Professor Dimitris Kugiumtzis, Aristotle University of Thessaloniki, Greece, presented this workshop on time series analysis as part of the Summer School on Modern Statistical Analysis and Computational Methods hosted by the Social Sciences Computing Hub at the Whitaker Institute, NUI Galway on 17th-19th June 2013.

1. Time Series, Part 1
Content
- Stationarity, autocorrelation, partial autocorrelation, removal of non-
stationary components, independence test for time series
- Linear Stochastic Processes: autoregressive (AR), moving average (MA),
autoregressive moving average (ARMA)
- Fit of models AR, MA and ARMA to stationary time series
- Linear models for non-stationary time series
- Prediction of time series
- Nonlinear analysis of time series with stochastic models
- Nonlinear analysis of time series and dynamical systems
Literature
- “The Analysis of Time Series, An Introduction”, Chatfield C., Sixth edition,
Chapman & Hall, 2004
- “Introduction to time series and forecasting”, Brockwell P.J. and Davis R.A.,
Second edition, Springer, 2002
- “Non-Linear Time Series, A Dynamical System Approach”, Tong H., Oxford
University Press, 1993
- “Nonlinear Time Series Analysis”, Kantz H. and Schreiber T., Cambridge
University Press, 2004

2. Real world time series
mechanics
physiology
geophysics economy
univariate
time series
non-stationarity
noise
electronics only one time series
limited length

3. Definitions / notations
observed quantity  variable Χ
The observations take place most often at fixed time steps
 sampling time
The values of the observed quantity change with randomness (stochasticity)
at some small or larger degree  random variable (r.v.) Χ
For each time point t we consider the value xt of the r.v. Χ
The set of the values of xt over a time period n (given in units of the sampling
time)  (univariate) time series
  1 21
{ , , , }
n
t nt
x x x x

If there are simultaneous observations of more than one variable
 multivariate time series
We apply methods and techniques on the given univariate or multivariate
time series in order to get insight for the system that generates it
 time series analysis
The time series can be considered as realization of a
stochastic or deterministic process (dynamical system)  t t
X



4. Exchange index and volume of the Athens Stock Exchange (ASE)
86 88 90 92 94 96 98 00 02 04 06 08 10 12
0
1000
2000
3000
4000
5000
6000
7000
years
closeindex
ASE index, period 1985 - 2011
07 08 09 10 11 12
0
1000
2000
3000
4000
5000
6000
years
closeindex
ASE index, period 2007 - 2011
01 02 03 04 05 06 07 08 09 10 11
600
800
1000
1200
1400
1600
1800
months
closeindex
ASE index, period 2011
98 99 00 01 02 03 04 05 06 07 08
0
5
10
15
x 10
5
years
volume
ASE volume, period 1998 - 2008
Prediction?
Dynamical system ?
stochastic process ?
What is the index value tomorrow? The day after?
What is the mechanism of the
Greek stock market?

5. General Index of Consumer Prices (GICP)
01 02 03 04 05 06
100
105
110
115
120
125
years
GeneralIndexofComsumerPrices
General Index of Comsumer Prices, period Jan 2001 - Aug 2005
Trend ?
Seasonality / periodicity ?
Autocorrelation ?
Autoregression ?
Prediction ?

6. Annual sunspot numbers
1700 1750 1800 1850 1900 1950 2000
0
50
100
150
200
years
numberofsunspots
Annual sunspots, period 1700 - 2010
1900 1920 1940 1960 1980 2000
20
40
60
80
100
120
140
160
180
200
years
numberofsunspots
Annual sunspots, period 1900 - 2010
What will be the sunspot number in 2013, 2014 … ?
What is the mechanism / system / process
that generates sunspots?
Is it a periodic system + noise ?
Is it a stochastic system?
Is it a chaotic system?
1960 1970 1980 1990 2000 2010
0
50
100
150
200
years
numberofsunspots
Annual sunspots, period 1960 - 1995
Given the sunspot number for up to 1995,
what is the sunspot number in 1996?
and the years after?

