Applications of state space models in finance

7.4 Empirical results 153
cumulative return (%)
1200
1000
800
600
400
200
0
(a) KF cumulative spread return
1996 1998 2000 2002 2004
relative cumulative return (%)
20
0
−20
−40
−60
−80
−100
(b) Relative cumulative spread returns
RR1
RR5
RLS
1996 1998 2000 2002 2004
Figure 7.7: (a) Cumulative KF spread return and (b) cumulative spread returns of the
alternative portfolios relative to the KF based portfolios.
conditional factor loadings to generate return forecasts results in long-short portfolios
that are characterized by both higher absolute and higher risk-adjusted returns.
In order to analyze how the performance of the Kalman filter based strategy relative to
that of the alternative least squares based strategies evolves over time, we look at relative
cumulative spread returns. The relative cumulative spread return of an alternative least
squares based long-short portfolio at date t for t = 164, . . . , 683, is computed as the
difference between its cumulative spread return and the KF cumulative spread return
at the same date. The left hand side of Figure 7.7 shows the KF cumulative spread
return, which is steadily growing over time. Panel (b) presents the relative cumulative
spread returns for the RLS, RR5 and RR1 based long-short portfolios, respectively.
It can be seen that during the low volatily regime at the beginning of the sample, all
relative cumulative spread return series move sideways around the zero line. In 1999,
the RR1 series reaches 20%, which means that this strategy has been temporally able to
outperform the KF strategy. At the end of 1999, the KF long-short portfolio started
to consistently outperform all alternative strategies. The sharpest relative gains are
observed for the last few months before the market peaked, and also in 2002 when
global equity markets collapsed.
The results in this section demonstrate the practical relevance of employing conditional
factor loadings from a portfolio management perspective. The analysis of longshort
portfolios reveals that the use of Kalman filter based factor loadings, which take
the time-varying nature of fundamental and macroeconomic systematic risks explicitly
into account, results in better return forecasts of pan-European sectors. The relative
performance of Kalman filter based long-short portfolios is analyzed over time by looking
at relative cumulative spread returns. It is found that the superior performance traces
back to the increased flexibility of conditional factor loadings, which particularly pays
off during more volatile market regimes.