Directory UMM :Data Elmu:jurnal:J-a:Journal of Energy Finance & Development:Vol4.Issue1.Jun1999:
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Journal of Energy Finance and Development 4 (1999) 69–87
Oil price risk and the Australian stock market
Robert W. Faff
a,*, Timothy J. Brailsford
baRMIT University, School of Economics and Finance, GPO Box 2476 V, Melbourne,
Victoria 3001, Australia
bAustralian National University, Department of Commerce, Canberra, ACT 0200, Australia Received August 1998; accepted February 1999
Abstract
The primary aim of this paper is to investigate the sensitivity of Australian industry equity returns to an oil price factor over the period 1983–1996. The paper employs an augmented market model to establish the sensitivity. The key findings are as follows. First, a degree of pervasiveness of an oil price factor, beyond the influence of the market, is detected across some Australian industries. Second, we propose and find significant positive oil price sensitivity in the Oil and Gas and Diversified Resources industries. Similarly, we propose and find significant negative oil price sensitivity in the Paper and Packaging, and Transport industries. Generally, we find that long-term effects persist, although we hypothesize that some firms have been able to pass on oil price changes to customers or hedge the risk. The results have implications for management in these industries and policy makers and enhance our
understanding of the “Dutch disease.” 1999 Elsevier Science Inc. All rights reserved.
Keywords:Asset pricing; Oil price risk; Australian industries
1. Introduction
Energy expenditures account for a relatively large proportion of GDP in most industrial nations. For instance, Jones and Kaul (1996) document that energy expenditure was as high as 14% of GDP in the U.S. during the 1980s. Similar figures have been documented for other countries in Helliwell et al. (1986). The importance of oil to individual economies and the world economy is well known and is demonstrated by the events surrounding the Iraqi invasion of Kuwait in 1990. Evidence is provided by Malliaris and Urrutia (1995), who document a strong negative share price reaction to the Persian Gulf crisis. Notably, the strongest effects were observed in markets in the Asian and Australian region.
* Corresponding author. Tel.:161-399-255-906. fax:161-399-255-986.
E-mail address: [email protected] (R.W. Faff)
1085-7443/99/$ – see front matter1999 Elsevier Science Inc. All rights reserved. PII: S1085-7443(99)00005-8
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There has been a continuing interest by researchers over recent years in the role and impact that oil and other energy sources have on financial markets and stock prices of modern corporations. Some researchers (e.g., Strong, 1991) have examined how well investors are able to hedge oil price risk using oil equity portfolios. Others, such as Miller and Upton (1985a, 1985b) and Crain and Jamal (1991), have investigated how well Hotelling’s valuation principle applies to oil and gas companies. Other research has analyzed forward and futures prices on oil-related contracts (see Bopp & Lady, 1991; Farmer, 1993; Moosa & Al-Loughani, 1994; Foster, 1996).
However, of greatest relevance are papers such as Chen et al. (1986), Hamao (1989), Al-Mudhaf and Goodwin (1993), Kaneko and Lee (1995) and Jones and Kaul (1996), which tested whether an oil price factor constitutes a systematic influence in the determination of prices in the equity markets of the U.S., Canada, Japan, and the U.K. Energy prices in general, and oil prices in particular, are likely to have an important potential impact on the costs of factor inputs for many companies. Specifi-cally, we expect the potential for a negative oil price sensitivity to be greatest in industries with a relatively high proportion of their costs devoted to oil-based inputs, such as Transport. However, the detection of any impact (either direct or indirect) is complicated by the ability of firms to pass on their sensitivity to oil price changes to customers through changing goods prices or by the extent to which firms hedge against oil price risk.
In their trail-blazing paper, Chen et al. (1986) provided a test of a multi-factor asset pricing model using innovations in a set of macroeconomic variables. This analysis included the possibility that a return series derived from oil prices could constitute an economic pricing factor. Generally, Chen et al. found no evidence to suggest that such a factor exists in their sample of U.S. equities. Hamao (1989) re-applied the Chen et al. approach to a set of Japanese equity data and confirmed the result for the oil price change factor. In contrast, Kaneko and Lee (1995) found that the oil price change factor was important in a more recent sample of Japanese equity data. Kaneko and Lee attribute this divergence in results to method and data differences between studies. In a study concerning predictability and time-varying risk across world equity markets, Ferson and Harvey (1995) reject the hypothesis that a risk variable based on oil price changes is equal across their sample of 18 countries. On the basis of rejecting this inequality hypothesis, they include the oil price risk variable as a separate factor in their asset pricing analysis. Finally, Jones and Kaul (1996), in perhaps the most comprehensive study, analyze the impact of oil price changes in Canada, Japan, the U.K., and the U.S. Their findings indicate that oil price changes have a detrimental effect on output and real stock returns in all four countries. Moreover, the magnitude of the impact is substantially different across the four markets.
The issue of asset pricing in general in relation to equities is controversial. The development of alternative multi-factor models, such as the arbitrage pricing theory (APT), is a response to the failure of traditional models, such as the capital asset pricing model (CAPM), to adequately explain cross-sectional variation in equity returns. More recently, research has again cast doubt on the CAPM (e.g. Fama & French 1992,
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1996a, 1996b). Within this context, researchers have sought to examine the sensitivity of equity returns to economic factors.
For example, Al-Mudhaf and Goodwin (1993) investigated a two-factor version of the APT, using a market and an oil price change factor. Based on a sample of 29 NYSE-listed oil companies covering the period 1970–1978, they found that the oil price risk premium was highly unstable. Specifically, in the period immediately following the OPEC oil price shock of 1973, equity returns of domestic oil producers and large multinationals were associated with a significantex ante oil risk premia. While their investigation of the asset pricing issue in the context of a small sample of oil companies served the narrow purpose of their study, it leaves open the question of how pervasive the oil price factor is across all sectors. Similarly, Jones and Kaul (1996) use an APT-type model in which fundamental variables are augmented by an oil price change variable.
This article follows in the spirit of this previous work. We do not propose to conduct a full-scale asset pricing test. Rather, we focus on the well-known market model and augment it through the addition of an oil price change factor. Our purpose is to establish whether an oil price change factor exhibits a systematic influence on equity returns over and above the influence of market returns. The focus on Australian equities is important given the limited international evidence in this area and the unique characteristics of the Australian environment. The large geographical size of Australia combined with a diverse and small population base and relative isolation from the rest of the world means that oil price changes are likely to have important consequences for many industries.
The analysis by industry is important for two reasons. First, it extends on previous work and follows in the spirit of Fama and French (1997), who find substantial differences in factor sensitivities across U.S. industries. Industries are not homoge-neous, and different factors can have different industry influences. An understanding of how certain economic factors impact on equity returns is important for management in those industries.