7. 1995 2000 2005 2010 2015 2020
0
50
100
150
200
year
sunspotnumber
Genuine predictions of sunspot data
Model
comparison
Genuine
prediction

8. What is the generating system of a real time series?
100 200 300 400 500
periodic + noise
time in seconds
100 200 300 400 500
low dimensional chaos
time in seconds
100 200 300 400 500
high dimensional chaos
time in seconds
Candidate deterministic models
0 200 400
time index i
x(i)
stochastic
Candidate
stochastic
models
100 200 300 400 500
preictal EEG
time in seconds
Real time series
100 200 300 400 500
ictal EEG
time in seconds

9. Dripping water faucet
(original experiment at UC Santa Cruz).
The observation of the dripping faucet
shows that for some flow velocity the
drops do not run at constant time
intervals.
Crutchfield et al, Scientific American, 1986
3x1x 2x
2 1( , )x x
3 2( , )x x
The scatter diagram of the data
showed that the drop flow is not
random.
scatter
diagram 1( , )i ix x  1 2( , , )i i ix x x 
Hénon map
2
1 21 1.4 0.3i i is s s   
observed variable
i i ix s w  wi noise
chaos

10. 01 02 03 04 05 06
100
105
110
115
120
125
years
GeneralIndexofComsumerPricesGeneral Index of Comsumer Prices, period Jan 2001 - Aug 2005
Trend?
Seasonality / periodicity?
Autocorrelation ?
0 50 100 150 200 250 300 350 400 450 500
0
200
400
600
800
1000
1200
1400
time [10 min]
AEindex
Auroral Electrojet Index
Volatility ?
Non-stationarity

11. Variance stabilizing transformation
simple solution: log( )t tX Y ?
Power transform (Box-Cox):
1t
t
Y
X




?
λ Χt Var[yt]
-1
-0.5
0
0.5
Other transforms ?
0 50 100 150 200 250 300 350 400 450 500
0
200
400
600
800
1000
1200
1400
time [10 min]
AEindex
Auroral Electrojet Index
 1 2, , , ny y y
0 50 100 150 200 250 300 350 400 450 500
3
4
5
6
7
8
time [10 min]
AEindex
Logarithm transform of Auroral Electrojet Index
 1 2, , , nx x x
1
tY
1
tY
tY
4
tc
log( )tY 2
tc
tc
3
tc
Assumption: Var[Υt] changes as a
function of the trend μt
Transform Χt=T(Υt) that stabilizes
the variance of Υt ?
Var[ ] consttX 

12. 0 50 100 150 200 250 300 350 400 450 500
0
200
400
600
800
1000
1200
1400
time [10 min]
AEindex
Auroral Electrojet Index
0 50 100 150 200 250 300 350 400 450 500
3
4
5
6
7
8
time [10 min]
AEindex
Logarithm transform of Auroral Electrojet Index
-1000 -500 0 500 1000 1500
0
1
2
3
4
5
x 10
-3
x
f
X
(x)
y
normal
0 2 4 6 8 10
0
0.1
0.2
0.3
0.4
0.5
y
f
Y
(y)
x=log(y)
normal
 1 2, , , ny y y
log( )t tX Y
0 50 100 150 200 250 300 350 400 450 500
-4
-3
-2
-1
0
1
2
3
4
time [10 min]
AEindex
Gaussian transform of Auroral Electrojet Index
-4 -2 0 2 4
0
0.1
0.2
0.3
0.4
0.5
z
f
Z
(z)
x=-1
(FY
(y))
normal
 1
( )t Y tX F Y
 
?

13. Stationarity - trend
Plastic deformationdeterministic trend: a function of time μt = f(t)
Trend: slow change of the successive values of yt
82 85 87 90 92 95 97 00 02 05
0
200
400
600
800
1000
1200
1400
1600
years
index
S&P500
 1 2, , , ny y ytime series
stochastic trend: random slow change μt

14. Removal of trend t t tY X 
1. Deterministic trend :
known or estimated function of time μt = f(t)
Fit with first
degree
polynomial
Fit with fifth
degree
polynomial
Plastic deformation
t t tX Y  
{Xt} stationary
μt: mean value as
function of t (slowly
varying mean level)
Example: polynomial of degree p
0 1( ) p
t pf t a a t a t     

