=Paper=
{{Paper
|id=Vol-2422/paper34
|storemode=property
|title=Detecting Stock Crashes Using Levy Distribution
|pdfUrl=https://ceur-ws.org/Vol-2422/paper34.pdf
|volume=Vol-2422
|authors=Andrii Bielinskyi,Vladimir Soloviev,Serhiy Semerikov,Viktoria Solovieva
|dblpUrl=https://dblp.org/rec/conf/m3e2/BielinskyiSSS19
}}
==Detecting Stock Crashes Using Levy Distribution==
https://ceur-ws.org/Vol-2422/paper34.pdf
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Detecting Stock Crashes Using Levy Distribution
Andrii Bielinskyi, Vladimir Soloviev[0000-0002-4945-202X],
Serhiy Semerikov[0000-0003-0789-0272]
Kryvyi Rih State Pedagogical University, 54, Gagarina Ave, Kryvyi Rih, 50086, Ukraine
{krivogame, vnsoloviev2016, semerikov}@gmail.com
Viktoria Solovieva
Kryvyi Rih Economic Institute of Kyiv National Economic University named after Vadym
Hetman, 16, Medychna St., Kryvyi Rih, 50000, Ukraine
vikasolovieva2027@gmail.com
Abstract. In this paper we study the possibility of construction indicators-
precursors relying on one of the most power-law tailed distributions – Levy’s
stable distribution. Here, we apply Levy’s parameters for 29 stock indices for the
period from 1 March 2000 to 28 March 2019 daily values and show their
effectiveness as indicators of crisis states on the example of Dow Jones Industrial
Average index for the period from 2 January 1920 to 2019. In spite of popularity
of the Gaussian distribution in financial modeling, we demonstrated that Levy’s
stable distribution is more suitable due to its theoretical reasons and analysis
results. And finally, we conclude that stability α and skewness β parameters of
Levy’s stable distribution which demonstrate characteristic behavior for crash
and critical states, can serve as an indicator-precursors of unstable states.
Keywords: alpha-stable distribution, stock market crash, indicator-predictor,
indicator of critical events, log-returns fluctuations, Dow Jones Industrial
Average Index.
1 Introduction
The efficient financial market is an integral part of the modern market economy. With
a rapidly growing financial market, new risk management methods are becoming more
demanded that take into account new non-Gaussian distributions. The task of
monitoring and predicting of possible critical states of financial and economics systems
are very relevant today. In our opinion, the availability of the time series for stock
markets gives the opportunity to solve such tasks in very effective ways. Financial
crises that regularly shake the world economy are characterized by noticeable
fluctuations in stock indices, thereby causing noticeable changes in the statistical
distributions of empirical data [1, 2]. Consequently, the analysis of the form and
parameters of the distribution of price fluctuations of the stock market indexes will
make it possible to predict the possible occurrence of the financial crisis.
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In 1900, Bachelier proposed the first model for the stochastic process of returns – an
uncorrelated random walk with independent, identically Gaussian distributed (i.i.d)
random variables [3]. This model is natural if one considers the return over a time scale
∆t to be the result of many independent “shocks”, which then lead by the central limit
theorem to a Gaussian distribution of returns [3]. However, empirical studies [4-6]
show that the distribution of returns has pronounced tails in striking contrast to that of
a Gaussian.
For time series S(t) which describes the dynamics of price on stock index, the returns
g(t) over some time scale Δt is defined as the forward changes in the logarithm of S(t),
g (t ) (ln S (t t ) / ln S (t )). (1)
For small changes in the price, the returns g(t) is approximately the forward relative
change
S (t t ) S (t )
g (t ) . (2)
S (t )
To illustrate mentioned above fact, we show in Fig. 1 the daily returns of the DJIA
market index for 1900-2019 and contrast it with a sequence of i.i.d. Gaussian random
variables.
Fig. 1. Probability density function of DJIA daily normalized returns during the period from
1900 to 2019.
It is obvious that the distribution of returns has heavy tails and in the general case can
be described as
P(g > x) ~ x–(1+α), α (0, 2] (3)
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and for stock indexes it has a universal look, known as the cubic laws of stock market
activity [6].
Fig. 2 confirms the cubic law for the DJIA index.
Fig. 2. Cumulative distributions of the normalized DJIA daily returns. Fits yield values
α = 2.02 ± 0.02.
