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a portfolio is not guaranteed in a
real market, even if one assumes that the asset dynamics are well described
by a geometric Brownian motion [58]. When we consider real markets, the
complexity of the modeling grows, the number of assumptions increases, and
the generality of the solutions diminishes.
15.4 Extension of the Black & Scholes model
It is a common approach in science to use a model system to understand
the basic aspect of a scientific problem. The idealized model is not able to
describe all the occurrences observed in real systems, but is able to describe
those that are most essential. As soon as the validity of the idealized model is
assessed, extensions and generalizations of the model are attempted in order
to better describe the real system under consideration. Some extensions do
not change the nature of the solutions obtained using the model, but others
do.
The Black & Scholes model is one of the more successful idealized models
currently in use. Since its introduction in 1973, a large amount of literature
dealing with the extension of the Black & Scholes model has appeared.
These extensions aim to relax assumptions that may not be realistic for real
financial markets. Examples include
• option pricing with stochastic interest rate [4, 120];
• option pricing with a jump-diffusion/pure-jump stochastic process of stock
price [13, 121];
• option pricing with a stochastic volatility [71, 72]; and
• option pricing with non-Gaussian distributions of log prices [7, 21] and
with a truncated Le´vy distribution [118].
128 Options in real markets
We will briefly comment on general equations describing the time evolution
of stock price and volatility [12] that is much more general than the Black
& Scholes assumption of geometric Brownian motion. Our aim is to show
how the complexity of equations increases when one or several of the Black
& Scholes assumptions are relaxed. These general equations are
dY (t)
Y (t)
= [r(t)− λµJ]dt+ σ(t)dWY (t) + J(t)dq(t) (15.4)
and
dσ2(t) = [θv −Kvσ2(t)]dt+ σvσ(t)dWv(t), (15.5)
while the Black & Scholes assumption of geometric Brownian motion is,
from (14.5),
dY (t)
Y (t)
= µdt+ σdW (t); σ = const. (15.6)
Here r(t) is the instantaneous spot interest rate, λ the frequency of jumps
per year, σ2(t) the diffusion component of return variance, WY (t) and Wv(t)
standard Wiener processes with covariance cov[dWY (t), dWv(t)] = ρdt, J(t)
the percentage jump size with unconditional mean µJ , q(t) a Poisson process
with intensity λ, and Kv, θv and σv parameters of the diffusion component
of return variance σ2(t).
It is worth pointing out that the increase in complexity is not only technical,
but also conceptual. This is the case because the process is so general that it
is no longer possible to build a simple replicating portfolio, or to perfectly
hedge an ‘optimal’ portfolio. The elegance of the Black & Scholes solution
is lost in real markets.
15.5 Summary
Complete knowledge of statistical properties of asset return dynamics is
essential for fundamental and applied reasons. Such knowledge is crucial for
the building and testing of a statistical model of a financial market. In spite
of more than 50 years of effort, this goal has not yet been achieved.
The practical relevance of the resolution of the problem of the statistical
properties of asset return dynamics is related to the optimal resolution
of the rational pricing of an option. This is a financial activity that is
extremely important in present-day financial markets. We saw that the
dynamical properties of asset return dynamics – such as the continuous or
discontinuous nature of its changes, the random character of its volatility,
15.5 Summary 129
and the knowledge of the pdf function of asset returns – need to be known
in order to adequately pose, and possibly solve, the option-pricing problem.
Statistical and theoretical physicists can contribute to the resolution of
these scientific problems by sharing – with researchers in the other disciplines
involved – the background in critical phenomena, disordered systems, scaling,
and universality that has been developed over the last 30 years.
Appendix A: Notation guide
Chapter 1
x income of a given individual
y number of people having income x or greater
ν exponent of Pareto law
xi random variable
Sn sum of n random variables
P (x) probability density function of the random variable x
α index of the Le´vy stable distribution
d dimension of a chaotic attractor
∼ symbol to denote asymptotic equality
Chapter 2
t time
Yt price of a financial asset at time t
E{x} expected value of the variable x
E{x|y1, y2, y3, . . .} expected value of x conditional on the occurrence of
y1, y2, y3, . . .
K(n) bit length of the shortest computer program able to
print a given string of length n
Chapter 3
xi random variable
n number of random variables
Sn sum of n random variables
E{f(x)} average value of f(x)
δij Kronecker delta
∆t time step
130
Appendix A: Notation guide 131
Chapter 3 (cont.)
x(n∆t) sum of n random variables, each one occurring after a
time step ∆t
s2 second moment of a dichotomic variable xi
D diffusion constant⊗
convolution symbol
σn standard deviation of Sn
Ui truncated random variable
� small number
x˜ scaled variable
P (x) probability density function
P˜ (x˜) scaled probability density function
π pi
Fn(S) distribution function of a scaled S˜(n)
Φ(S) distribution function of a Gaussian process
Qj(S) polynomial encountered in convergence studies
ri third moment of the absolute value of xi
s2n sum of n variances σ
2
i
Chapter 4
P (x) probability density function
ϕ(q) characteristic function
F[f(x)] operator indicating the Fourier transform of f(x)
F(q) Fourier transform of f(x)
ϕn(q) characteristic function of random variable Sn
PL(x) symmetric Le´vy stable distribution
γ scale factor of the Le´vy distribution
µ average value of a random variable
β asymmetry parameter of Le´vy distribution
Γ(x) Gamma function
S˜n scaled variable
P˜ (S˜n) scaled probability density function
ϕk(q) characteristic function of elementary random variable
concurring to an infinitely divisible random variable
- length
k integer number
Z(t) price change at time t
132 Appendix A: Notation guide
Chapter 5
Y (t) price of a financial asset at time t
Z(t) price change at time t
ZD(t) deflated or discounted price change
D(t) deflating or discounting time series
R(t) return at time t
S(t) successive differences of the natural logarithm of price
Chapters 6 and 7
E{f(x)} expected value of f(x)
f(x, t) probability density of observing x at time t
f(x1, x2; t1, t2) joint probability density of observing x1 at time t1 and x2
at time t2
f(x1; t1|x2; t2) conditional probability density of observing x2 at t2 after
observing x1 at t1
µ average value of the random process
R(t1, t2) autocorrelation function
τ ≡ t2 − t1 time lag
C(t1, t2) autocovariance
τc characteristic time
τ0 time scale
ν exponent
η exponent
τ∗ typical time
σ2 variance
f frequency
S(f) power spectrum
σ(t) standard deviation at different time horizons
Chapters 8 and 9
Y (t) price of a financial asset at time t
S(t) difference of the logarithm of price
x random variable
yi random variables
Z random variable
P (Z) probability density function
n, k, m integers
Cn constant
Γ(x) Gamma function
Ω(t) directing process
Appendix A: Notation guide 133
Chapters 8 and 9 (cont.)
PL(x) Le´vy distribution
- truncation length
PG(x) Gaussian distribution
α index of the Le´vy distribution
γ scale factor of the Le´vy distribution
c constant
n× number of i.i.d. variables needed to observe a crossover
between Le´vy and Gaussian regimes
S˜ scaled variable
P˜ (S˜) scaled probability density function
ϕ(q) characteristic function
Z(t) index changes
σ standard deviation
gi normalized difference of the logarithm of price of company i
F(g) cumulative distribution
σi volatility (standard deviation) of company i
Chapter