Second, the importance of recognizing differential industry effects is recognized in economic theory through the work of Gregory (1976), Corden (1984), and others on the so-called “Dutch disease.” In this analysis, the emergence of a new export sector may result from a boom in mineral or energy prices shifting the foreign exchange supply curve to the right. The resultant excess foreign exchange supply induces a contraction in the outputs of other competing industry sectors. Foreign exchange intervention in such circumstances through devaluation leads to either higher inflation or an accumulation of foreign exchange reserves. The model highlights the sectoral dependence in an economy and how the price of output of each sector relative to that of other sectors determines the allocation of resources. The Dutch disease is often cited in the context of mineral and energy booms, such as the natural gas discoveries by the Dutch in the 1960s, the North Sea oil discovery in Britain in the 1970s, and the various mineral booms in Australia as in the late 1960s. The focus on Australia is particularly relevant in this context given its large natural resources sector. This article initially conducts a study to investigate the sensitivity of Australian
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industry equity returns to an oil price factor. The paper is organized as follows. Section 2 discusses the importance of oil across the different Australian economic sectors. Section 3 outlines the data and research design, and the results are presented in section 4. Section 5 briefly discusses results from some sensitivity and further analysis. The final section presents the summary and concluding remarks.
2. Oil and the Australian economy
Australia has virtually been self-sufficient in petroleum products over the last 20 or more years. Despite this fact, Australia has engaged in import and export activity of petroleum products. Generally, during the 1980s Australia was a net importer, whereas during the 1990s, Australia has been a net exporter.1However, the net external
trade has typically been less than 2% of domestic consumption.
Given Australia’s vast geographic size, relatively small and diverse population, and its remoteness, the costs of transportation and freight potentially constitute a major component of the costs of many Australian companies, and the price of oil is likely to have an impact on these costs. Conversely, some Australian industries derive considerable revenue from oil and oil-related products and hence changes in oil prices will affect the profitability of these industries.
It is difficult to formulate predicted signs and relative magnitudes across specific industries of oil factor sensitivities. Industries with a relatively high proportion of their costs devoted to oil-based inputs, such as Transport, are expected to have a negative sensitivity. Conversely, in the absence of offsetting effects, we would expect a positive oil return sensitivity in oil and oil-related industries, in which oil directly impacts the revenue side of the income statement. However, in general, the impact of oil price changes on equity prices will depend on the ability of firms to pass on the effect to customers through changing goods prices.
Moreover, firms can protect themselves against adverse movements in oil pricing through hedging using derivative instruments. For instance, airline companies are likely to enter into energy futures or longer-term oil delivery commitments. However, the extent to which hedging occurs will make the sensitivity to oil price changes harder to detect and bias against significant findings. Furthermore, given the growth in derivative products and the improved understanding of risk management, we expect hedging practices to have become more common in recent years and hence the sensitiv-ity to oil price changes, if any, will have weakened over time. But ultimately, the extent of an “extra-market” sensitivity to oil price returns is an empirical matter.
In an attempt to form predictions about the sign of the oil factor sensitivity, we first group firms into industries following the Australian Stock Exchange (ASX) Industry Classification Report (1997). We initially hypothesize that there are four industries in which oil price changes are expected to have a net impact on revenue side.2These industries are
1. Gold (sub-industry code 14: Gold, Oil). 2. Solid Fuels (sub-industry code 36: Coal, Oil).
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3. Oil and Gas (sub-industry code 41: producers, sub-industry code 42: explorers, sub-industry code 43: investors and sub-industry code 44: distribution).
4. Diversified Resources (sub-industry code 52: Oil, Steel, Mining, sub-industry code 55: Coal, Gold and Oil and sub-industry code 56: Oil, Gold, Investment). Conversely, in the absence of offsetting effects, we expect a negative oil return sensitivity in the non–oil-related industries, wherein oil price changes directly impact on costs. Specifically, we expect the potential for a negative oil-price sensitivity is greatest in industries with a relatively high proportion of their costs devoted to oil-based inputs, such as Transport.3 To assess these effects, we have consulted the
Australian Input-Output Tables for 1993–1994, and the relevant details are reported in Table 1.
Table 1 reveals that the Australian Bureau of Statistics broadly classifies Australian industries into 35 different sectors (column 2). Unfortunately, this scheme does not readily translate to the industry classification system employed by the Australian Stock Exchange (ASX), which uses 24 categories, for which we have available equity price data.4The final column of Table 1 is an attempt to link the ASX industries to
their ABS industry counterparts. In some cases, the match is quite tight; for example, ABS industry 12 and ASX industry 10 are both labeled “Chemicals.” In other cases, the relationship seems less than ideal, or at least affected by the mixing of other areas of activity. For example, ABS 7 Textiles is matched with ASX 22 Miscellaneous Industrials on the basis that ASX sub-industry 223 is labeled “Textiles” (ASX, 1997, p. A2). However, a problem arises with this match because ASX industry 22 is a “Miscellaneous” industry as it also contains Automotive (222), Household Durables (224), and other sub-industries. Consequently, the matching of ABS and ASX indus-tries provided in Table 1 needs to be treated with due caution.