In the analysis of cotton prices, Mandelbrot observed that in addition to being non-
Gaussian, the process of returns shows another interesting property: “time scaling” —
that is, the distributions of returns for various choices of ∆t, ranging from 1 day up to 1
month have similar functional forms [7]. Motivated by (i) pronounced tails, and (ii) a
stable functional form for different time scales, Mandelbrot [7] proposed that the
distribution of returns is consistent with a Levy stable distribution [8] – that is, the
returns can be modeled as a Levy stable process. Levy stable distributions arise from
the generalization of the Central Limit Theorem (CLT) to random variables which do
not have a finite second moment.
The CLT [9], which offers the fundamental justification for approximate normality,
points to the importance of α-stable distribution: they are the only limiting laws of
normalized sums of independent, identically distributed random variables. Gaussian
distributions, the best known member of the stable family, have long been well
understood and widely used in all sorts of problems. However, they do not allow for
large fluctuations and are thus inadequate for modeling high variability. Non-Gaussian
stable models, on other hand, do not share such limitations. In general, the upper and
lower tails of their distributions decreases like a power function. In literature, this is
often characterized as heavy or long tails. In the last two or three decades, data which
seem to fit the stable model has been collected in fields as diverse as economics,
telecommunications, hydrology and physics (see for example [6]).
During our research of Levy’s stable distribution, applied for the stock market, we
have found that there are many articles, which were devoted to it [4-6, 10-12].
Consequently, it was pointed out that Levy’s stable distribution fits better that the
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Gaussian distribution to financial markets. It is still debatable whether Levy’s stable
distribution is appreciable, since there is not enough theoretical material and there is
not a universal analyzing method for estimating parameters of Levy’s stable
distribution.
Therefore, during our research we discuss theoretical material applied to Levy’s
stable distribution, and discuss whether it acceptable for indicating crisis states on
financial markets or not.
Our research structured as follows. Section 2 is introduction to Levy’s stable
distribution and its properties. Section 3 describes different approaches for estimating
stable distribution parameters. In Section 4 we described how to estimate Levy’s stable
distribution and which method the most appreciable method for calculating its
parameters. Section 5 present classified DJIA price data, and obtained results.
2 Levy’s Stable Distribution Properties
Levy’s stable distribution being the generalization of the CLT, became an addition to a
n
wide class of distributions. Assume that Pn i 1 xi is the sum of i.i.d. random
variables xi. Then, if the variables xi have finite second moment, the CLT holds and Pn
is distributed as a Gaussian in the limit n→∞.
In case when the random variables xi are characterized by a distribution having
asymptotic power-law behavior (3) Pn will converge to a Levy stable stochastic process
of index α in the limit n→∞.
Stable distribution is presented by 4 parameters: α (0, 2] is the stability parameter,
β [–1, 1] the skewness parameter, γ [0, ∞) the scale parameter and δ (–∞, ∞) the
location parameter. Since the variables xi is characterized by four parameters, we will
denote α-stable distribution by S(α, β, γ, δ) and write
x ~ S(α, β, γ, δ) (4)
Stable distribution has a property that the mean cannot be defined for α (0, 1] and the
variance diverges for α (0, 2).
Furthermore, the Levy stable distributions cannot be defined in closed form for a
few cases: the case of (α, β) = (2, 0) corresponds to the Gaussian distribution,
(α, β) = (1, 0) to the Cauchy distribution. Instead, it is expressed in terms of their
Fourier transforms or characteristic functions (CF), which we denote as λ(k), where k
denotes the Fourier transformed variable.
For Levy stable distribution, if the variable xi follows S(α, β, γ, δ), the CF can be
expressed as [13]
k
exp{i k | k | [1 i k tg ( 2 )]}, ( 1)
(k ) . (5)
exp{i k | k | [1 i k ln k ]}, ( 1)
k
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It worth considering that with value of β = 0, the distribution is symmetric, right-tailed
if positive, and left-tailed if negative.
3 Methods for Estimation of Stable Law Parameters
There are numerous approaches which can estimate stable distribution parameters.
Since the probability density functions is not always expressed in a closed form, there
are some challenges to overcome the analytic difficulties. Thus, there have been
constructed a variety of methods: the approximate maximum likelihood estimation [14,
15], quantiles method [16, 17], fractional lower order moment method [18, 19], method
of log-cumulant [20], the logarithmic moment method [21] and more. Unfortunately,
some of those methods cannot be applied due to computational problems associated
with limited range of estimation, restricted range of parameters, high computational
costs, or requiring large number of data. However, several of them should be
mentioned.
3.1 Maximum Likelihood Method
DuMouchel was the first to obtain approximate ML estimates of α and γ (assuming
δ = 0) [22]. A multinomial approximation to the likelihood function is used in his
approach. Under some additional assumptions on α̂ and the likelihood function,
DuMouchel has shown the obtained estimates to be consistent and asymptotically
normal. However, the computational effort involved seems considerable.
