An Application of the EM-algorithm to
Approximate Empirical Distributions of Financial
Indices with the Gaussian Mixtures

arXiv:1607.01033v1 [] 29 Jun 2016

Sergey Tarasenko

Abstract—In this study I briefly illustrate application of the
Gaussian mixtures to approximate empirical distributions of financial
indices (DAX, Dow Jones, Nikkei, RTSI, S&P 500). The resulting
distributions illustrate very high quality of approximation as evaluated
by Kolmogorov-Smirnov test. This implies further study of application of the Gaussian mixtures to approximate empirical distributions
of financial indices.

Let Θ be a vector of parameters: Θ = (p1 ,p2 ,...,
pk−1 ,Θ1 ,Θ2 ,..., Θk ). Next, we deompose the logarithms likelihood function into three components:

lnL(Θ) =

Keywords—financial indices, Gaussian distribution, mixtures of
Gaussian distributions, Gaussian mixtures, EM-algorithm

n
X

X

k
ln
pj · f (xi ; Θj ) =

i=1

n
k X
X

PPROXIMATION of empirical distributions of financial indices using mixture of Gaussian distributinos
(Gaussian mixtures) (eq. (1)) has been recently discussed
by Tarasenko and Artukhov [1]. Here I provide detailed
explanation of steps and methods.

A

n
X

pi · N (µi , σi )

(1)

i=1

II. EM- ALGORITHM FOR MIXTURE SEPARATION
A. General Theory
The effective procedure for separation of mixtures was
proposed by Day [2], [3] and Dempster et al. [4]. This
procedure is based on maximization of logarithmic likelihood
function under parameters p1 ,p2 ,..., pk−1 ,Θ1 ,Θ2 ,..., Θk , where
k is number of mixture components:
n
X
i=1

X

k
ln
pj · f (xi ; Θj ) → max
j=1

gij ln(pj )

(5)

gij lnf (xi ; Θj )

(6)

j=1 i=1

I. I NTRODUCTION

GM =

(4)

j=1

pj ,Θj

Posterior probability gij is equal or greater then 0 and
Pk
j=1 gij for any i.
S. Tarasenko is independent researcher. Email: infra.core@gmail.com

−

k X
n
X

gij ln(gij )

(7)

j=1 i=1

For this algorithm to work, the initial value Θ̂0 is used
to calculate inital approximations for posterior probabilities
0
0
gij
. This is Expectation step. Then values of gij
are used to
1
calculate value of Θ̂ during the Maximization step.
Each of components (6) and (7) are maximized independently from each other. This is possible because component
(6) depends only on pj (i=1,...,k), and component (7) depends
only on Θj (j=1,...,n).
As a solution of optimization task (8)
k X
n
X

gij ln(pj ) → max

p1 ,...,pk

j=1 i=1
(t+1)

(3)

n
k X
X
j=1 i=1

(2)

In general, the algorithms of mixture separations based on
(2) are called Estimation and Maximization (EM) algorithms.
EM-algorithm consists of two steps: E - expectation and Mmaximization. This section is focused scheme how to construct
EM-algorithm.
Let gij is defined as posterior porbability of oservations xi
to belong to j-th mixture component (class):
pj · f (xi ; Θj )
gij = Pk
j=1 pj · f (xi ; Θj )

+

a value of pj

(8)

for the iteration t + 1 is calculated as:
n

(t+1)

pj

=

1 X (t)
g
n i=1 ij

(9)

where t is iteration number, t = 1,2, ,,,
A solution of optimization task (10)
k X
n
X
j=1 i=1

gij lnf (xi ; Θj ) → max

Θ1 ,...,Θk

(10)

depends on a particular type of function f (·).
Next we consider solution of optimization task (10), when
f (·) is Gaussian distribution.

B. Mixtures of Gaussian Distributions

TABLE I
M IXTURE MODEL FOR DAX

Here we employ Guassian distributions:
(x−µ)2
1
N (x; µ, σ) = √ exp− 2σ2
σ 2π

(11)

Therefore, a specific formula to compute posterior probabilities in the case of Gaussian mixtures is
exp−

gij = P
k

(x−µ)2
2σ 2

−
j=1 exp

Component
Component
Component
Component

1
2
3
4

Weight

Mean

0.152
0.223
0.287
0.337

-0.002
0.001
0.004
0.001

Standard
Diviation
0.018
0.017
0.014
0.009

+ln(pj )−ln(σj )

(x−µ)2
2σ 2

(12)
+ln(pj )−ln(σj )

According to the EM-algorithm, the task is to find value of
parameters Θj = (µj , σj ) by solving maximization problem
(14)
k
X

lnLj =

k X
n
X

lnLj =

(13)



