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s2 ¼ 2(l À 1)= ln (2) (6:2:7)
The multifractal spectrum of the lognormal cascade that satisfies
(6.2.7) equals

(a À l)2
f(a) ¼ 1 À (6:2:8)
4(l À 1)
Note that in contrast to the binomial cascade, the lognormal
cascade may yield negative values of f(a), which requires interpret-
ation of f(a) other than the fractal dimension.
Innovation of multifractal process, DX ¼ X(t þ Dt) À X(t), is de-
scribed with the scaling rule

E[j(DX)jq ] ¼ c(q)(Dt)t(q)þ1 (6:2:9)
where c(q) and t(q) (so-called scaling function) are deterministic func-
tions of q. It can be shown that the scaling function t(q) is always
concave. Obviously, t(0) ¼ À1. A self-affine process (6.1.4) can be
treated as a multifractal process with t(q) ¼ Hq À 1. In particular, for
the Wiener processes, H ¼ 1„2 and tw (q) ¼ q=2 À 1. The scaling func-
tion of the binomial cascade can be expressed in terms of its multi-
pliers
t(q) ¼ log2 (m0 q þ m1 q ) (6:2:10)
67
Fractals



The scaling function t(q) is related to the multifractal spectrum f(a)
via the Legendre transformation
t(q) ¼ min[qa À f(a)] (6:2:11)
a

which is equivalent to
f(a) ¼ arg min[qa À t(q)] (6:2:12)
q

Note that f(a) ¼ q(a À H) þ 1 for the self-affine processes.
In practice, the scaling function of a multifractal process X(t) can
be calculated using so-called partition function
X
NÀ1
jX(t þ Dt) À X(t)jq
Sq (T, Dt) ¼ (6:2:13)
i¼0

where the sample X(t) has N points within the interval [0, T] with the
mesh size Dt. It follows from (6.2.9) that
log {E[Sq (T, Dt)]} ¼ t(q) log (Dt) þ c(q) log T (6:2:14)
Thus, plotting log {E[Sq (T, Dt)]} against log (Dt) for different values
of q reveals the character of the scaling function t(q). Multifractal
models have become very popular in analysis of the financial time
series. We shall return to this topic in Section 8.2


6.3 REFERENCES FOR FURTHER READING
The Mandelbrot™s work on scaling in the financial time series is
compiled in the collection [1]. Among many excellent books on frac-
tals, we choose [2] for its comprehensive material that includes a
description of relations between chaos and fractals and an important
chapter on multifractals [5].


6.4 EXERCISES
*1. Implement an algorithm that draws the Sierpinski triangle with
r ¼ 0:5 (see Figure 6.2).
Hint: Choose the following fixed points: (0, 0), (0, 100), (100,
0). Use the following method for the randomized choice of the
68 Fractals



fixed point: i ¼ [10 rand()] %3 where rand() is the uniform
distribution within [0, 1] and % is modulus (explain the ration-
ale behind this method). Note that at least 10000 iterations are
required for a good-quality picture.
*2. Reproduce the first five steps of the binomial cascade with
m0 ¼ 0:6 (see Figure 6.3). How will this cascade change if
m0 ¼ 0:8?
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
70 Nonlinear Dynamical Systems



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-
71
Nonlinear Dynamical Systems



dimensional continuous chaotic system, is described (Section 7.4).
Finally, the main pathways to chaos and the chaos measures are
outlined in Section 7.5 and Section 7.6, respectively.