A direct method can be formulated, after Brorsen and Yang [14], as follows. The
standard symmetric probability density functions defined by Zolotarev [23] is presented
as
/2
/( 1) U ( ,0 )
f ( x ) x1/ ( 1) U ( , 0)e x d , (6)
1 0
for α ≠ 1, x > 0, where Uα is defined by
/ (1 )
sin ( 0 ) cos( ( 0 ))
U ( ,0 ) , (7)
cos cos
and 0 is explained here [24]. Therefore, the parameters α, γ and δ can be estimated
from the observations xi (i = 1, 2, ..., n) by maximizing the log likelihood function
n n n /2
log zi zi /( 1)U ( ,0)
log f ( z ) n log n log( 1) 1 log U ( , 0)e
i d , (8)
i 1 i 1 i 1 0
where zi = |xi – δ| / γ.
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To avoid the discontinuity and nondifferentiability of the symmetric α-stable density
function at α = 1, α is restricted to be greater than one. Caution must be used when
evaluating the integrals (6) and (8), since the integrals are singular at η = 0.
An obvious disadvantage of this method is that it is a highly nonlinear optimization
problem and no initialization and convergence analysis is available.
3.2 Sample Quantiles Methods
Let xi be the f-th population quantile, so that S(α, β, γ, δ)(xi) = f. Let xˆ f be the
corresponding sample quantile, i.e. xˆ f satisfies Fn( xˆ f ) = f. As McCulloch [17] points
out, to avoid spurious skewness in finite samples, a correction must be made. If the xi’s
are arranged in ascending order, the correction may be performed by identifying xi with
2i 1
xˆq (i ) , where q(i ) , and then interpolating linearly to f from the two adjacent q(i)
2n
values. Then xˆ f is a consistent estimator of xf, the f quantile.
3.3 Regression Method
Koutrouvelis [13, 25] presented a regression type method of estimating the four
parameters of stable distribution. It is based on the following algorithm concerning the
CF. From (5) it can be derived that
2
log( log (k ) ) log(2 ) log k . (9)
The real and imaginary parts of λ(k) are for α ≠ 1 given by
R ( k ) exp( k ) cos k k sign(t )tg ,
2
and
I (k ) exp( k ) sin k k sign(t )tg .
2
The last two equations lead, apart from considerations of principal values, to
(k )
arctg I k tg sign(k ) k . (10)
R ( k ) 2
Equation (9) depends only on α and γ and suggests that we estimate these parameters
by regressing
y = log(–log|λN(k)|2)
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on ω = log|k| in the model
yn = m + αωn + εn, n = 1, 2, ..., N, (11)
where (kn) is an appropriate set of real numbers, m = log(2γα), and εn responds for an
error term.
With estimated and fixed parameters α and γ, the values of β and δ can be obtained
by using equation (9). Let gn(u) = Arctg(λI, n / λR, n), where Arctg denotes the principal
value of the arctan function. Then we can estimate β and δ by regressing z = gn(u) +
πτn(u) on u and sign(u)|u|α in the model
zl ul tg sign(ul ) ul l , l 1, . . . , L, (12)
2
where (ul) is an appropriate set of real numbers and vl denotes an error term.
As it was mentioned before, most of these methods have high computational costs,
restricted ranges of parameters or require a large number of data. Thus, we would like
to use simple approach proposed by Koutrovelis [25] which is based on CF and it is
tested to be valid and clears the above issues.
4 Estimation of Levy’s Stable Distribution
When we analyze data, we often assume that they are ergodic [26]. In general, if
random variables xn (n = 1, 2, ..., N) are ergodic with the integrable function f(x), the
preserving map T(x) and the measure p(x)dx in the space M, then the following equation
holds [27]:
1 N
lim f (T n x) f ( x) p( x)dx. (13)
N N M
n 1
Then, to consider characteristic functions, equation (13) comes out to be the following
ergodic equality [27]:
1 N
lim
N N
n 1
exp(ikxi ) exp(ikx) f ( x)dx,
(14)
for which we have
N
1
ˆ (k ) lim exp(ikxi ) . (15)
N N
n 1
This assumption allows us to empirically obtain the probability distribution. Hence, the
empirical characteristic function λN(k) of a large number of data set xn (n = 1, 2, ..., N)
can be calculated as
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N
1
ˆN (k ) exp(ikX n ). (16)
N n 1
When the data follow Levy’s stable distribution with the parameters (α, β, γ, δ) (α ≠ 1,
k > 0), the characteristic function can be presented as
ˆN (k ) exp i k ( k ) 1 i tan (17)
2
from equation (5). With equation (17), we can derive
log( log ˆN (k ) ) ˆ log k ˆ log ˆ (18)
and
ˆN , I ( k )
1
arctg ˆ ˆˆ tg ˆ k ˆ 1 ˆ,
(19)
ˆ
k N , R ( k ) 2
where each of ˆN , I (k ) and ˆN , R (k ) corresponds to the imaginary and real part of the
empirical CF. Through linear regression method in equations (18) and (19) around
k = 0 the parameters (α, β, γ, δ) can be estimated. In case when (γ, δ) are far from the
standard value of (1, 0), each parameter can not be estimated accurately. In this case
the data should be normalized to (γ, δ) = (1, 0) and then (α, β) can be estimated.