15. Index of the Athens Stock Exchange (ASE)
86 88 90 92 94 96 98 00 02 04 06 08 10 12
0
1000
2000
3000
4000
5000
6000
7000
years
closeindex
ASE index, period 1985 - 2011
orig
07 08 09 10 11 12
0
1000
2000
3000
4000
5000
6000
years
closeindex
ASE index, period 2007 - 2011
2. Stochastic trend
2α. Smoothing with moving average filter
86 88 90 92 94 96 98 00 02 04 06 08 10 12
-1000
0
1000
2000
3000
4000
5000
6000
7000
years
closeindex
ASE index, period 1985 - 2011
orig
local linear, 10 breakpoints
polynomial,p=20
Simple filter:
moving average
1
ˆ
2 1
q
t t j
j q
y
q
 



 2 1 3q   1 1
1 1 1
ˆ
3 3 3
t t t ty y y    
"2 1" 4q   ?
86 88 90 92 94 96 98 00 02 04 06 08 10 12
0
1000
2000
3000
4000
5000
6000
7000
years
closeindex
ASE index, period 1985 - 2011
orig
MA(31)
MA(151)
More general filter:
moving weighted average
ˆ
q
t j t j
j q
a y 

  1
q
j
j q
a


Simple moving average:
1
, , ,
2 1
ja j q q
q
  


16. 2b. Trend removal with differencing
If the trend is locally linear, it is removed by first differences:
0 1t a a t   1 1 1t t t t t t tY Y Y X X         
1 0 1 0 1 1( 1)t t a a t a a t a         constant!
If the trend is locally polynomial or degree p, it is removed by using
p
tY ?
08 10 12
-600
-400
-200
0
200
400
years
closeindex
ASE index: first differences, period 2007 - 2011
86 88 90 92 94 96 98 00 02 04 06 08 10 12
-600
-400
-200
0
200
400
years
closeindex
ASE index: first differences, period 2007 - 2011
Second order lag difference
2 2
1 2( ) (1 )(1 ) (1 2 ) 2t t t t t t tY Y B B Y B B Y Y Y Y             
One lag difference or first difference
1 (1 )t t t tY Y Y B Y     1t tBY YB: lag operator
[show first: ]!p
t tY p c X  

17. Which method for trend removal is best ?
08 10 12
0
1000
2000
3000
4000
5000
6000
years
closeindex
ASE index, period 2007 - 2011
orig
MA(31)
MA(151)
08 10 12
-1000
-500
0
500
1000
1500
2000
years
closeindex
ASE index detrended, period 2007 - 2011
MA(31)
MA(151)
08 10 12
0
1000
2000
3000
4000
5000
6000
years
closeindex
ASE index, period 2007 - 2011
orig
local linear, 10 breakpoints
polynomial,p=20
08 10 12
-1000
-500
0
500
1000
1500
2000
years
closeindex
ASE index detrended, period 2007 - 2011
local linear, 10 breakpoints
polynomial,p=20
08 10 12
-1000
-500
0
500
1000
1500
2000
years
closeindex ASE index: first differences, period 2007 - 2011
Estimation of trend

18. 82 85 87 90 92 95 97 00 02 05
0
200
400
600
800
1000
1200
1400
1600
years
index
S&P500
 1 2, , , ny y y time series 82 85 87 90 92 95 97 00 02 05
-100
-50
0
50
100
years
firstdifference
S&P500, first differences
1t t tx y y  
change of
the value
82 85 87 90 92 95 97 00 02 05
-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
years
relativechange
S&P500, relative changes
1t t
t
t
y y
x
y


relative
change of
the value
82 85 87 90 92 95 97 00 02 05
-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
years
differenceoflogs
S&P500, difference of logs
1ln lnt t tx y y  
change of the
logarithm of
the value
… more on differencing transform

19. Removal of seasonality
or periodicity
t t tY s X 
1. known or estimated periodic function st = f(t)
st: periodic function of
t with period d
Annual sunspots
1700 1750 1800 1850 1900 1950 2000
0
50
100
150
200
years
numberofsunspots
Annual sunspots, period 1700 - 2010
1900 1920 1940 1960 1980 2000
20
40
60
80
100
120
140
160
180
200
years
numberofsunspots
Annual sunspots, period 1900 - 2010
t t tX Y s  {Xt} stationaryPeriod d and
appropriate function st
?
2a. Estimation of si i=1,…,d from the averages for each component
Period d is known
1
1
ˆ
k
i i jd
j
s y
k