The major aim of Table 1 is, using the extracted Input-Output Table data, to provide an indication of the differential importance of oil prices on the costs of Australian industries. These data are used to calculate Direct Requirement Coefficients (DRC) for the ABS industries. According to the ABS Australian National Accounts Input-Output Tables 1993–1994 (p. 8), the DRC are obtained by calculating inputs as a percentage of the output of an industry and can be used for estimating the input requirements for any given output of that industry. To make the DRC information more easily interpretable across industries, we report in the second-to-last column of Table 1 the relative DRC (RDRC), which is calculated as the ratio of a particular industry’s DRC to the average DRC across all industries. For example, the Transport Industry (ABS 26 and ASX 14) has a high RDRC, at almost five times the economy-wide average. Perhaps surprisingly, the maximum value of the RDRC is in the ABS 2 Forestry and Fishing industry (ASX 12 Paper and Packaging). There are five other ABS industry classifications for which the RDRC value exceeds unity. These are ABS 1 Agriculture and Hunting (ASX 22 Miscellaneous Industrials), ABS 3 Mining (ASX 2 Other Metals), ABS 12 Chemicals (ASX 10 Chemicals), ABS 15 Basic Metals and Products (ASX 2 Other Metals), and ABS 35 Personal and Other Services (ASX 21 Miscellaneous Services). For these industries, other things being equal, we may predict a negative sensitivity to the oil price factor. However, the Miscellaneous Services
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Table 1
Direct Requirement Coefficients across Australian Industry Classifications
Petroleum Direct
& Coal Australian Requirement
Products Production Coefficient Relative
ABS Industry Classificationa $AUDm $AUDm (DRC)b DRCc ASX Industrie(s) (ASX Industry number)
1 Agriculture; Hunting 632.4 22,151.6 0.02855 2.90609 Miscellaneous Industrials (22)
2 Forestry & Fishing 171.5 2,725.7 0.06292 6.40484 Paper & Packaging (12)
3 Mining 552.6 31,047.2 0.01780 1.81180 Other Metals (2)
4 Meat & Dairy Products 62.3 15,386.7 0.00405 0.41216 Miscellaneous Industrials (22)
5 Other Food Products 131.0 15,645.4 0.00837 0.85233 Food & Household (9)
6 Beverages & Tobacco Products 34.9 6,222.3 0.00561 0.57095 Alcohol & Tobacco (8);
Entrepreneurial Investors (18)
7 Textiles 8.6 4,164.8 0.00206 0.21020 Miscellaneous Industrials (22)
8 Clothing & Footwear 5.2 5,426.5 0.00096 0.09755 Retail (13)
9 Wood & Wood Products 33.1 5,116.8 0.00647 0.65849 Building Materials (7);
Diversified Industrial (23)
10 Paper, Printing & Publishing 76.1 15,300.7 0.00497 0.50629 Paper & Packaging (12); Media (15)
11 Petroleum & Coal Products 220.7 10,373.3 0.02128 2.16575 Oil & Gas (4)
12 Chemicals 169.4 13,139.3 0.01289 1.31239 Chemicals (10)
13 Rubber & Plastic Products 15.6 6,123.6 0.00255 0.25932 Paper & Packaging (12)
14 Non-Metallic Mineral Products 76.3 7,608.4 0.01003 1.02083 Diversified Resources (5)
15 Basic Metals & Products 372.8 18,918.7 0.01971 2.00589 Other Metals (2)
16 Fabricated Metal Products 32.9 11,748.1 0.00280 0.28507 Miscellaneous Industrials (22)
17 Transport Equipment 27.1 14,393.9 0.00188 0.19165
18 Other Machinery & Equipment 23.5 16,014.3 0.00147 0.14938 Engineering (11)
19 Miscellaneous Manufacturing 11.6 5,282.5 0.00220 0.22353 Engineering (11); Retail (13)
20 Electricity, Gas & Water 229.4 25,216.8 0.00910 0.92603
21 Construction 215.0 48,560.3 0.00443 0.45069 Developers & Contractors (6)
22 Wholesale Trade 216.0 34,033.2 0.00635 0.64606
23 Retail Trade 182.9 34,953.7 0.00523 0.53265 Retail (13)
24 Repairs 76.2 13,178.8 0.00578 0.58857
25 Accommodation, Cafes &
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Table 1 (continued)
Petroleum Direct
& Coal Australian Requirement
Products Production Coefficient Relative
ABS Industry Classificationa $AUDm $AUDm (DRC)b DRCc ASX Industrie(s) (ASX Industry number)
26 Transport & Storage 2,055.0 41,928.8 0.04901 4.98909 Transport (14)
27 Communication Services 121.0 15,522.1 0.00780 0.79352 Media (15)
28 Finance & Insurance 31.9 40,073.5 0.00080 0.08103 Banks & Finance (16); Insurance (17)
29 Ownership of Dwellings 16.7 46,164.8 0.00036 0.03682
30 Property & Business Services 410.3 54,245.6 0.00756 0.76994 Property Trusts (20)
31 Government Administration 147.7 37,682.5 0.00392 0.39899
32 Education 0.7 22,631.8 0.00003 0.00315
33 Health & Community Services 155.6 33,768.1 0.00461 0.46906 Miscellaneous Services (21)
34 Cultural & Recreational
Services 40.9 13,866.2 0.00295 0.30025 Tourism & Leisure (24)
35 Personal & Other Services 232.8 12,844.3 0.01812 1.84499 Miscellaneous Services (21)
aAustralian Bureau of Statistics (ABS) classification contained in the Australian National Accounts Input-Output Tables.
bDirect Requirement Coefficient (DRC) for each industry is calculated as the ratio of ($ Petroleum and Coal Products)/($ Australian
Pro-duction).
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(ASX 21) industry provides a mixed signal; while it has a large RDRC, this effect will be countered by the positive effect via the inclusion of the Petroleum Products sub-industry (ASX 213). A similar argument applies in the case of the Miscellaneous Industrials (ASX 22) because it includes agricultural products, textiles, and fabricated metal products. Moreover, we are skeptical of any effect revealing itself in relation to the Other Metals industry (ASX 2), as it too provides a mixed signal. This industry has a high RDRC but is also likely to experience an indirect positive sensitivity because oil prices are generally correlated with metals prices.
It is also useful to consider the industries that have a value for the RDRC that is extremely low. One notable case is the ASX 13 Retail industry, which seems to be consistently low as it relates to ABS 8 (Clothing and Footwear), ABS 19 (Miscellaneous Manufacturing), and ABS 23 (Retail Trade). Another consistent case of low RDRC is the ASX 11 Engineering industry in relating to ABS 18 (Other Machinery and Equipment) and ABS 19 (Miscellaneous Manufacturing). Accordingly, other things being equal we predict a negligible role for the oil price factor for these industries. The remaining industries also appear to generally indicate a negligible role for the oil price factor.
This analysis highlights the importance of conducting the test on industry sectors. The hypothesized differential impact means that aggregate market analysis (as pre-viously conducted in papers such as Jones & Kaul, 1996) may mask industry effects. In summary, we predict a positive sensitivity in four oil and oil-related industries (ASX 1, 3, 4, and 5) and a negative sensitivity in four other industries (ASX 2, 10, 12, and 14). However, the ability of firms to pass on oil price changes to their customers and the extent of hedging activity mitigate against finding significant results.
3. Data and research design
3.1. Extra-market oil return sensitivities
Consider a two-factor model of the form:5
Rit 5 ai1 biRmt1 giOILR(AUD)t1eit (1)
where Rit is the return on the ith asset or portfolio in month t, Rmt is the return on
the market index in montht, and OILR(AUD)tis the return on the oil price in month
texpressed in Australian dollars.
Following Merton’s (1973) intertemporal CAPM, investors are concerned about unfavorable shifts in the investment opportunity set over time, in addition to their diversification needs. This feature gives rise to hedging activity, whereby investors are assumed to be able to construct portfolios that protect against uncertainties in state variables. Oil price risk may be viewed as one such case in which investors seek to hedge. A further related dimension of risk that is often discussed in a hedging context is foreign exchange risk.6 This is particularly relevant in the context of oil
because it is priced in an international market denominated in U.S. dollars. As a consequence, we can think of the domestic currency oil price factor as having two
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components, namely (a) a pure oil price factor (denominated in U.S. dollars) and (b) an exchange rate factor. Accordingly, consider the following expression for the oil return in Australian dollars:
OILR(AUD)t5ln[OIL(AUD)t/OIL(AUD)t21] (2)
OIL(AUD)t5 OIL(USD)t/XR(AUD/USD)t (3)
where OIL(AUD)t is the oil price in month t, expressed in Australian dollars,
OIL(USD)tis the oil price in montht, expressed in US dollars, and XR(AUD/USD)t
is the AUD/USD exchange rate att, namely the value of $1 Australian expressed in U.S. dollars.