While the standard estimation method use the probability density function from the
actual data with difficulty in estimating the tails of the distribution which are essentially
important part of Levy’s stable distribution, the method which we use in this paper can
indicate the tail through the characteristic function. In addition, this method has a faster
convergence according to the increasing number of data. The introduced integer τn(u)
accounts for possible nonprincipal branches of the arctan function.
5 Data Classification of Dow Jones Industrial Average
In this paper we have estimated Levy’s parameters for stock indices for the period from
1 March 2000 to 28 March 2019 daily values. This data include stock indices of
developed countries, developing and emergent markets. The data were downloaded
from Yahoo Finance (http://finance.yahoo.com) and Investing.com
(https://www.investing.com). The distribution parameters were found for the entire
time series and the algorithm of a moving window. For moving window, the part of the
time series (window), for which there were calculated corresponding parameters, was
selected. Then, the window was displaced along the time series in definite increment
(step) and the procedure repeated until all the studied series had exhausted. For our case
the window width is 500 and 1000 days, time step 1 and 5 days. The calculation results
for the whole time series of the order-decreasing parameter α are shown in Table 1.
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Table 1. The calculated Levy’s stable parameters α and β for the considered stock indices. The
results were obtained for the length of window 500 and time step 1 day.
N Index α β
1 Nikkei 225 1.71 -0.240
2 IBEX 35 1.71 -0.206
3 CAC 40 1.70 -0.247
4 OMX Stockholm 30 1.70 -0.152
5 FTSE MIB 1.69 -0.374
6 Swiss Market Index 1.69 -0.210
7 DAX PERFORMANCE 1.68 -0.199
8 FTSE 100 1.68 -0.180
9 Warsaw Stock Exhange WIG 1.68 -0.022
10 BEL 20 1.67 -0.236
11 TA 35 1.67 -0.186
12 KOSPI Composite 1.66 -0.304
13 S&P/TSX Composite 1.60 -0.349
14 AEX 1.60 -0.214
15 BIST 100 1.60 -0.120
16 Dow Jones Industrial Average 1.59 -0.126
17 BOVESPA 1.58 -0.080
18 Hang Seng 1.58 -0.153
19 S&P 500 1.57 -0.151
20 IPC MEXICO 1.48 -0.118
21 NASDAQ Composite Index 1.48 -0.139
22 RTS Index 1.46 -0.081
23 BSE Sensex 30 1.44 -0.027
24 Nifty 50 1.42 -0.047
25 Jakarta Stock Exchange Composite 1.27 -0.043
26 Shanghai Composite 1.27 -0.046
27 KSE 100 1.05 -0.050
28 Ukraine PFTS 0.83 -0.089
29 S&P Merval 0.74 -0.055
The considered stock indices for the specified period include crisis phenomena and
these periods obviously affect the dynamics of distribution parameters. Therefore, you
should calculate them in the model of the moving window and compare their dynamics
with the dynamics of the original time series.
For analysing and explaining basic characteristics of complex systems with α-stable
distribution, we have chosen Dow Jones Industrial Average index (DJIA) as the most
quoted financial barometer in the world. In addition, like complex systems, financial
markets fascinating examples of complexity: a real world complex system whose
evolution is dictated by the decisions of many people, generating huge amounts of data.
For understanding of the falls that occurred on this market, we analysed different
scientific articles [31-32], and relying on our research, we classified them on crashes
and critical event, and separated DJIA time series into two parts where first part
occupies period from 2 January 1920 to 3 January 1983 and second part from 4 January
1983 to 18 March 2019, for having better overview of its dynamics. Note that the data
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set here is an every 1-day data, except those when stock market closed and does not
work. During our research it was established that:
─ Crashes are short, time-localized drops, with strong losing of price each day.
─ Critical events are those falls that, during their existence, have not had such serious
changes in price as crashes.
