modelling lon run relationship in finance

‘Introductory Econometrics for Finance’ © Chris Brooks 2008
1
Chapter 7
Modelling long-run relationship in finance

Stationarity and Unit Root Testing
Why do we need to test for Non-Stationarity?
• The stationarity or otherwise of a series can strongly influence its
behaviour and properties - e.g. persistence of shocks will be infinite for
nonstationary series
• Spurious regressions. If two variables are trending over time, a
regression of one on the other could have a high R2 even if the two are
totally unrelated
• If the variables in the regression model are not stationary, then it can
be proved that the standard assumptions for asymptotic analysis will
not be valid. In other words, the usual “t-ratios” will not follow a t-
distribution, so we cannot validly undertake hypothesis tests about the
regression parameters.

Value of R2 for 1000 Sets of Regressions of a
Non-stationary Variable on another Independent
Non-stationary Variable

Value of t-ratio on Slope Coefficient for 1000 Sets of
Regressions of a Non-stationary Variable on another
Independent Non-stationary Variable

Two types of Non-Stationarity
• Various definitions of non-stationarity exist
• In this chapter, we are really referring to the weak form or covariance
stationarity
• There are two models which have been frequently used to characterise
non-stationarity: the random walk model with drift:
yt =  + yt-1 + ut (1)
and the deterministic trend process:
yt =  + t + ut (2)
where ut is iid in both cases.

Stochastic Non-Stationarity
• Note that the model (1) could be generalised to the case where yt is an
explosive process:
yt =  + yt-1 + ut
where  > 1.
• Typically, the explosive case is ignored and we use  = 1 to
characterise the non-stationarity because
–  > 1 does not describe many data series in economics and finance.
–  > 1 has an intuitively unappealing property: shocks to the system
are not only persistent through time, they are propagated so that a
given shock will have an increasingly large influence.

Stochastic Non-stationarity: The Impact of Shocks
• To see this, consider the general case of an AR(1) with no drift:
yt = yt-1 + ut (3)
Let  take any value for now.
• We can write: yt-1 = yt-2 + ut-1
yt-2 = yt-3 + ut-2
• Substituting into (3) yields: yt = (yt-2 + ut-1) + ut
= 2yt-2 + ut-1 + ut
• Substituting again for yt-2: yt = 2(yt-3 + ut-2) + ut-1 + ut
= 3 yt-3 + 2ut-2 + ut-1 + ut
• Successive substitutions of this type lead to:
yt = T y0 + ut-1 + 2ut-2 + 3ut-3 + ...+ Tu0 + ut

The Impact of Shocks for
Stationary and Non-stationary Series
• We have 3 cases:
1. <1  T0 as T
So the shocks to the system gradually die away.
2. =1  T =1 T
So shocks persist in the system and never die away. We obtain:
as T
So just an infinite sum of past shocks plus some starting value of y0.
3. >1. Now given shocks become more influential as time goes on,
since if >1, 3>2> etc.





0
0
i
t
t u
y
y

Detrending a Stochastically Non-stationary Series
• Going back to our 2 characterisations of non-stationarity, the r.w. with drift:
yt =  + yt-1 + ut (1)
and the trend-stationary process
yt =  + t + ut (2)
• The two will require different treatments to induce stationarity. The second case
is known as deterministic non-stationarity and what is required is detrending.
• The first case is known as stochastic non-stationarity. If we let
yt = yt - yt-1
and L yt = yt-1
so (1-L) yt = yt - L yt = yt - yt-1
If we take (1) and subtract yt-1 from both sides:
yt - yt-1 =  + ut
yt =  + ut
We say that we have induced stationarity by “differencing once”.

Detrending a Series: Using the Right Method
• Although trend-stationary and difference-stationary series are both
“trending” over time, the correct approach needs to be used in each case.
• If we first difference the trend-stationary series, it would “remove” the
non-stationarity, but at the expense on introducing an MA(1) structure into
the errors.
• Conversely if we try to detrend a series which has stochastic trend, then we
will not remove the non-stationarity.
• We will now concentrate on the stochastic non-stationarity model since
deterministic non-stationarity does not adequately describe most series in
economics or finance.