  /k n d
2b. Removal of periodicity using lag differences of order d (d-differencing)
(1 )d
d t t t d tY Y Y B Y    

20. Removal of trend and periodicity t t t tY s X  
1. Removal of trend t t t t tY Y s X   
2. Removal of periodicity t t t t t tX Y s Y s    
First remove trend and then
periodicity or vice versa ?
01 02 03 04 05 06
100
105
110
115
120
125
year
GICP
General Index of Comsumer Prices, period 1/2001-8/2005
01 02 03 04 05 06
-3
-2
-1
0
1
2
3
years
detrendedGICP
GICP: Residual of linear fit
01 02 03 04 05 06
-3
-2
-1
0
1
2
3
year
yearcycleofGICP
GICP: Year cycle estimate
01 02 03 04 05 06
-3
-2
-1
0
1
2
3
year
residualGICP
GICP: detrended and deseasoned
01 02 03 04 05 06
100
105
110
115
120
125
year
GICP
GICP: Linear fit
non-stationary  1 2, , , ny y y stationary  1 2, , , nx x x Is there information
in the residuals?
{Χt}: time series of residuals

21. Time correlation
tY
82 85 87 90 92 95 97 00 02 05
0
200
400
600
800
1000
1200
1400
1600
years
index
S&P500
: the value of the quantity
 1 2, , , ny y y time series
( )tYf y
0 500 1000 1500 2000
0
0.5
1
1.5
2
2.5
3
3.5
x 10
-3
Yt
f
Y
t
(y)
Gaussian pdf superimposed to S&P500
( )tXf x
-0.05 0 0.05
0
10
20
30
40
50
60
Xt
f
X
t
(x)
Gaussian pdf superimposed to S&P500 returns
Static description …
 marginal distribution
Dynamic description?
 Time correlation
Stochastic process
 t t
Y


 t t
X


82 85 87 90 92 95 97 00 02 05
-100
-50
0
50
100
years
firstdifference
S&P500, first differences
1t t tx y y  
change of
the value

22. Distribution and moments of a stochastic process
A stochastic process can be fully described in terms of the
marginal and joint probability distributions
( ) ( , )tY Yf y f y tt Z  marginal distribution
1 2
, 1 2 1 2 1 2( , ) ( , , , )t tY Y Yf y y f y y t t
1 2 3
, , 1 2 3 1 2 3 1 2 3( , , ) ( , , , , , )t t tY Y Y Yf y y y f y y y t t t
joint distribution of 2 r.v.
joint distribution of 3 r.v.
1 2,t t Z 
1 2 3, ,t t t Z 
…
The probability distribution and moments may change in time
First order moment (mean)   ( , )dt tYY yf y t y 


  
Second order moment
1 2 1 2 1 2 1 2 1 2 1 2( , , , )d d ( , )t t YY Y y y f y y t t y y t t
 
 
      
Higher order moments …
Central second order moment
1 1 2 2 1 21 2 1 2( , )( )( ) ( , )t t t t t t t tY Y t t           
autocovariance

23. Stationarity
The distributions do not change with time (equivalently, all moments are constant)
( ) ( , ) ( )t tY Y Yf y f y t f y t Z 
1 2 1 23
, , 1 2 3 , , 1 2 3( , , ) ( , , )tt tt t tY Y Y Y Y Yf y y y f y y y  

1 2,t t Z 
1 2 3, ,t t t Z 
Strict-sense stationarity
1 2
, 1 2 , 1 2( , ) ( , )t tt tY Y Y Yf y y f y y

constant
t Z 
for τ=0 2
(0)tY     constant variance
  
22 2 2
(0) (0)t tY Y           
Wide-sense stationarity
The first two moments are constant in time
 tY  
 1 2
, ( , ) ( )t tt tY Y Y Y t t    
       
constant
t Z 
1 2( , ) ( , ) ( )t t t t      
constant
- mean
- variance
- autocovariance

24. Autocorrelation
Stationary time series
Autocovariance       
2 2
( )( ) ( )( ) t t t t tX X X X X                 
Variance   
22 2 2
(0) (0)t tX X           
Autocorrelation
( )
)
)
0
(
(
 