7.2 DISCRETE SYSTEMS: THE LOGISTIC MAP
The logistic map is a simple discrete model that was originally used
to describe the dynamics of biological populations (see, e.g., [5] and
references therein). Let us consider a variable number of individuals
in a population, N. Its value at the k-th time interval is described with
the following equation
Nk ¼ ANkÀ1 À BNkÀ1 2 (7:2:1)
Parameter A characterizes the population growth that is determined
by such factors as food supply, climate, etc. Obviously, the popula-
tion grows only if A > 1. If there are no restrictive factors (i.e., when
B ¼ 0), the growth is exponential, which never happens in nature for
long. Finite food supply, predators, and other causes of mortality
restrict the population growth, which is reflected in factor B. The
maximum value of Nk equals Nmax ¼ A=B. It is convenient to intro-
duce the dimensionless variable Xk ¼ Nk =Nmax . Then 0 Xk 1,
and equation (7.2.1) has the form
Xk ¼ AXkÀ1 (1 À XkÀ1 ) (7:2:2)
A generic discrete equation in the form
Xk ¼ f(XkÀ1 ) (7:2:3)
is called an (iterated) map, and the function f(XkÀ1 ) is called the
iteration function. The map (7.2.2) is named the logistic map. The
sequence of values Xk that are generated by the iteration procedure
is called a trajectory. Trajectories depend not only on the iteration
function but also on the initial value X0 . Some initial points turn out
to be the map solution at all iterations. The value XÃ that satisfies the
equation
XÃ ¼ f(XÃ ) (7:2:4)
is named the fixed point of the map. There are two fixed points for the
logistic map (7.2.2):
72 Nonlinear Dynamical Systems



XÃ ¼ 0, and XÃ ¼ (A À 1)=A (7:2:5)
1 2

If A 1, the logistic map trajectory approaches the fixed point XÃ 1
from any initial value 0 X0 1. The set of points that the trajec-
tories tend to approach is called the attractor. Generally, nonlinear
dynamical systems can have several attractors. The set of initial values
from which the trajectories approach a particular attractor are called
the basin of attraction. For the logistic map with A < 1, the attractor
is XÃ ¼ 0, and its basin is the interval 0 X0 1.
1
If 1 < A < 3, the logistic map trajectories have the attractor
Ã
X2 ¼ (A À 1)=A and its basin is also 0 X0 1. In the mean time,
the point XÃ ¼ 0 is the repellent fixed point, which implies that any
1
trajectory that starts near XÃ tends to move away from it.
1
A new type of solutions to the logistic map appears at A > 3.
Consider the case with A ¼ 3:1: the trajectory does not have a single
attractor but rather oscillates between two values, X % 0:558 and
X % 0:764. In the biological context, this implies that the growing
population overexerts its survival capacity at X % 0:764. Then the
population shrinks ˜˜too much™™ (i.e., to X % 0:558), which yields
capacity for further growth, and so on. This regime is called period-
2. The parameter value at which solution changes qualitatively is
named the bifurcation point. Hence, it is said that the period-doubling
bifurcation occurs at A ¼ 3. With a further increase of A, the oscilla-
tion amplitude grows until A approaches the value of about 3.45. At
higher values of A, another period-doubling bifurcation occurs
(period-4). This implies that the population oscillates among four
states with different capacities for further growth. Period doubling
continues with rising A until its value approaches 3.57. Typical tra-
jectories for period-2 and period-8 are given in Figure 7.1. With
further growth of A, the number of periods becomes infinite, and
the system becomes chaotic. Note that the solution to the logistic map
at A > 4 is unbounded.
Specifics of the solutions for the logistic map are often illustrated
with the bifurcation diagram in which all possible values of X are
plotted against A (see Figure 7.2). Interestingly, it seems that there is
some order in this diagram even in the chaotic region at A > 3:6. This
order points to the fractal nature of the chaotic attractor, which will
be discussed later on.
73
Nonlinear Dynamical Systems



0.95
Xk

0.85


0.75


0.65


0.55


0.45


0.35
A = 2.0
A = 3.1 k
A = 3.6
0.25
1 11 21 31 41
Figure 7.1 Solution to the logistic map at different values of the
parameter A.


