Chapter 7
Nonlinear Dynamical Systems
7.1 MOTIVATION
It is well known that many nonlinear dynamical systems, including
seemingly simple cases, can exhibit chaotic behavior. In short, the
presence of chaos implies that very small changes in the initial condi-
tions or parameters of a system can lead to drastic changes in its
behavior. In the chaotic regime, the system solutions stay within the
phase space region named strange attractor. These solutions never
repeat themselves; they are not periodic and they never intersect.
Thus, in the chaotic regime, the system becomes unpredictable. The
chaos theory is an exciting and complex topic. Many excellent books
are devoted to the chaos theory and its applications (see, e.g., refer-
ences in Section 7.7). Here, I only outline the main concepts that may
be relevant to quantitative finance.
The first reason to turn to chaotic dynamics is a better understand-
ing of possible causes of price randomness. Obviously, new infor-
mation coming to the market moves prices. Whether it is a
company’s performance report, a financial analyst’s comments, or a
macroeconomic event, the company’s stock and option prices may
change, thus reflecting the news. Since news usually comes unexpect-
edly, prices change in unpredictable ways.
1
But is new information the
only source reason for price randomness? One may doubt this while
observing the price fluctuations at times when no relevant news is
69
released. A tempting proposition is that the price dynamics can be
attributed in part to the complexity of financial markets. The possi-
bility that the deterministic processes modulate the price variations
has a very important practical implication: even though these pro-
cesses can have the chaotic regimes, their deterministic nature means
that prices may be partly forecastable. Therefore, research of chaos in
finance and economics is accompanied with discussion of limited
predictability of the processes under investigation [1].
There have been several attempts to find possible strange attractors
in the financial and economic time series (see, e.g., [1–3] and refer-
ences therein). Discerning the deterministic chaotic dynamics from a
‘‘pure’’ stochastic process is always a non-trivial task. This problem is
even more complicated for financial markets whose parameters may
have non-stationary components [4]. So far, there has been little (if
any) evidence found of low-dimensional chaos in financial and eco-
nomic time series. Still, the search of chaotic regimes remains an
interesting aspect of empirical research.
There is also another reason for paying attention to the chaotic
dynamics. One may introduce chaos inadvertently while modeling
financial or economic processes with some nonlinear system. This
problem is particularly relevant in agent-based modeling of financial
markets where variables generally are not observable (see Chapter
12). Nonlinear continuous systems exhibit possible chaos if their
dimension exceeds two. However, nonlinear discrete systems (maps)
can become chaotic even in the one-dimensional case. Note that the
autoregressive models being widely used in analysis of financial time
series (see Section 5.1) are maps in terms of the dynamical systems
theory. Thus, a simple nonlinear expansion of a univariate autore-
gressive map may lead to chaos, while the continuous analog of this
model is perfectly predictable. Hence, understanding of nonlinear
dynamical effects is important not only for examining empirical
time series but also for analyzing possible artifacts of the theoretical
modeling.
This chapter continues with a widely popular one-dimensional
discrete model, the logistic map, which illustrates the major concepts
in the chaos theory (Section 7.2). Furthermore, the framework for the
continuous systems is introduced in Section 7.3. Then the three-
dimensional Lorenz model, being the classical example of the low-
70 Nonlinear Dynamical Systems

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