  
Time correlation of variables of
at a lag τ.
Measures the “memory” of
 t t
X


 t t
X


 t t
X


(0) 1 
Notation: ( )    ( )   
0k 
Comments:
1k and
k k  k k and
Autocovariance matrix
01
201
110











n
n
n
n
Autocorrelation matrix
1 1
1 2
1
1
1
1
n
n
n
n
 
 




 

25. Basic stochastic processes
2
E i j ijX X     
white noise (WN), non-correlated r.v. t t
X


 t t
X


independent and identically distributed r.v. (iid)
)()()(),,,( 22112211 nnnn xXPxXPxXPxXxXxXP  
 E 0tX 
 E 0tX  2 2
E tX    
 1 0 1E | , , ,t t tY Y Y Y Y 
random walk (RW)
1 1 2t t t tY Y X X X X    
  1t t
Y


 t t
X


iid
 E 0tY  2 2
E tY t   
Variance increases linearly with time!
?
1
3
2

26. Chatfield C., “The Analysis of Time Series, An Introduction”, 6th edition, p. 38 (Chapter 3):
“Some authors prefer to make the weaker assumption that the zt’s are mutually uncorrelated,
rather than independent. This is adequate for linear, normal processes, but the stronger
independence assumption is needed when considering non-linear models (Chapter 11). Note
that a purely random process is sometimes called white noise, particularly by engineers.
p. 221 (Chapter 11):
When examining the properties of non-linear models, it can be very important to distinguish
between independent and uncorrelated random variables. In Section 3.4.1, white noise (or a
purely random process) was defined to be a sequence of independent and identically
distributed (i.i.d.) random variables. This is sometimes called strict white noise (SWN), and the
phrase uncorrelated white noise (UWN) is used when successive values are merely
uncorrelated, rather than independent. Of course if successive values follow a normal
(Gaussian) distribution, then zero correlation implies independence so that Gaussian
UWN is SWN. However, with non-linear models, distributions are generally non-normal and
zero correlation need not imply independence.
Wei W.W.C., “Time Series Analysis, Univariate and Multivariate Methods”, p. 15:
2.4 White Noise Processes
A process {at} is called a white noise process if it is a sequence of uncorrelated random
variables from a fixed distribution with constant mean (usually assumed 0), constant
variance and zero autocovariance for lags different from 0.
Uncorrelated (white noise) and independent (iid) observations

27.  t t
X


Gaussian (normal) stochastic process
For each order p: is p-dimensional Gaussian distribution
1 1
, , , 1 2( , , , )t t t p
X X X pf x x x   
Gaussian distribution is completely defined by the first two moments
strict stationarity ≡ weak stationarity
4
Example
sin( )tX A t  Stochastic process:
A r.v. E[ ] 0A  Var[ ] 1A 
~ [ , ]U   θ and A independent
E[ ] E[ ]E[sin( )] 0tX A t   
Is the process weak stationary?
2 1
E[ ] E sin( )sin( ( ) ) ... cos( )
2
t tX X A t t      
       
?
The first and second order moments do not depend on time t.

28. Sample autocovariance / autocorrelation
 1 2, , , nx x xtime series
1
1 n
t
t
xx
n 
 Sample mean
unbiased estimate of the mean μ of the time series ?
2 2
1
1
(0) ( )
n
t
t
c x x
n 
 
Another estimate of autocovariance
2
1
( )
1
( )
n
t t
t
x x x
n
c 


 
 
 

 
Biased estimates:
E[ ] ( )Var[ ]
n
c x
n n
  
 
 

  
E[ ] Var[ ]c x   
( )c c 
Notation
bias increases
with the lag τ
Sample autocovariance
2
1
( ))
1
(
n
t t
t
xc x x
n


 
 
  0,1, , 1n  
(
(
)
)
)
(0
c
r
c

 Sample autocorrelation (0) 1r  ( )r r 
Notation
~ N( ,Var[ ])r r  For large n:
2 2 21
Var[ ] ( 2 4 )m m m m m m
m
r
n
            

  