(x−µ)2
1
−
2
2σ
ln √ exp
→
max
Θ1 ,Θ2 ,...,Θk
σ 2π
j=1 i=1

(14)

j=1

j=1 i=1

n
k X
X

The solution of this mazimization problem is given by eqs.
(16) and (16):

µ̂j = Pn
σ̂j = Pn

1

1

n
X

i=1 gij i=1
n
X

i=1 gij i=1

gij xi

gij (xi − µ̂j )2

(15)
TABLE II
M IXTURE MODEL FOR D OW J ONES (DJIA)

(16)

Having calculated optimal values for weights pj and paramaters Θj (j=1,...,k) during a single iteration, we apply these
optimal values to obtain estimates of posterior probabilities
during the Expectation step of the next iteration.
As a stop criterion, we use difference between values of
loglikelyhood on iteration t and iteration t + 1:
lnL(t+1) (Θ) − lnL(t) (Θ) < 

Fig. 1. Empirical distribution of DAX values during the period 14 April
2003 - 14 April 2004. p < 0.05, KSSTAT=0.031

Component
Component
Component
Component

1
2
3
4

Weight

Mean

0.173
0.279
0.396
0.152

0.001
0.001
0.001
0.000

Standard
Diviation
0.008
0.008
0.008
0.001

(17)

where  is infinitely small real value.
III. A N A PPLICATION OF THE EM- ALGORITHM TO
A PPROXIMATE E MPIRICAL D ISTRIBUTIONS OF F INANCIAL
I NDICES WITH G UASSIAN M IXTURES
In this section, I provide several examples of EM-algorithm
applications to approximate empirical distributions of financial
indices with Gaussian mixtures. I consider the following
indices: DAX, Dow Jones Industrial, Nikkei, RTSI, and S&P
500.
In Figs. 1-5, the green line corresponds to the Gaussian
distribution, the red line illustrates a Gaussian mixture and
the blue lines represent components of the Gaussian mixture.

Fig. 2. Empirical distribution of Dow Jones Industrial values during the
period 14 April 2003 - 14 April 2004, p < 0.01, KSSTAT=0.023

TABLE V
M IXTURE MODEL FOR S&P 500

TABLE III
M IXTURE MODEL FOR N IKKEI

Component
Component
Component
Component

1
2
3
4

Weight

Mean

0.167
0.180
0.367
0.286

-0.014
0.002
0.011
-0.002

Standard
Diviation
0.014
0.013
0.008
0.005

Component
Component
Component
Component

1
2
3
4

Weight

Mean

0.014
0.331
0.470
0.186

0.011
0.000
0.001
0.000

Standard
Diviation
0.027
0.009
0.009
0.001

Fig. 5. Empirical distribution of S&P 500 values during the period 14 April
2003 - 14 April 2004, p < 0.01, KSSTAT=0.024
Fig. 3. Empirical distribution of Nikkei index values during the period 14
April 2003 - 14 April 2004, p < 0.01, KSSTAT=0.027

TABLE IV
M IXTURE MODEL FOR RTSI

Component
Component
Component
Component

1
2
3
4

Weight

Mean

0.062
0.294
0.303
0.341

-0.014
-0.005
0.005
0.011

Standard
Diviation
0.044
0.019
0.014
0.011

IV. D ISCUSSION AND C ONCLUSION
The results presented in this study illustrate that EMalgrothim can be effectively used to approximate empiral
distributions of log daily differences of financial indices.
Throughout the data for five selected indices, EM-algorithm
provided very good approximation of empoiral distirbution
with Gaussian mixtures.
The approximations based on Gaussian mixtures can be
used to improve application of Value-at-Risk and other methods for financial risk analysis.
This implies further explorations in applying Gaussian mixtures and the EM-algorithm for the purpose of approximation
of empirial distirbutions of financial indices.
R EFERENCES
[1] Tarasenko, S., and Artukhov, S. (2004) Stock market pricing models.
In the Proceedings of International Conference of Young Scientists
Lomonosov 2004, p. 248-250.
[2] Day, N.E. (1969) Divisive cluster analysis and test for multivariate
normality. Session of the ISI, London, 1969.
[3] Day, N.E. (1969) Estimating the components of a mixture of normal
distributions., Biometrika, 56, N3.
[4] Dempster, A., Laird, G. and Rubin, J. (1977) Maximum likelihood from
incomplete data via EM algorithm. Journal of Royal Statistical Society,
B, 39.

Fig. 4. Empirical distribution of RTSI values during the period 14 April
2003 - 14 April 2004, p < 0.01, KSSTAT=0.021