Sample Plots for various Stochastic Processes:
A White Noise Process
-4
-3
-2
-1
0
1
2
3
4
1 40 79 118 157 196 235 274 313 352 391 430 469

A Random Walk and a Random Walk with Drift
-20
-10
0
10
20
30
40
50
60
70
1 19 37 55 73 91 109 127 145 163 181 199 217 235 253 271 289 307 325 343 361 379 397 415 433 451 469 487
Random Walk
Random Walk with Drift

A Deterministic Trend Process
-5
0
5
10
15
20
25
30
1 40 79 118 157 196 235 274 313 352 391 430 469

Autoregressive Processes with
differing values of  (0, 0.8, 1)
-20
-15
-10
-5
0
5
10
15
1 53 105 157 209 261 313 365 417 469 521 573 625 677 729 781 833 885 937 989
Phi=1
Phi=0.8
Phi=0

Definition of Non-Stationarity
• Consider again the simplest stochastic trend model:
yt = yt-1 + ut
or yt = ut
• We can generalise this concept to consider the case where the series
contains more than one “unit root”. That is, we would need to apply the
first difference operator, , more than once to induce stationarity.
Definition
If a non-stationary series, yt must be differenced d times before it becomes
stationary, then it is said to be integrated of order d. We write yt I(d).
So if yt  I(d) then dyt I(0).
An I(0) series is a stationary series
An I(1) series contains one unit root,
e.g. yt = yt-1 + ut

Characteristics of I(0), I(1) and I(2) Series
• An I(2) series contains two unit roots and so would require differencing
twice to induce stationarity.
• I(1) and I(2) series can wander a long way from their mean value and
cross this mean value rarely.
• I(0) series should cross the mean frequently.
• The majority of economic and financial series contain a single unit root,
although some are stationary and consumer prices have been argued to
have 2 unit roots.

How do we test for a unit root?
• The early and pioneering work on testing for a unit root in time series
was done by Dickey and Fuller (Dickey and Fuller 1979, Fuller 1976).
The basic objective of the test is to test the null hypothesis that  =1 in:
yt = yt-1 + ut
against the one-sided alternative  <1. So we have
H0: series contains a unit root
vs. H1: series is stationary.
• We usually use the regression:
yt = yt-1 + ut
so that a test of =1 is equivalent to a test of =0 (since -1=).

Different forms for the DF Test Regressions
• Dickey Fuller tests are also known as  tests: , , .
• The null (H0) and alternative (H1) models in each case are
i) H0: yt = yt-1+ut
H1: yt = yt-1+ut, <1
This is a test for a random walk against a stationary autoregressive process of
order one (AR(1))
ii) H0: yt = yt-1+ut
H1: yt = yt-1++ut, <1
This is a test for a random walk against a stationary AR(1) with drift.
iii) H0: yt = yt-1+ut
H1: yt = yt-1++t+ut, <1
This is a test for a random walk against a stationary AR(1) with drift and a
time trend.

Computing the DF Test Statistic
• We can write
yt=ut
where yt = yt- yt-1, and the alternatives may be expressed as
yt = yt-1++t +ut
with ==0 in case i), and =0 in case ii) and =-1. In each case, the
tests are based on the t-ratio on the yt-1 term in the estimated regression of
yt on yt-1, plus a constant in case ii) and a constant and trend in case iii).
The test statistics are defined as
test statistic =
• The test statistic does not follow the usual t-distribution under the null,
since the null is one of non-stationarity, but rather follows a non-standard
distribution. Critical values are derived from Monte Carlo experiments in,
for example, Fuller (1976). Relevant examples of the distribution are
shown in table 4.1 below





SE( )

Critical Values for the DF Test
The null hypothesis of a unit root is rejected in favour of the stationary alternative
in each case if the test statistic is more negative than the critical value.
Significance level 10% 5% 1%
C.V. for constant
but no trend
-2.57 -2.86 -3.43
C.V. for constant
and trend
-3.12 -3.41 -3.96
Table 4.1: Critical Values for DF and ADF Tests (Fuller,
1976, p373).

