(13.2%), and the chemical sector (1

(13.2%), and the chemical sector (13.2%). The rest of the rest of the sectors
consume less than 3% of oil. The implication here is that since oil
usage intensity is different for different sectors any increase in the oil
price will impact sectors differently.
3.2. Main findings
3.2.1. Oil price–return volatility relationship
To examine the effect of the oil price on firm return volatility, we estimate
the following GARCH (1,1) time series model for each of the 560
firms using daily time series data from05 January 2000 to 31 December
2008:
h2t
¼ γ0
þ γ1gOPt þ γ2ε2t
−1
þ γ3h2t
−1; ð1Þ
where ht
2 is stock return volatility on day t; εt − 1
2 is the squared of the
one period lagged error term obtained from Eq. (2); ht − 1
2 is the one
period lagged variance term, which measures persistence; and gOPt is
the growth rate in the crude oil price. The mean equation in for the
GARCH (1,1) model, where the lag orders are chosen based on the
Schwarz Information Criterion, is of the following form:
Rt ¼ α1 þ εt ð2Þ
where Rt is the stock return at time t and εt is an error term.
We report results for the oil price and the firm return volatility relationship
in Table 2. We run Eq. (1) 560 times, then categorise the 560
firms into sectors, and work out the percentage of the times the relationship
between the oil price and firm return volatility is statistically
significant and negative and statistically significant and positive.
Our main finding, consistent with our a priori belief, is that the oil
price affects firm return volatility differently depending on the sector
to which firms belong to, both in terms of sign and magnitude. These
results on the oil price and firm return volatility are as follows. First,
the effect of the oil price on firm return volatility is positive and statistically
significant in 41.2% of firms in the banking sector. Around 15% of
firms experience a negative and statistically significant impact of the
oil price on firm return volatility in the banking sector. For the remaining
13 sectors, the relationship for the majority of firms is negative and
statistically significant. The statistically significant negative relationship
ranges from as low as 36.8% in the case of firms in the electricity sector
to as high as 67.9% for firms in the medical sector.
Table 2
Effects of oil price on sectoral volatility. The results are based on the following GARCH
(1,1) model: ht
2 = γ0 + γ1gOPt + γ2εt − 1
2 + γ3ht − 1
2 for different sectors. In this
model, ht
2 is stock return volatility on day t; gOP is the growth rate in crude oil
prices; and γ1 is the main parameter that is estimated. The mean equation has
the following form: Rt = α1 + εt; where Rt is the stock returns at time t and εt is
an error term. We report the number of firms in different sectors that are statistically
significant or statistically insignificant with positive and negative signs. In
addition, this result is converted into percentage for each sector and reported in
the parenthesis.
Sectors Sig + Sig − Insig + Insig −
Energy 4 (10%) 15 (37.5%) 9 (22.5%) 12 (30%)
Electricity 14 (18.4%) 28 (36.8%) 14 (18.4%) 20 (26.3%)
Supply 10 (15.4%) 30(46.2%) 12 (18.5%) 13 (20%)
Manufacturing 15 (20%) 33 (44%) 9 (12%) 17 (22.7%)
Food 5 (17.9%) 15 (53.6%) 3 (10.7%) 5 (17.9%)
General services 5 (22.7%) 12 (54.5%) 2 (9.1%) 3 (13.6%)
Chemical 3 (15.8%) 9 (47.4%) 4 (21.1%) 3 (15.8%)
Medical 3 (10.7%) 19 (67.9%) 2 (7.1%) 4 (14.3%)
Engineering 5 (13.9%) 18 (50%) 7 (19.4%) 6 (16.7%)
Computer 1 (6.7%) 7 (46.7%) 3 (20%) 4 (26.7%)
Transportation 3 (11.5%) 12 (46.2%) 2 (7.7%) 9 (34.6%)
Banking 14 (41.2%) 5 (14.7%) 11 (32.4%) 4 (11.8%)
Financial 12 (16.4%) 29 (39.7%) 17 (23.3%) 15 (20.5%)
Real estate 3 (13.0%) 10 (43.5%) 3 (13.0%) 7 (30.4%)