Upon substitution of Eq. 3 into Eq. 2, we get:
OILR(AUD)t5ln
3
OIL(USD)t* XR(AUD/USD)t21
OIL(USD)t21* XR(AUD/USD)t
4
5ln
3
OIL(USD)tOIL(USD)t21
4
1ln
3
XR(AUD/USD)t21XR(AUD/USD)t
4
5OILR(USD)t1 ln
3
XR(USD/AUD)t
XR(USD/AUD)t21
4
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where XR(USD/AUD)tis the USD/AUD exchange rate at t, namely the value of $1
U.S. expressed in Australian dollars, and OILR(USD)tis the oil price return in month
t, expressed in U.S. dollars.
Furthermore, noting that the return from holding U.S. dollars, XRt, is defined as
follows:
XRt5ln
3
XR(USD/AUD)t
XR(USD/AUD)t21
4
, (5)
the final oil return decomposition becomes:
OILR(AUD)t5OILR(USD)t1 XRt (6)
A comparison of Eqs. 1 and 6 suggests that the imposed restriction provides a method of testing the validity of the specification in Eq. 1. The validity of this restriction can be tested by the following model:
Rit 5 ai1 biRmt1 giOILR(USD)t1 diXRt1eit (7)
It should be noted thatganddcoefficients in Eq. 7 will be equal only if the exchange rate has absolutely no influence on returns, except for its impact on AUD-denominated oil prices. If the coefficients are not equal, then Eq. 1 is mis-specified.
3.2. Data
The data used are continuously compounded monthly returns over the period July 1983 to March 1996, on 24 Australian industry portfolios based on the ASX industry groupings. Returns are calculated from the Price Relatives File of the Centre for
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Table 2
Estimation of the Market Model Augmented by an Oil Factor: 1983:07 to 1996:03
ASX Industry ai bi gi wia R2 DW
1. Gold 20.0093 1.3977* 0.0759 0.2098* 0.571 1.939
(21.23) (14.19) (1.21) (2.62)
2. Other Metals 20.0069 1.3031* 20.0358 — 0.700 1.778
(21.65) (18.50) (20.84)
3. Solid Fuels 0.0031 0.6648* 20.0117 — 0.427 1.732
(0.81) (10.47) (20.31)
4. Oil and Gas 20.0048 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 20.0018 1.0628* 20.0249 — 0.797 1.927
Contractors (20.67) (24.01) (20.93)
7. Building Materials 0.0011 0.8157* 20.0033 — 0.767 2.056
(0.50) (22.07) (20.15)
8. Alcohol and 0.0065 0.8649* 20.0197 0.1740 0.498 1.954
Tobacco (1.25) (11.99) (20.43) (2.14)
9. Food and 0.0053 0.8029* 20.0706 — 0.693 1.929
Household Goods (1.96) (17.82) (22.60)
10. Chemicals 0.0082* 0.8017* 20.0424 — 0.619 2.052
(2.63) (15.31) (21.34)
11. Engineering 0.0014 0.7511* 20.0196 — 0.629 1.890
(0.47) (15.78) (20.68)
12. Paper and Packaging 0.0029 0.7772* 20.0884* — 0.678 1.994
(1.06) (17.04) (23.21)
13. Retail 0.0006 0.8581* 20.0439 — 0.696 1.737
(0.23) (18.20) (21.54)
14. Transport 20.0004 1.1098* 20.0959* — 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 about20.10% measured in Australian dollars, compared to about20.18% when measured in U.S. dollars. A time series analysis of the oil return reveals a strong predictable structure, as reflected
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ASX Industry ai bi gi wia R2 DW
15. Media 0.0068 1.1810* 20.0850 — 0.522 1.720
(1.20) (12.48) (21.49)
16. Banks 0.0059 0.7691* 20.0873* — 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 20.0129 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* 20.0051 — 0.538 1.897
(3.07) (13.10) (20.25)
21. Miscellaneous 0.0078 0.6336* 20.0682 — 0.438 1.814
Services (2.15) (10.39) (21.85)
22. Miscellaneous 20.0016 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* 20.0229 — 0.823 2.000
Industrials (0.82) (26.10) (20.99)
24. Tourism and Leisure 0.0064 0.5746* 20.0052 — 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:
Rit5 ai1 biRmt1 giOILR(AUD)t1eit (1) whereRitis the return on theith asset or portfolio in montht,Rmtis the return on the market index in monthtand OILR(AUD)tis the return on the oil price in monthtexpressed in Australian dollars.
* Coefficient estimate is significantly different from zero at the 1% level (t statistics in parentheses). aThe 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.7However, the empirical
analysis is qualitatively robust to whether the raw or the adjusted return series is used.8Accordingly, 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
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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 gi and di 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) H0:gi 5 diand (2) H0: gi5 di5 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
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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.10Furthermore, 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
The analyses in Tables 2 and 3 cover the full sample period of 1983–1996. As such, their length may obscure possible changes in sensitivity to the oil price factor across industries over time. Therefore, to investigate for potential changes in the sensitivity to the oil price factor over time, two sub-periods of (1) July 1983 to October 1989 and (2) November 1989 to March 1996 are examined. The analysis involves augmenting Eq. 1 with dichotomous dummy variables on both the intercept and slope coefficients. The first sub-period produces five industries with a significant oil price sensitivity using the model in Eq. 1 consistent with the full period results reported in Table 2. Four of these industries are consistent with our initial predictions, while we place little emphasis on the Banks industry result given the previous argument that the model is mis-specified in this industry.
In the second sub-period, the two industries with a significant positive sensitivity (Oil and Gas, and Diversified Resources) remain so. However, the industries with a significant negative sensitivity in the first sub-period lose their significance in the second sub-period. This result is consistent with an argument that firms with a signifi-cant negative sensitivity either can effectively pass on price (negative) changes to their customers or have become increasingly engaged in effective hedging activities.
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Table 3
Estimation of the Market Model Augmented by an Oil and Exchange Rate Factor: 1983:07 to 1996:03 Wald Testa Wald Testb gi di H0:gi5 di H0:gi5 di50
ASX Industry (t statistic) (t statistic) (p value) (p value)
1. Gold 0.0795 20.0014 0.166 1.600
(1.24) (20.01) (0.68) (0.45)
2. Other Metals 20.0339 20.0719 0.073 0.776
(20.78) (20.51) (0.79) (0.68)
3. Solid Fuels 20.0197 0.1418 1.640 1.734
(20.51) (1.13) (0.20) (0.42)
4. Oil and Gas 0.2454* 0.0285 4.542 58.021*
(7.55) (0.28) (0.03) (0.00)
5. Diversified 0.1309* 0.0642 0.445 18.105*
Resources (4.25) (0.64) (0.50) (0.00)
6. Developers and 20.0231 20.0589 0.165 1.024
Contractors (20.85) (20.67) (0.68) (0.60)
7. Building 0.0056 20.1728 6.088* 6.110
Materials (0.25) (22.40) (0.01) (0.05)
8. Alcohol and 20.0136 20.1352 0.703 0.889
Tobacco (20.29) (20.93) (0.40) (0.64)
9. Food and 20.0735* 20.0153 0.420 7.132
Household Goods (22.66) (20.17) (0.52) (0.03)
10. Chemicals 20.0369 20.1482 1.141 2.940
(21.15) (21.43) (0.29) (0.230)
11. Engineering 20.0061 20.2785* 8.690* 9.178*
(20.22) (23.02) (0.01) (0.01)
12. Paper and 20.0939* 0.0156 1.460 11.794*
Packaging (23.37) (0.17) (0.23) (0.01)
13. Retail 20.0342 20.2300 4.457 6.889
(21.20) (22.49) (0.04) (0.03)
14. Transport 20.0908* 20.1945 0.893 9.180*
(22.69) (21.78) (0.35) (0.01)
continued
Formally, there is only one case (Miscellaneous Services) in which the Wald test rejects the null hypothesis of equality of the coefficient across sub-periods. As such, there appears to be little evidence of changing sensitivities across time, implying that oil price sensitivity is a long-term phenomenon, except perhaps for those industries with a significant negative sensitivity.