The Augmented Dickey Fuller (ADF) Test
• The tests above are only valid if ut is white noise. In particular, ut will be
autocorrelated if there was autocorrelation in the dependent variable of the
regression (yt) which we have not modelled. The solution is to “augment”
the test using p lags of the dependent variable. The alternative model in
case (i) is now written:
• The same critical values from the DF tables are used as before. A problem
now arises in determining the optimal number of lags of the dependent
variable.
There are 2 ways
- use the frequency of the data to decide
- use information criteria



 




p
i
t
i
t
i
t
t u
y
y
y
1
1 


Testing for Higher Orders of Integration
• Consider the simple regression:
yt = yt-1 + ut
We test H0: =0 vs. H1: <0.
• If H0 is rejected we simply conclude that yt does not contain a unit root.
• But what do we conclude if H0 is not rejected? The series contains a unit
root, but is that it? No! What if ytI(2)? We would still not have rejected. So
we now need to test
H0: ytI(2) vs. H1: ytI(1)
We would continue to test for a further unit root until we rejected H0.
• We now regress 2yt on yt-1 (plus lags of 2yt if necessary).
• Now we test H0: ytI(1) which is equivalent to H0: ytI(2).
• So in this case, if we do not reject (unlikely), we conclude that yt is at least
I(2).

The Phillips-Perron Test
• Phillips and Perron have developed a more comprehensive theory of
unit root nonstationarity. The tests are similar to ADF tests, but they
incorporate an automatic correction to the DF procedure to allow for
autocorrelated residuals.
• The tests usually give the same conclusions as the ADF tests, and the
calculation of the test statistics is complex.

Criticism of Dickey-Fuller and
Phillips-Perron-type tests
• Main criticism is that the power of the tests is low if the process is
stationary but with a root close to the non-stationary boundary.
e.g. the tests are poor at deciding if
=1 or =0.95,
especially with small sample sizes.
• If the true data generating process (dgp) is
yt = 0.95yt-1 + ut
then the null hypothesis of a unit root should be rejected.
• One way to get around this is to use a stationarity test as well as the
unit root tests we have looked at.

Stationarity tests
• Stationarity tests have
H0: yt is stationary
versus H1: yt is non-stationary
So that by default under the null the data will appear stationary.
• One such stationarity test is the KPSS test (Kwaitowski, Phillips,
Schmidt and Shin, 1992).
• Thus we can compare the results of these tests with the ADF/PP
procedure to see if we obtain the same conclusion.

Stationarity tests (cont’d)
• A Comparison
ADF / PP KPSS
H0: yt  I(1) H0: yt  I(0)
H1: yt  I(0) H1: yt  I(1)
• 4 possible outcomes
Reject H0 and Do not reject H0
Do not reject H0 and Reject H0
Reject H0 and Reject H0
Do not reject H0 and Do not reject H0

Cointegration: An Introduction
• In most cases, if we combine two variables which are I(1), then the
combination will also be I(1).
• More generally, if we combine variables with differing orders of
integration, the combination will have an order of integration equal to the
largest. i.e.,
if Xi,t  I(di) for i = 1,2,3,...,k
so we have k variables each integrated of order di.
Let (1)
Then zt  I(max di)
z X
t i i t
i
k


 ,
1

Linear Combinations of Non-stationary Variables
• Rearranging (1), we can write
where
• This is just a regression equation.
• But the disturbances would have some very undesirable properties: zt´ is
not stationary and is autocorrelated if all of the Xi are I(1).
• We want to ensure that the disturbances are I(0). Under what circumstances
will this be the case?


 
i
i
t
t
z
z
i k
   
1 1
2
, ' , ,...,
X X z
t i i t t
i
k
1
2
, , '
 



Definition of Cointegration (Engle & Granger, 1987)
• Let zt be a k1 vector of variables, then the components of zt are cointegrated
of order (d,b) if
i) All components of zt are I(d)
ii) There is at least one vector of coefficients  such that  zt  I(d-b)
• Many time series are non-stationary but “move together” over time.
• If variables are cointegrated, it means that a linear combination of them will
be stationary.
• There may be up to r linearly independent cointegrating relationships (where
r  k-1), also known as cointegrating vectors. r is also known as the
cointegrating rank of zt.
• A cointegrating relationship may also be seen as a long term relationship.

Cointegration and Equilibrium
• Examples of possible Cointegrating Relationships in finance:
– spot and futures prices
– ratio of relative prices and an exchange rate
– equity prices and dividends
• Market forces arising from no arbitrage conditions should ensure an
equilibrium relationship.
• No cointegration implies that series could wander apart without bound
in the long run.

