Applications of state space models in finance

7.4 Empirical results 145
β TS
0.4
0.3
0.2
0.1
0.0
−0.1
−0.2
−0.3
(a) Automobiles
1994 1998 2002
β TS
0.6
0.4
0.2
0.0
−0.2
(b) Insurance
1994 1998 2002
Figure 7.3: T S factor loadings for Automobiles and Insurance.
is dominated by large cap pharmaceutical stocks, such as GlaxoSmithKline, Novartis
and Sanofi-Aventis. Therefore, size beta of Healthcare can be expected to be higher.
At the first glance, both series mostly move in positive territory, which is representative
for all sectors. This means that over the entire estimation period returns are usually
positively related to an outperformance of large caps over small caps. As can be seen
from Panel (a), the size beta of the Industrials sector occasionally becomes negative and
rarely takes on values greater than 0.5. This is in contrast to the average size beta of
Healthcare, which moves around unity as shown in Panel (b).
Changes in the slope of the yield curve are generally assumed to capture changes in
the state of the economy and would naturally be expected to have a systematic impact
on cyclical sectors. The two panels in Figure 7.3 illustrate the paths of T S loadings for
the non-financial cyclical sector Automobiles and the financial cyclical Insurance sector,
respectively. For both sectors, the sensitivities alternate between positive and negative
territory: at some stages of the cycle the corresponding sectors rise when the yield curve
β OIL
0.4
0.3
0.2
0.1
0.0
−0.1
(a) OIL beta
1994 1998 2002
β VGR
0.6
0.4
0.2
0.0
−0.2
(b) VGR beta
1994 1998 2002
Figure 7.4: OIL and V GR betas for the Oil & Gas sector.