A further issue is the potential effect of the stock market crash of October 1987. We conduct some sensitivity analysis to assess how robust the findings are to this extreme market movement. Interestingly, the estimation of the oil price sensitivities is qualitatively unaffected, and importantly, the conclusions drawn based upon them are robust to the treatment of the crash.
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R.W. Faff, T.J. Brailsford / Journal of Energy Finance and Development 4 (1999) 69–87 83 Table 3 (continued)
Wald Testa Wald Testb gi di H0:gi5 di H0:gi5 di50
ASX Industry (t statistic) (t statistic) (p value) (p value)
15. Media 20.0947 0.1009 1.081 3.296
(21.64) (0.54) (0.30) (0.19)
16. Banks 20.0740 20.3416* 6.149* 13.246*
(22.23) (23.17) (0.01) (0.01)
17. Insurance 0.0928 0.0266 0.225 4.158
(2.04) (0.19) (0.64) (0.13)
18. Entrepreneurial 20.0052 0.1429 0.595 0.596
Investors (20.09) (0.75) (0.44) (0.74)
19. Investment and 0.0576 20.0412 1.346 5.189
Financial Services (2.12) (20.48) (0.25) (0.08)
20. Property Trusts 20.0089 0.0673 1.279 1.341
(20.43) (1.00) (0.26) (0.51)
21. Miscellaneous 20.0794 0.1474 3.551 7.032
Services (2.14) (1.23) (0.06) (0.03)
22. Miscellaneous 0.0128 20.0056 0.044 0.223
Industrials (0.45) (20.06) (0.83) (0.90)
23. Diversified 20.0236 20.0094 0.035 1.015
Industrials (21.01) (20.12) (0.85) (0.60)
24. Tourism and 20.0016 20.0756 0.424 0.447
Leisure (20.05) (20.67) (0.52) (0.80)
Number of Significant
Test Statistics 5 2 3 6
This table reports the output from the following regression:
Rit5 ai1 biRmt1 giOILR(USD)t1 diXRt1eit (7) whereRitis the return on theith asset or portfolio in montht,Rmtis the return on the market index in monthtand OILR(USD)tis the oil price return in montht, expressed in U.S. dollars, and XRtis the return from holding U.S. dollars.
* Statistic is significantly different from zero at the 1% level.
aWald test statistic has a chi square distribution with 1 degree of freedom. The p value is given in parentheses.
bWald test statistic has a chi square distribution with 2 degrees of freedom. The p value is given in parentheses.
and the oil price factors.11 To perform these tests, estimation of the system of
zero-intercept two-factor models in excess returns form is undertaken. Furthermore, the tests are performed in two variations: (1) using returns on a value-weighted domestic market index, and (2) using returns on a global market index. In a system of excess return models, the null hypothesis is that the intercept terms are jointly equal to zero.12The overwhelming result is that the null model cannot be rejected. This finding
is robust to (1) the choice of market portfolio proxy, (2) performing sub-period analysis, (3) performing separate industry sector (resource or industrial) analysis, and (4) the crash of October 1987. Hence, we accept the restriction placed on the model.13
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84 R.W. Faff, T.J. Brailsford / Journal of Energy Finance and Development 4 (1999) 69–87 6. Conclusion
A great deal of research over recent years has been directed toward identifying potential economic factors that may serve as proxies for the fundamental pervasive risks that play a role in the pricing of equity returns. This paper extends this line of research by focusing upon an oil price factor.
The primary objective was to investigate the sensitivity of Australian industry equity returns to an oil price factor, over and above the sensitivity to market returns. Energy prices in general and oil prices in particular are likely to have an important impact on the costs of many companies. Having established some degree of sensitivity, the second related objective was to then conduct a test of the “oil-market” two-factor APT.
Using data on energy requirements, we formed predictions about the sensitivity to an oil price factor in certain industries. The results generally confirm these predictions with a significant positive sensitivity observed for the Oil and Gas, and Diversified Resources industries and a significant negative sensitivity observed for the Paper and Packaging, and Transport industries. A significant negative sensitivity was also observed for the Banking industry although we argue that this is due to model mis-specification. The sensitivities appear to be a long-term phenomenon, although we have hypothesized that some firms may have found ways to pass on oil price changes to their customers or found effective hedging mechanisms. We find the results to be generally robust in sensitivity analysis.
These results add weight to the argument that industries are not homogeneous and that different factors can affect industry returns in different ways. Indirectly, this evidence could be construed as support for the model of the “Dutch disease.” Notably, the establishment of significant sensitivities in some industries indicates that manage-ment in these industries needs to be aware of the consequent risks.
Acknowledgments
The authors wish to thank seminar participants at the Australian National University and the University of Strathclyde, Tony Noon at the Australian Petroleum Production and Exploration Association Limited for his help in locating background statistics on the oil industry, Robert Albon and Mark Stewart for their helpful comments, and Sveta Risman for her very capable research assistance. The paper has also benefitted from the comments of participants at the 1998 International Conference in Lille France and an anonymous referee. Part of this research was completed while the first author was visiting the Department of Accounting and Finance at the University of Strathclyde.
Notes
1. Over the period 1983–1996, Australian imports of refined petroleum products averaged 2,890 million liters per year, while exports averaged 3,007 million liters per year. Over the same period, annual domestic production averaged 39,705 million liters (ABARE, 1996, p. 314).
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2. The Miscellaneous Services industry comprises a sub-industry code 213, Petro-leum Products, such that we may initially expect a positive sensitivity to be observed. However, any such sensitivity of the Miscellaneous Services industry is likely to be countered by the composition of firms in the category. As detailed later in the text, this industry contains sub-industries that are likely to experience both positive and negative sensitivities.
3. The discussion here assumes that wholesale price movements translate to the world price of crude oil. However, different industries face different tariff structures, meaning that changes in the price of crude oil may not translate to industry price on a one-to-one basis.