Equilibrium Correction or Error Correction Models
• When the concept of non-stationarity was first considered, a usual
response was to independently take the first differences of a series of I(1)
variables.
• The problem with this approach is that pure first difference models have no
long run solution.
e.g. Consider yt and xt both I(1).
The model we may want to estimate is
 yt = xt + ut
But this collapses to nothing in the long run.
• The definition of the long run that we use is where
yt = yt-1 = y; xt = xt-1 = x.
• Hence all the difference terms will be zero, i.e.  yt = 0; xt = 0.

Specifying an ECM
• One way to get around this problem is to use both first difference and
levels terms, e.g.
 yt = 1xt + 2(yt-1-xt-1) + ut (2)
• yt-1-xt-1 is known as the error correction term.
• Providing that yt and xt are cointegrated with cointegrating coefficient
, then (yt-1-xt-1) will be I(0) even though the constituents are I(1).
• We can thus validly use OLS on (2).
• The Granger representation theorem shows that any cointegrating
relationship can be expressed as an equilibrium correction model.

Testing for Cointegration in Regression
• The model for the equilibrium correction term can be generalised to
include more than two variables:
yt = 1 + 2x2t + 3x3t + … + kxkt + ut (3)
• ut should be I(0) if the variables yt, x2t, ... xkt are cointegrated.
• So what we want to test is the residuals of equation (3) to see if they
are non-stationary or stationary. We can use the DF / ADF test on ut.
So we have the regression
with vt  iid.
• However, since this is a test on the residuals of an actual model, ,
then the critical values are changed.
 
u u v
t t t
 

 1

ut

Testing for Cointegration in Regression:
Conclusions
• Engle and Granger (1987) have tabulated a new set of critical values
and hence the test is known as the Engle Granger (E.G.) test.
• We can also use the Durbin Watson test statistic or the Phillips Perron
approach to test for non-stationarity of .
• What are the null and alternative hypotheses for a test on the residuals
of a potentially cointegrating regression?
H0 : unit root in cointegrating regression’s residuals
H1 : residuals from cointegrating regression are stationary

ut

Methods of Parameter Estimation in
Cointegrated Systems:
The Engle-Granger Approach
• There are (at least) 3 methods we could use: Engle Granger, Engle and Yoo,
and Johansen.
• The Engle Granger 2 Step Method
This is a single equation technique which is conducted as follows:
Step 1:
- Make sure that all the individual variables are I(1).
- Then estimate the cointegrating regression using OLS.
- Save the residuals of the cointegrating regression, .
- Test these residuals to ensure that they are I(0).
Step 2:
- Use the step 1 residuals as one variable in the error correction model e.g.
 yt = 1xt + 2( ) + ut
where = yt-1- xt-1
1
ˆ 
t
u
1
ˆ 
t
u

ut
ˆ

An Example of a Model for Non-stationary Variables:
Lead-Lag Relationships between Spot
and Futures Prices
Background
• We expect changes in the spot price of a financial asset and its corresponding
futures price to be perfectly contemporaneously correlated and not to be
cross-autocorrelated.
i.e. expect Corr(ln(Ft),ln(St))  1
Corr(ln(Ft),ln(St-k))  0  k
Corr(ln(Ft-j),ln(St))  0  j
• We can test this idea by modelling the lead-lag relationship between the two.
• We will consider two papers Tse(1995) and Brooks et al (2001).

Futures & Spot Data
• Tse (1995): 1055 daily observations on NSA stock index and stock
index futures values from December 1988 - April 1993.
• Brooks et al (2001): 13,035 10-minutely observations on the FTSE
100 stock index and stock index futures prices for all trading days in
the period June 1996 – 1997.

