Title: Linear and nonlinear market correlations: characterizing financial crises and portfolio optimization,
Abstract: Pearson correlation and mutual information based complex networks of the
day-to-day returns of US S&P500 stocks between 1985 and 2015 have been
constructed in order to investigate the mutual dependencies of the stocks and
their nature. We show that both networks detect qualitative differences
especially during (recent) turbulent market periods thus indicating strongly
fluctuating interconnections between the stocks of different companies in
changing economic environments. A measure for the strength of nonlinear
dependencies is derived using surrogate data and leads to interesting
observations during periods of financial market crises. In contrast to the
expectation that dependencies reduce mainly to linear correlations during
crises we show that (at least in the 2008 crisis) nonlinear effects are
significantly increasing. It turns out that the concept of centrality within a
network could potentially be used as some kind of an early warning indicator
for abnormal market behavior as we demonstrate with the example of the 2008
subprime mortgage crisis. Finally, we apply a Markowitz mean variance portfolio
optimization and integrate the measure of nonlinear dependencies to scale the
investment exposure. This leads to significant outperformance as compared to a
fully invested portfolio.