   
Bartlett
formula
21
Var[ ] m
m
r
n
 


  very large n

29. Autocorrelation for white noise
 1 2, , , nx x x white noise time series 0, 0   
1
~ N(0, )r
n
 ?
Test for independence
 1 2, , , nx x x
observed stationary time series
residual time series after trend
or periodicity removal
Are there
correlations ?
Is it iid ? Η0
Η0
Hypotheses
Η0: is iid 1 2, , , nx x x Η0: is white noise 1 2, , , nx x x
Statistical Significance test for autocorrelation
0H : 0  1H : 0 
Rejection region: 1 /2|
1/
t
r
R r z
n


 
  
 
for significance level 
Band of insignificant autocorrelation: 1 /2
1
az
n
 for =0.05
2
n

1
N(0, )r
n

white noise 1 2, , , nx x x

30. 0 5 10 15
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3

r()
GICP residual: autocorrelation
At significance level =0.05,
Η0 is rejected for τ=10
Is there any correlation in the GICP time series?
Significance test Η0:
for each independently
0 
Numerical Example
For a time series of 200 observations, the autocorrelation for τ=1,…,10 are:
1 2 3 4 5 6 7 8 9 10
-0.38 -0.28 0.11 -0.08 0.02 0.00 0.01 0.07 -0.08 0.05
Assume that the time series is purely random (Η0:ρ=0):
1
Var[ ] 0.005
200
r  
for =0.05, we expect 95% of autocorrelations to be in the interval
1
1.96 1.96 0.07 0.139
200
      
ρ1≠0, ρ2≠0 και ρτ≠0 για τ=3,4,…
Example of GICP

31. The Portmanteau significance test
A test for each lag 1, ,k 
0H : 0, 1, ,k  
Test statistic Q:
2
1
k
Q n r
 
 
2
1
( 2) / ( )
k
Q n n r n j
 
  
Box-Pierce
Ljung-Box
2
~ kQ rejection region  2
;1k aR Q  
0 5 10 15
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3

r()
GICP residual: autocorrelation
10k 
24.06Q 
2
;1 18.30k a 
H0 for τ=10
is rejected
0 5 10 15
0
5
10
15
20
25
30
35
k
Q(k)
GICP residual: Portmanteau (Ljung-Box)
One test for all lags together ?

32. 0 10 20 30 40 50 60 70 80 90 100
0
0.2
0.4
0.6
0.8
1
t
x(t) random time series
0 2 4 6 8 10
-0.3
-0.2
-0.1
0
0.1
0.2

r()
random time series: autocorrelation
0 10 20 30 40 50 60 70 80 90 100
0
0.2
0.4
0.6
0.8
1
t
x(t)
logistic time series
0 2 4 6 8 10
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2

r()
logistic time series: autocorrelation
An appropriate significance test ?

33. Is there correlation in the returns time series of the
ASE index (time period 2007-2011)?
07 08 09 10 11 12
0
1000
2000
3000
4000
5000
6000
years
closeindex
ASE index, period 2007 - 2011
0 2 4 6 8 10
0
5
10
15
20
k
Q(k)
ASE returns: Portmanteau (Ljung-Box)
sample Q
X2
(k,1)
0 2 4 6 8 10
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08

r()
ASE first differences: autocorrelation
0 2 4 6 8 10
0
5
10
15
20
k
Q(k)
ASE first differences: Portmanteau (Ljung-Box)
sample Q
X2
(k,1)
0 2 4 6 8 10
-0.08
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08

r()
ASE returns: autocorrelation
Is there correlation?
0 2 4 6 8 10
0
50
100
150
200
250
300
350
k
Q(k)
ASE square returns: Portmanteau (Ljung-Box)
sample Q
X2
(k,1)
0 2 4 6 8 10
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3

r()
ASE square returns: autocorrelation
07 08 09 10 11 12
-300
-200
-100
0
100
200
300
400
years
closeindex
ASE index: first differences, period 2007 - 2011
07 08 09 10 11 12
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
years
closeindex
ASE index: returns, period 2007 - 2011
What is the appropriate stationary time series:
first differences or returns ?
first
differences
returns
1t t tx y y  
1ln lnt t tx y y  
07 08 09 10 11 12
0
0.005
0.01
0.015
0.02
years
closeindex
ASE index: square returns, period 2007 - 2011
square of
returns
1ln lnt t tx y y 
  
2
( )t tx x
… nonlinear ? 2 2
E t tX X 
  