4. The ASX partitions the stock market into 24 industry categories, of which five (ASX Industries 1–5) are in the mining and resources sector and 19 (ASX Industries 6–24) are in the industrial sector.
5. The equations are estimated using ordinary least squares. Cases in which signifi-cant autocorrelations are found are re-estimated using the generalized least squares Cochrane-Orcutt adjustment (Cochrane and Orcutt, 1949).
6. See Loudon (1993) and Khoo (1994) for an examination of the pure foreign exchange exposure of Australian companies.
7. The Box-Ljung Q-statistic for 36 lags of the AR(1) series of innovations is 34.242, having a p value of 0.552.
8. The finding of insensitivity between the raw or unexpected oil return series is consistent with the literature that examines the issue of interest rate risk in the banking sector. Typically, these studies also find that qualitatively it does not matter whether raw or unexpected interest rate returns are used. See for example Flannery and James (1984), Bae (1990), and Madura and Zarruk (1995). 9. Given the empirical nature of the tests, we use 1% as a conservative significance
level.
10. In the cases of a significant sensitivity, the model in Eq. 1 was re-run using the USD oil price factor rather than the AUD oil price factor as the variable augmented to the market model. In all cases, the USD oil price sensitivity is of very similar magnitude and statistical significance to the AUD oil price sensitivity reported in Table 2.
11. For a discussion of GMM tests in an asset pricing context, see MacKinlay and Richardson (1991). Details of these tests are available from the authors upon request.
12. This zero-intercept hypothesis follows a widely applied approach in the asset pricing literature, having its roots in the testing of the Sharpe-Lintner CAPM. 13. Detailed results of the analysis described in this section are available on request.
References
ABARE.(December, 1996).Australian Commodity Statistics.Canberra: Commonwealth of Australia. Al-Mudhaf, A., & Goodwin, T. H. (1993). Oil shocks and oil stocks: Evidence from the 1970s.Applied
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← →
86 R.W. Faff, T.J. Brailsford / Journal of Energy Finance and Development 4 (1999) 69–87
Australian Bureau of Statistics. (1993–1994).Australian National Accounts Input-Output Tables. Can-berra: Australian Bureau of Statistics.
Australian Stock Exchange. (January, 1997).Industry Classification Report.
Bae, S. (1990). Interest rate changes and common stock returns of financial institutions: revisited.Journal of Financial Research 13, 71–79.
Bopp, A. E., & Lady, G. M. (1991). A comparison of petroleum futures versus spot prices as predictors of prices in the future.Energy Economics 13, 274–281.
Chen, N. F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market.Journal of Business 59, 383–403.
Cochrane, D., & Orcutt, G. H. (1949). Application of least squares regressions to relationships containing autocorrelated error terms.Journal of the American Statistical Association 44, 3.
Corden, W. M. (1984). Booming sector and Dutch disease economics: survey and consolidation.Oxford Economic Papers 36, 359–380.
Crain, J. L., & Jamal, A. M. M. (1991). The valuation of natural resources: evidence from oil and gas pure plays.Journal of Business Finance and Accounting 18, 755–761.
Fama, E. F., & French, K. R. (1992). The cross-section of expected stock returns.Journal of Finance 47, 427–465.
Fama, E. F., & French, K. R. (1996a). Multifactor explanations of asset pricing anomalies.Journal of Finance 51, 55–84.
Fama, E. F., & French, K. R. (1996b). The CAPM is wanted dead or alive.Journal of Finance 51, 1947–1958.
Fama, E. F.,& French, K. R. (1997). Industry costs of equity.Journal of Financial Economics 43, 153–193. Farmer, R. D. (1993). Forward markets and changes in macroeconomic performance: the case of oil.
Journal of Macroeconomics 15, 521–552.
Ferson, W., & Harvey, C. R. (1995). Predictability and time-varying risk in world equity markets.Research in Finance 13, 25–88.
Flannery, M. J., & James, C. M. (1984). The effect of interest rate changes on the common stock returns of financial institutions.Journal of Finance 39, 1141–1153.
Foster, A. (1996). Price discovery in oil markets: a time-varying analysis of the 1990–91 gulf conflict.
Energy Economics 18, 231–246.
Gregory, R. G. (1976). Some implications of the growth of the mineral sector.Australian Journal of Agricultural Economics 20, 71–91.
Hamao, Y. (1989). An empirical examination of the arbitrage pricing theory: using Japanese data.Japan and the World Economy 1, 45–61.
Helliwell, J., Sturm, P., Jarrett, P., & Salou, G. (1986). The supply side in the OECD’s macroeconomic model.OECD Economic Studies 6, 75–131.
Jones, C., & Kaul, G. (1996). Oil and the stock markets.Journal of Finance 51, 463–491.
Kaneko, T., & Lee, B. S. (1995). Relative importance of economic factors in the U.S. and Japanese stock markets.Journal of the Japanese and International Economies 9, 290–307.
Khoo, A. (1994). Estimation of foreign exchange exposure: an application to mining companies in Australia.Journal of International Money and Finance 13, 342–363.
Loudon, G. (1993). The foreign exchange operating exposure of Australian stocks. Accounting and Finance 32, 19–32.
MacKinlay, A. C., & Richardson, M. P. (1991). Using generalized method of moments to test mean-variance efficiency.Journal of Finance 46, 511–527.
Madura, J., & E. R. Zarruk. (1995). Bank exposure to interest rate risk: a global perspective.Journal of Financial Research 18, 1–13.
Malliaris, A. G., & Urrutia, J. L. (1995). The impact of the Persian gulf crisis on national equity markets.
Advances in International Banking and Finance 1, 43–65.
Merton, R. (1973). An intertemporal capital asset pricing model.Econometrica 41, 867–887.
Miller, M. H., & Upton C. W. (1985a). A test of the Hotelling valuation principle.Journal of Political Economy 93, 1–25.
(19)
← →
R.W. Faff, T.J. Brailsford / Journal of Energy Finance and Development 4 (1999) 69–87 87 Miller, M. H., & Upton, C. W. (1985b). The pricing of oil and gas: some further results. Journal of
Finance 40, 1009–1018.
Moosa, I. A., & Al-Loughani, N. E. (1994). Unbiasedness and time varying risk premia in the crude oil futures markets.Energy Economics 16, 99–105.
Strong, J. S. (1991). Using oil share portfolios to hedge oil price risk.Quarterly Review of Economics and Business 3, 48–63.