As it is seen from the Table 1, during DJIA existence, many crashes and critical events
shook it. According to our classification, events with number (1, 10, 13, 15) are crashes,
all the rest – critical events. From the data above, we estimate the parameters α and β
of the stable distribution that the best describes the empirical returns.
Further, comparing the dynamics of the actual time series and the corresponding
measures of complexity, we can judge the characteristic changes in the dynamics of the
behavior of complexity with changes in the stock index. If the estimated parameter
behaves in a definite way for all periods of crashes, for example, decreases or increases
during the pre-critical period, then it can serve as an indicator-precursor of such a
crashes phenomenon.
Table 2 shows the major crashes and critical events related to our classification.
Table 2. Major Historical Corrections since 1920.
N Interval Days in correction Decline, %
1 03.09.1929-29.10.1929 41 39.64
2 01.03.1938-31.03.1938 23 24.15
3 08.04.1940-05.06.1940 42 25.10
4 21.08.1946-10.09.1946 14 16.35
5 30.07.1957-22.10.1957 60 17.51
6 19.03.1962-28.05.1962 50 19.91
7 18.07.1966-07.10.1966 59 12.84
8 09.04.1970-26.05.1970 34 20.35
9 24.10.1974-04.10.1974 52 27.45
10 02.10.1987-19.10.1987 12 34.16
11 17.07.1990-23.08.1990 28 17.21
12 01.10.1997-21.10.1997 15 12.43
13 17.08.1998-31.08.1998 11 18.44
14 14.08.2002-01.10.2002 34 19.52
15 16.10.2008-15.12.2008 42 30.21
16 09.08.2011-22.09.2011 32 11.94
17 18.08.2015-25.08.2015 6 10.53
18 29.12.2015-20.01.2016 16 11.02
19 03.12.2018-24.12.2018 15 15.62
From the figures below we can see that our parameters start to decrease in crisis
states. Such abnormal behavior can serve as indicator or precursor of crashes and
critical states.
For the first time, the use of dynamic indicators, precursors of crashes in stock
markets using the parameters of a α-stable distribution, was proposed by us in the works
[30, 31] and later repeated in a recent work [32]. Moreover, the authors [32], analyzing
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only one crisis of 2008 and using a limited set of stock market indices (only three),
conclude that the β parameter is an even more convincing indicator of the approaching
crisis. Our data for a large set of critical events and crashes, as well as stock indices of
countries of different levels of development convincingly speak in favor of the α
parameter.
Interesting are the conclusions that follow from the analysis of Table 1. Indeed, the
indexes of stock markets, ordered by the value of the α parameter, reveal a characteristic
pattern that large α parameters correspond to more advanced stock markets of
developed countries. At the same time, the β asymmetry parameter also differs
markedly from zero. For emerging and emerging markets, the α parameter is noticeably
smaller, and the β parameter tends to zero.
In our opinion, this indicates that crises in emerging markets occur more often, are
more profound and long lasting. This leads to a decrease in the α parameter (see Fig. 3a,
c) and leveling of the distribution asymmetry, with the result that the β tends to zero.
a) b)
c) d)
Fig. 3. The corresponding time series and estimated for them parameters α (a, c) and β (b, d).
Vertical arrows indicate crashes and critical events.
6 Conclusions
Recently, there has been an increasing of interest in the study of quantitative methods
for the stability of financial objects, especially in crisis situations. It is extremely
important to take precisely preventive measures to prevent significant financial losses.
431
In this respect, an important role is played by the methods of constructing indicators
of crisis phenomena, which warn in advance of a possible approaching crisis, that
makes them as indicators-precursors of possible crisis states.
Crises manifest themselves in the form of strong price fluctuations of most assets
and financial market instruments. In particular, stock market indexes exhibit increased
volatility, which is reflected through the appearance of long tails in non-Gaussian
probability density functions.
This paper has examined the behaviors of stock markets price fluctuations. As many
others results, our research have demonstrated that the fluctuation distribution of DJIA
index over the long period of 1900-2019 are characterized by heavy tails and can be
described by the Levy’s stable parameters. A similar pattern is observed for other stock
indices taken over the shorter period from 2000 to 2019. Relating on theoretical
background of Levy’s stable distribution, stock markets time series and normalized log-
returns for stock index price, it have been obtained that the Gaussian distribution for
stock market is less suitable than Levy’s stable distribution.
Further, we have discussed different method for the parameters estimation of the
distribution, and pointed out which method is the best. Calculated parameters (α, β)
have presented a similar behavior for different crisis states and proved that they can be
used as indicators of crashes and critical periods. Moreover, it is shown that the absolute
values of the distribution parameters themselves characterize the degree of
development and efficiency of the stock market itself.
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