Methodology
• The fair futures price is given by
where Ft
* is the fair futures price, St is the spot price, r is a
continuously compounded risk-free rate of interest, d is the
continuously compounded yield in terms of dividends derived from the
stock index until the futures contract matures, and (T-t) is the time to
maturity of the futures contract. Taking logarithms of both sides of
equation above gives
• First, test ft and st for nonstationarity.
t
*
t
(r-d)(T-t)
F = S e
t)
-
d)(T
-
(r
s
f t
t 

*

Dickey-Fuller Tests on Log-Prices and Returns for High
Frequency FTSE Data
Futures Spot
Dickey-Fuller Statistics
for Log-Price Data
-0.1329 -0.7335
Dickey Fuller Statistics
for Returns Data
-84.9968 -114.1803

Cointegration Test Regression and Test on Residuals
• Conclusion: log Ft and log St are not stationary, but log Ft and log St are
stationary.
• But a model containing only first differences has no long run relationship.
• Solution is to see if there exists a cointegrating relationship between ft and
st which would mean that we can validly include levels terms in this
framework.
• Potential cointegrating regression:
where zt is a disturbance term.
• Estimate the regression, collect the residuals, , and test whether they are
stationary.

zt
t
t
t z
f
s 

 1
0 


Estimated Equation and Test for Cointegration for
High Frequency FTSE Data
Cointegrating Regression
Coefficient

0

1
Estimated Value
0.1345
0.9834
DF Test on residuals
t
ẑ
Test Statistic
-14.7303

Conclusions from Unit Root and Cointegration Tests
• Conclusion: are stationary and therefore we have a cointegrating
relationship between log Ft and log St.
• Final stage in Engle-Granger 2-step method is to use the first stage
residuals, as the equilibrium correction term in the general equation.
• The overall model is

zt

zt
t
t
t
t
t v
F
S
z
S 






 

 1
1
1
1
1
0 ln
ln
ˆ
ln 




Estimated Error Correction Model for
High Frequency FTSE Data
Look at the signs and significances of the coefficients:
• is positive and highly significant
• is positive and highly significant
• is negative and highly significant
Coefficient Estimated Value t-ratio

0
9.6713E-06 1.6083

 -8.3388E-01 -5.1298

1
0.1799 19.2886

1 0.1312 20.4946
1
̂
1
̂



Forecasting High Frequency FTSE Returns
• Is it possible to use the error correction model to produce superior
forecasts to other models?
Comparison of Out of Sample Forecasting Accuracy
ECM ECM-COC ARIMA VAR
RMSE 0.0004382 0.0004350 0.0004531 0.0004510
MAE 0.4259 0.4255 0.4382 0.4378
% Correct
Direction
67.69% 68.75% 64.36% 66.80%

Can Profitable Trading Rules be Derived from the
ECM-COC Forecasts?
• The trading strategy involves analysing the forecast for the spot return, and
incorporating the decision dictated by the trading rules described below. It is assumed
that the original investment is £1000, and if the holding in the stock index is zero, the
investment earns the risk free rate.
– Liquid Trading Strategy - making a round trip trade (i.e. a purchase and sale of
the FTSE100 stocks) every ten minutes that the return is predicted to be positive
by the model.
– Buy-&-Hold while Forecast Positive Strategy - allows the trader to continue
holding the index if the return at the next predicted investment period is positive.
– Filter Strategy: Better Predicted Return Than Average - involves purchasing the
index only if the predicted returns are greater than the average positive return.
– Filter Strategy: Better Predicted Return Than First Decile - only the returns
predicted to be in the top 10% of all returns are traded on
– Filter Strategy: High Arbitrary Cut Off - An arbitrary filter of 0.0075% is
imposed,

Trading Strategy Terminal
Wealth
( £ )
Return ( % )
{Annualised}
Terminal
Wealth (£)
with slippage
Return ( % )
{Annualised}
with slippage
Number
of trades
Passive
Investment
1040.92 4.09
{49.08}
1040.92 4.09
{49.08}
1
Liquid Trading 1156.21 15.62
{187.44}
1056.38 5.64
{67.68}
583
Buy-&-Hold while
Forecast Positive
1156.21 15.62
{187.44}
1055.77 5.58
{66.96}
383
Filter I 1144.51 14.45
{173.40}
1123.57 12.36
{148.32}
135
Filter II 1100.01 10.00
{120.00}
1046.17 4.62
{55.44}
65
Filter III 1019.82 1.98
{23.76}
1003.23 0.32
{3.84}
8
Spot Trading Strategy Results for Error Correction
Model Incorporating the Cost of Carry

Conclusions
• The futures market “leads” the spot market because:
• the stock index is not a single entity, so
• some components of the index are infrequently traded
• it is more expensive to transact in the spot market
• stock market indices are only recalculated every minute
• Spot & futures markets do indeed have a long run relationship.
• Since it appears impossible to profit from lead/lag relationships, their
existence is entirely consistent with the absence of arbitrage
opportunities and in accordance with modern definitions of the
efficient markets hypothesis.