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Table 3
Estimation of the Market Model Augmented by an Oil and Exchange Rate Factor: 1983:07 to 1996:03 Wald Testa Wald Testb
gi di H0:gi5 di H0:gi5 di50
ASX Industry (t statistic) (t statistic) (p value) (p value)
1. Gold 0.0795 20.0014 0.166 1.600
(1.24) (20.01) (0.68) (0.45)
2. Other Metals 20.0339 20.0719 0.073 0.776
(20.78) (20.51) (0.79) (0.68)
3. Solid Fuels 20.0197 0.1418 1.640 1.734
(20.51) (1.13) (0.20) (0.42)
4. Oil and Gas 0.2454* 0.0285 4.542 58.021*
(7.55) (0.28) (0.03) (0.00)
5. Diversified 0.1309* 0.0642 0.445 18.105*
Resources (4.25) (0.64) (0.50) (0.00)
6. Developers and 20.0231 20.0589 0.165 1.024
Contractors (20.85) (20.67) (0.68) (0.60)
7. Building 0.0056 20.1728 6.088* 6.110
Materials (0.25) (22.40) (0.01) (0.05)
8. Alcohol and 20.0136 20.1352 0.703 0.889
Tobacco (20.29) (20.93) (0.40) (0.64)
9. Food and 20.0735* 20.0153 0.420 7.132
Household Goods (22.66) (20.17) (0.52) (0.03)
10. Chemicals 20.0369 20.1482 1.141 2.940
(21.15) (21.43) (0.29) (0.230)
11. Engineering 20.0061 20.2785* 8.690* 9.178*
(20.22) (23.02) (0.01) (0.01)
12. Paper and 20.0939* 0.0156 1.460 11.794*
Packaging (23.37) (0.17) (0.23) (0.01)
13. Retail 20.0342 20.2300 4.457 6.889
(21.20) (22.49) (0.04) (0.03)
14. Transport 20.0908* 20.1945 0.893 9.180*
(22.69) (21.78) (0.35) (0.01)
continued
Formally, there is only one case (Miscellaneous Services) in which the Wald test rejects the null hypothesis of equality of the coefficient across sub-periods. As such, there appears to be little evidence of changing sensitivities across time, implying that oil price sensitivity is a long-term phenomenon, except perhaps for those industries with a significant negative sensitivity.
A further issue is the potential effect of the stock market crash of October 1987. We conduct some sensitivity analysis to assess how robust the findings are to this extreme market movement. Interestingly, the estimation of the oil price sensitivities is qualitatively unaffected, and importantly, the conclusions drawn based upon them are robust to the treatment of the crash.
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R.W. Faff, T.J. Brailsford / Journal of Energy Finance and Development 4 (1999) 69–87 83 Table 3 (continued)
Wald Testa Wald Testb
gi di H0:gi5 di H0:gi5 di50
ASX Industry (t statistic) (t statistic) (p value) (p value)
15. Media 20.0947 0.1009 1.081 3.296
(21.64) (0.54) (0.30) (0.19)
16. Banks 20.0740 20.3416* 6.149* 13.246*
(22.23) (23.17) (0.01) (0.01)
17. Insurance 0.0928 0.0266 0.225 4.158
(2.04) (0.19) (0.64) (0.13)
18. Entrepreneurial 20.0052 0.1429 0.595 0.596
Investors (20.09) (0.75) (0.44) (0.74)
19. Investment and 0.0576 20.0412 1.346 5.189
Financial Services (2.12) (20.48) (0.25) (0.08)
20. Property Trusts 20.0089 0.0673 1.279 1.341
(20.43) (1.00) (0.26) (0.51)
21. Miscellaneous 20.0794 0.1474 3.551 7.032
Services (2.14) (1.23) (0.06) (0.03)
22. Miscellaneous 0.0128 20.0056 0.044 0.223
Industrials (0.45) (20.06) (0.83) (0.90)
23. Diversified 20.0236 20.0094 0.035 1.015
Industrials (21.01) (20.12) (0.85) (0.60)
24. Tourism and 20.0016 20.0756 0.424 0.447
Leisure (20.05) (20.67) (0.52) (0.80)
Number of Significant
Test Statistics 5 2 3 6
This table reports the output from the following regression:
Rit5 ai1 biRmt1 giOILR(USD)t1 diXRt1eit (7)
whereRitis the return on theith asset or portfolio in montht,Rmtis the return on the market index in
monthtand OILR(USD)tis the oil price return in montht, expressed in U.S. dollars, and XRtis the
return from holding U.S. dollars.
* Statistic is significantly different from zero at the 1% level.
aWald test statistic has a chi square distribution with 1 degree of freedom. The p value is given in parentheses.
bWald test statistic has a chi square distribution with 2 degrees of freedom. The p value is given in parentheses.
and the oil price factors.11 To perform these tests, estimation of the system of
zero-intercept two-factor models in excess returns form is undertaken. Furthermore, the tests are performed in two variations: (1) using returns on a value-weighted domestic market index, and (2) using returns on a global market index. In a system of excess return models, the null hypothesis is that the intercept terms are jointly equal to zero.12The overwhelming result is that the null model cannot be rejected. This finding
is robust to (1) the choice of market portfolio proxy, (2) performing sub-period analysis, (3) performing separate industry sector (resource or industrial) analysis, and (4) the crash of October 1987. Hence, we accept the restriction placed on the model.13
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6. Conclusion
A great deal of research over recent years has been directed toward identifying potential economic factors that may serve as proxies for the fundamental pervasive risks that play a role in the pricing of equity returns. This paper extends this line of research by focusing upon an oil price factor.
The primary objective was to investigate the sensitivity of Australian industry equity returns to an oil price factor, over and above the sensitivity to market returns. Energy prices in general and oil prices in particular are likely to have an important impact on the costs of many companies. Having established some degree of sensitivity, the second related objective was to then conduct a test of the “oil-market” two-factor APT.
Using data on energy requirements, we formed predictions about the sensitivity to an oil price factor in certain industries. The results generally confirm these predictions with a significant positive sensitivity observed for the Oil and Gas, and Diversified Resources industries and a significant negative sensitivity observed for the Paper and Packaging, and Transport industries. A significant negative sensitivity was also observed for the Banking industry although we argue that this is due to model mis-specification. The sensitivities appear to be a long-term phenomenon, although we have hypothesized that some firms may have found ways to pass on oil price changes to their customers or found effective hedging mechanisms. We find the results to be generally robust in sensitivity analysis.
These results add weight to the argument that industries are not homogeneous and that different factors can affect industry returns in different ways. Indirectly, this evidence could be construed as support for the model of the “Dutch disease.” Notably, the establishment of significant sensitivities in some industries indicates that manage-ment in these industries needs to be aware of the consequent risks.
Acknowledgments
The authors wish to thank seminar participants at the Australian National University and the University of Strathclyde, Tony Noon at the Australian Petroleum Production and Exploration Association Limited for his help in locating background statistics on the oil industry, Robert Albon and Mark Stewart for their helpful comments, and Sveta Risman for her very capable research assistance. The paper has also benefitted from the comments of participants at the 1998 International Conference in Lille France and an anonymous referee. Part of this research was completed while the first author was visiting the Department of Accounting and Finance at the University of Strathclyde.
Notes
1. Over the period 1983–1996, Australian imports of refined petroleum products averaged 2,890 million liters per year, while exports averaged 3,007 million liters per year. Over the same period, annual domestic production averaged 39,705 million liters (ABARE, 1996, p. 314).