0 X 1
3




A




4
Figure 7.2 The bifurcation diagram of the logistic map in the parameter
region 3 A < 4.
74 Nonlinear Dynamical Systems



Another manifestation of universality that may be present in cha-
otic processes is the Feigenbaum™s observation of the limiting rate at
which the period-doubling bifurcations occur. Namely, if An is the
value of A at which the period-2n occurs, then the ratio
dn ¼ (An À AnÀ1 )=(Anþ1 À An ) (7:2:6)
has the limit
lim dn ¼ 4:669 . . . : (7:2:7)
n!1

It turns out that the limit (7.2.7) is valid for the entire family of maps
with the parabolic iteration functions [5].
A very important feature of the chaotic regime is extreme sensitiv-
ity of trajectories to the initial conditions. This is illustrated with
Figure 7.3 for A ¼ 3:8. Namely, two trajectories with the initial
conditions X0 ¼ 0:400 and X0 ¼ 0:405 diverge completely after 10


1
Xk



0.8




0.6




0.4




0.2

X0 = 0.4
X0 = 0.405
k
0
1 11 21
Figure 7.3 Solution to the logistic map for A ¼ 3.8 and two initial condi-
tions: X0 ¼ 0:400 and X0 ¼ 0:405.
75
Nonlinear Dynamical Systems



iterations. Thus, the logistic map provides an illuminating example of
complexity and universality generated by interplay of nonlinearity
and discreteness.


7.3 CONTINUOUS SYSTEMS
While the discrete time series are the convenient framework for
financial data analysis, financial processes are often described using
continuous presentation [6]. Hence, we need understanding of the
chaos specifics in continuous systems. First, let us introduce several
important notions with a simple model of a damped oscillator (see,
e.g., [7]). Its equation of motion in terms of the angle of deviation
from equilibrium, u, is
d2 u du
þ g þ v2 u ¼ 0 (7:3:1)
dt2 dt
In (7.3.1), g is the damping coefficient and v is the angular frequency.
Dynamical systems are often described with flows, sets of coupled
differential equations of the first order. These equations in the vector
notations have the following form
dX
¼ F(X(t)), X ¼ (X1 , X2 , . . . XN )0 (7:3:2)
dt
We shall consider so-called autonomous systems for which the func-
tion F in the right-hand side of (7.3.2) does not depend explicitly on
time. A non-autonomous system can be transformed into an autono-
mous one by treating time in the function F(X, t) as an additional
variable, XNþ1 ¼ t, and adding another equation to the flow
dXNþ1
¼1 (7:3:3)
dt
As a result, the dimension of the phase space increases by one. The
notion of the fixed point in continuous systems differs from that of
discrete systems (7.2.4). Namely, the fixed points for the flow (7.3.2)
are the points XÃ at which all derivatives in its left-hand side equal
zero. For the obvious reason, these points are also named the equilib-
rium (or stationary) points: If the system reaches one of these points,
it stays there forever.
76 Nonlinear Dynamical Systems



Equations with derivatives of order greater than one can be also
transformed into flows by introducing additional variables. For
example, equation (7.3.1) can be transformed into the system
du dw
¼ Àgw À v2 u
¼ w, (7:3:4)
dt dt
Hence, the damped oscillator may be described in the two-dimen-
sional phase space (w, u). The energy of the damped oscillator, E,
E ¼ 0:5(w2 þ v2 u2 ) (7:3:5)
evolves with time according to the equation
dE
¼ Àgw2 (7:3:6)
dt
It follows from (7.3.6) that the dumped oscillator dissipates energy
(i.e., is a dissipative system) at g > 0. Typical trajectories of the
dumped oscillator are shown in Figure 7.4. In the case g ¼ 0, the
trajectories are circles centered at the origin of the phase plane. If
g > 0, the trajectories have a form of a spiral approaching the origin
of plane.2 In general, the dissipative systems have a point attractor in
the center of coordinates that corresponds to the zero energy.
Chaos is usually associated with dissipative systems. Systems with-
out energy dissipation are named conservative or Hamiltonian


2.5 2.5

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