The Engle-Granger Approach: Some Drawbacks
This method suffers from a number of problems:
1. Unit root and cointegration tests have low power in finite samples
2. We are forced to treat the variables asymmetrically and to specify one as
the dependent and the other as independent variables.
3. Cannot perform any hypothesis tests about the actual cointegrating
relationship estimated at stage 1.
- Problem 1 is a small sample problem that should disappear
asymptotically.
- Problem 2 is addressed by the Johansen approach.
- Problem 3 is addressed by the Engle and Yoo approach or the Johansen
approach.

• One of the problems with the EG 2-step method is that we cannot make
any inferences about the actual cointegrating regression.
• The Engle & Yoo (EY) 3-step procedure takes its first two steps from EG.
• EY add a third step giving updated estimates of the cointegrating vector
and its standard errors.
• The most important problem with both these techniques is that in the
general case above, where we have more than two variables which may be
cointegrated, there could be more than one cointegrating relationship.
• In fact there can be up to r linearly independent cointegrating vectors
(where r  g-1), where g is the number of variables in total.
The Engle & Yoo 3-Step Method

• So, in the case where we just had y and x, then r can only be one or
zero.
• But in the general case there could be more cointegrating relationships.
• And if there are others, how do we know how many there are or
whether we have found the “best”?
• The answer to this is to use a systems approach to cointegration which
will allow determination of all r cointegrating relationships -
Johansen’s method.
The Engle & Yoo 3-Step Method (cont’d)

• To use Johansen’s method, we need to turn the VAR of the form
yt = 1 yt-1 + 2 yt-2 +...+ k yt-k + ut
g×1 g×g g×1 g×g g×1 g×g g×1 g×1
into a VECM, which can be written as
yt =  yt-k + 1 yt-1 + 2 yt-2 + ... + k-1 yt-(k-1) + ut
where  = and
 is a long run coefficient matrix since all the yt-i = 0.
Testing for and Estimating Cointegrating Systems
Using the Johansen Technique Based on VARs



k
j
g
i I
1
)
(  




i
j
g
j
i I
1
)
( 

• Let  denote a gg square matrix and let c denote a g1 non-zero vector,
and let  denote a set of scalars.
•  is called a characteristic root or set of roots of  if we can write
 c =  c
gg g1 g1
• We can also write
 c =  Ip c
and hence
(  - Ig ) c = 0
where Ig is an identity matrix.
Review of Matrix Algebra
necessary for the Johansen Test

• Since c  0 by definition, then for this system to have zero solution, we
require the matrix (  - Ig ) to be singular (i.e. to have zero determinant).
  - Ig  = 0
• For example, let  be the 2  2 matrix
• Then the characteristic equation is
  - Ig 
Review of Matrix Algebra (cont’d)
 






5 1
2 4






 





 



      
5 1
2 4
1 0
0 1
0
5 1
2 4
5 4 2 9 18
2



   
( )( )

• This gives the solutions  = 6 and  = 3.
• The characteristic roots are also known as Eigenvalues.
• The rank of a matrix is equal to the number of linearly independent rows or
columns in the matrix.
• We write Rank () = r
• The rank of a matrix is equal to the order of the largest square matrix we
can obtain from  which has a non-zero determinant.
• For example, the determinant of  above  0, therefore it has rank 2.
Review of Matrix Algebra (cont’d)

• Some properties of the eigenvalues of any square matrix A:
1. the sum of the eigenvalues is the trace
2. the product of the eigenvalues is the determinant
3. the number of non-zero eigenvalues is the rank
• Returning to Johansen’s test, the VECM representation of the VAR was
yt =  yt-1 + 1 yt-1 + 2 yt-2 + ... + k-1 yt-(k-1) + ut
• The test for cointegration between the y’s is calculated by looking at the
rank of the  matrix via its eigenvalues. (To prove this requires some
technical intermediate steps).
• The rank of a matrix is equal to the number of its characteristic roots
(eigenvalues) that are different from zero.
The Johansen Test and Eigenvalues

