a market beta of 0.74 and an oil beta of 0.31. Both of these coefficients are positive and statistically significant. The adjusted R
2
value
4
While the industry has been less risky than the overall market, this does not imply that each individual stock has market beta less than unity. In fact, small oil and gas producers are more likely to
have market betas greater than unity.
P. Sadorsky r Energy Economics 23 2001 17]28 25
indicates that 22 of the variation in oil and gas share price returns can be Ž .
explained by market returns and oil price returns. Model 1 has considerably higher explanatory power than the single factor market model. The residual
diagnostic test statistics, however, indicate the presence of ARCH effects. Ž .
Estimation results for model 2 are presented in the fourth column of Table 4. The general finding is that each of the multifactor variables is individually statisti-
Ž .
cally significant. The multifactor market return beta 0.71 is numerically similar to the market return betas estimated in the other two regressions. This provides some
Ž .
support for the robustness of the market return beta. The oil price beta 0.31 is positive and statistically significant. This result provides evidence to support the
conjecture that oil price movements impact oil and gas stock returns. The esti- mated coefficients on the term premium variable and the exchange rate variable
are each negative and statistically significant. A higher term premium increases borrowing costs thereby lowering oil and gas stock returns. The negative coefficient
on the exchange rate variable, measured as CrUS, is somewhat unexpected given that a lower Canadian dollar helps Canadian energy exports. To obtain a
negative sign on the exchange rate variable, a depreciation of the domestic currency must increase firm costs by more than it increases firm revenues. The
Ž .
overall effect, which has been observed in the gold mining industry by Khoo 1994 , is a deterioration of a firm’s financial position. For example, Canadian companies
importing machinery and equipment from the United States and borrowing money outside of Canada would all be financially worse off with a falling Canadian dollar.
Ž . The multifactor market model represented by Eq. 2 in the fourth column of
2
Ž .
Table 4 has the highest adjusted R value 0.25 . The joint F-test constraining all slope coefficients equal to zero is rejected at the 0.00 probability value. This
indicates the significance of having all four of these variables in the regression model. The regression diagnostic tests indicate that at the 5 level of significance,
there is no evidence of serial correlation, ARCH effects or miss-specified functio- nal form. Consequently, this model appears to be adequately specified.
The estimation period covers the somewhat turbulent time of the late 1980s and the early 1990s. Consequently it is important to test the multifactor model in Eq.
Ž . 2 for structural breaks. This is accomplished by recursively estimating regression
Ž . Eq. 2 over the sample period. One way of checking parameter stability is to
estimate the multifactor market model recursively and plot the recursively esti- mated coefficients with associated standard error bands. The plots shown in Fig. 2
suggest that a structural stability problem probably does not exist since, for each factor, the size of the estimated coefficient is fairly similar over the entire sample
period.
5. Concluding remarks