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R.W. Faff, T.J. Brailsford / Journal of Energy Finance and Development 4 (1999) 69–87 85 2. The Miscellaneous Services industry comprises a sub-industry code 213, Petro-leum Products, such that we may initially expect a positive sensitivity to be observed. However, any such sensitivity of the Miscellaneous Services industry is likely to be countered by the composition of firms in the category. As detailed later in the text, this industry contains sub-industries that are likely to experience both positive and negative sensitivities.
3. The discussion here assumes that wholesale price movements translate to the world price of crude oil. However, different industries face different tariff structures, meaning that changes in the price of crude oil may not translate to industry price on a one-to-one basis.
4. The ASX partitions the stock market into 24 industry categories, of which five (ASX Industries 1–5) are in the mining and resources sector and 19 (ASX Industries 6–24) are in the industrial sector.
5. The equations are estimated using ordinary least squares. Cases in which signifi-cant autocorrelations are found are re-estimated using the generalized least squares Cochrane-Orcutt adjustment (Cochrane and Orcutt, 1949).
6. See Loudon (1993) and Khoo (1994) for an examination of the pure foreign exchange exposure of Australian companies.
7. The Box-Ljung Q-statistic for 36 lags of the AR(1) series of innovations is 34.242, having a p value of 0.552.
8. The finding of insensitivity between the raw or unexpected oil return series is consistent with the literature that examines the issue of interest rate risk in the banking sector. Typically, these studies also find that qualitatively it does not matter whether raw or unexpected interest rate returns are used. See for example Flannery and James (1984), Bae (1990), and Madura and Zarruk (1995). 9. Given the empirical nature of the tests, we use 1% as a conservative significance
level.
10. In the cases of a significant sensitivity, the model in Eq. 1 was re-run using the USD oil price factor rather than the AUD oil price factor as the variable augmented to the market model. In all cases, the USD oil price sensitivity is of very similar magnitude and statistical significance to the AUD oil price sensitivity reported in Table 2.
11. For a discussion of GMM tests in an asset pricing context, see MacKinlay and Richardson (1991). Details of these tests are available from the authors upon request.
12. This zero-intercept hypothesis follows a widely applied approach in the asset pricing literature, having its roots in the testing of the Sharpe-Lintner CAPM. 13. Detailed results of the analysis described in this section are available on request.
References
ABARE.(December, 1996).Australian Commodity Statistics.Canberra: Commonwealth of Australia. Al-Mudhaf, A., & Goodwin, T. H. (1993). Oil shocks and oil stocks: Evidence from the 1970s.Applied
(5)
Australian Bureau of Statistics. (1993–1994).Australian National Accounts Input-Output Tables. Can-berra: Australian Bureau of Statistics.
Australian Stock Exchange. (January, 1997).Industry Classification Report.
Bae, S. (1990). Interest rate changes and common stock returns of financial institutions: revisited.Journal of Financial Research 13, 71–79.
Bopp, A. E., & Lady, G. M. (1991). A comparison of petroleum futures versus spot prices as predictors of prices in the future.Energy Economics 13, 274–281.
Chen, N. F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market.Journal of Business 59, 383–403.
Cochrane, D., & Orcutt, G. H. (1949). Application of least squares regressions to relationships containing autocorrelated error terms.Journal of the American Statistical Association 44, 3.
Corden, W. M. (1984). Booming sector and Dutch disease economics: survey and consolidation.Oxford Economic Papers 36, 359–380.
Crain, J. L., & Jamal, A. M. M. (1991). The valuation of natural resources: evidence from oil and gas pure plays.Journal of Business Finance and Accounting 18, 755–761.
Fama, E. F., & French, K. R. (1992). The cross-section of expected stock returns.Journal of Finance 47, 427–465.
Fama, E. F., & French, K. R. (1996a). Multifactor explanations of asset pricing anomalies.Journal of Finance 51, 55–84.
Fama, E. F., & French, K. R. (1996b). The CAPM is wanted dead or alive.Journal of Finance 51, 1947–1958.
Fama, E. F.,& French, K. R. (1997). Industry costs of equity.Journal of Financial Economics 43, 153–193. Farmer, R. D. (1993). Forward markets and changes in macroeconomic performance: the case of oil.
Journal of Macroeconomics 15, 521–552.
Ferson, W., & Harvey, C. R. (1995). Predictability and time-varying risk in world equity markets.Research in Finance 13, 25–88.
Flannery, M. J., & James, C. M. (1984). The effect of interest rate changes on the common stock returns of financial institutions.Journal of Finance 39, 1141–1153.
Foster, A. (1996). Price discovery in oil markets: a time-varying analysis of the 1990–91 gulf conflict. Energy Economics 18, 231–246.
Gregory, R. G. (1976). Some implications of the growth of the mineral sector.Australian Journal of Agricultural Economics 20, 71–91.
Hamao, Y. (1989). An empirical examination of the arbitrage pricing theory: using Japanese data.Japan and the World Economy 1, 45–61.
Helliwell, J., Sturm, P., Jarrett, P., & Salou, G. (1986). The supply side in the OECD’s macroeconomic model.OECD Economic Studies 6, 75–131.
Jones, C., & Kaul, G. (1996). Oil and the stock markets.Journal of Finance 51, 463–491.
Kaneko, T., & Lee, B. S. (1995). Relative importance of economic factors in the U.S. and Japanese stock markets.Journal of the Japanese and International Economies 9, 290–307.
Khoo, A. (1994). Estimation of foreign exchange exposure: an application to mining companies in Australia.Journal of International Money and Finance 13, 342–363.
Loudon, G. (1993). The foreign exchange operating exposure of Australian stocks. Accounting and Finance 32, 19–32.
MacKinlay, A. C., & Richardson, M. P. (1991). Using generalized method of moments to test mean-variance efficiency.Journal of Finance 46, 511–527.
Madura, J., & E. R. Zarruk. (1995). Bank exposure to interest rate risk: a global perspective.Journal of Financial Research 18, 1–13.
Malliaris, A. G., & Urrutia, J. L. (1995). The impact of the Persian gulf crisis on national equity markets. Advances in International Banking and Finance 1, 43–65.
Merton, R. (1973). An intertemporal capital asset pricing model.Econometrica 41, 867–887.
Miller, M. H., & Upton C. W. (1985a). A test of the Hotelling valuation principle.Journal of Political Economy 93, 1–25.
(6)
R.W. Faff, T.J. Brailsford / Journal of Energy Finance and Development 4 (1999) 69–87 87 Miller, M. H., & Upton, C. W. (1985b). The pricing of oil and gas: some further results. Journal of
Finance 40, 1009–1018.
Moosa, I. A., & Al-Loughani, N. E. (1994). Unbiasedness and time varying risk premia in the crude oil futures markets.Energy Economics 16, 99–105.
Strong, J. S. (1991). Using oil share portfolios to hedge oil price risk.Quarterly Review of Economics and Business 3, 48–63.