• The eigenvalues denoted i are put in order:
1  2  ...  g
• If the variables are not cointegrated, the rank of  will not be
significantly different from zero, so i = 0  i.
Then if i = 0, ln(1-i) = 0
If the ’s are roots, they must be less than 1 in absolute value.
• Say rank () = 1, then ln(1-1) will be negative and ln(1-i) = 0
• If the eigenvalue i is non-zero, then ln(1-i) < 0  i > 1.
The Johansen Test and Eigenvalues (cont’d)

• The test statistics for cointegration are formulated as
and
where is the estimated value for the ith ordered eigenvalue from the
 matrix.
trace tests the null that the number of cointegrating vectors is less than
equal to r against an unspecified alternative.
trace = 0 when all the i = 0, so it is a joint test.
max tests the null that the number of cointegrating vectors is r against
an alternative of r+1.
The Johansen Test Statistics
 
max ( , ) ln(  )
r r T r
    
1 1 1






g
r
i
i
trace T
r
1
)
ˆ
1
ln(
)
( 


Decomposition of the  Matrix
• For any 1 < r < g,  is defined as the product of two matrices:
 = 
gg gr rg
•  contains the cointegrating vectors while  gives the “loadings” of
each cointegrating vector in each equation.
• For example, if g=4 and r=1,  and  will be 41, and yt-k will be
given by:
or
 
k
t
y
y
y
y































4
3
2
1
14
13
12
11
14
13
12
11








  k
t
y
y
y
y 















 4
14
3
13
2
12
1
11
14
13
12
11









• Johansen & Juselius (1990) provide critical values for the 2 statistics.
The distribution of the test statistics is non-standard. The critical
values depend on:
1. the value of g-r, the number of non-stationary components
2. whether a constant and / or trend are included in the regressions.
• If the test statistic is greater than the critical value from Johansen’s
tables, reject the null hypothesis that there are r cointegrating vectors
in favour of the alternative that there are more than r.
Johansen Critical Values

• The testing sequence under the null is r = 0, 1, ..., g-1
so that the hypotheses for trace are
H0: r = 0 vs H1: 0 < r  g
H0: r = 1 vs H1: 1 < r  g
H0: r = 2 vs H1: 2 < r  g
... ... ...
H0: r = g-1 vs H1: r = g
• We keep increasing the value of r until we no longer reject the null.
The Johansen Testing Sequence

• But how does this correspond to a test of the rank of the  matrix?
• r is the rank of .
•  cannot be of full rank (g) since this would correspond to the original
yt being stationary.
• If  has zero rank, then by analogy to the univariate case, yt depends
only on yt-j and not on yt-1, so that there is no long run relationship
between the elements of yt-1. Hence there is no cointegration.
• For 1 < rank () < g , there are multiple cointegrating vectors.
Interpretation of Johansen Test Results

Hypothesis Testing Using Johansen
• EG did not allow us to do hypothesis tests on the cointegrating relationship
itself, but the Johansen approach does.
• If there exist r cointegrating vectors, only these linear combinations will be
stationary.
• You can test a hypothesis about one or more coefficients in the
cointegrating relationship by viewing the hypothesis as a restriction on the
 matrix.
• All linear combinations of the cointegrating vectors are also cointegrating
vectors.
• If the number of cointegrating vectors is large, and the hypothesis under
consideration is simple, it may be possible to recombine the cointegrating
vectors to satisfy the restrictions exactly.

Hypothesis Testing Using Johansen (cont’d)
• As the restrictions become more complex or more numerous, it will
eventually become impossible to satisfy them by renormalisation.
• After this point, if the restriction is not severe, then the cointegrating
vectors will not change much upon imposing the restriction.
• A test statistic to test this hypothesis is given by
 2(m)
where,
are the characteristic roots of the restricted model
are the characteristic roots of the unrestricted model
r is the number of non-zero characteristic roots in the unrestricted model,
and m is the number of restrictions.
i
*
i






r
i
i
i
T
1
*)]
1
ln(
)
1
[ln( 


Cointegration Tests using Johansen:
Three Examples
Example 1: Hamilton(1994, pp.647 )
• Does the PPP relationship hold for the US / Italian exchange rate -
price system?
• A VAR was estimated with 12 lags on 189 observations. The Johansen
test statistics were
r max critical value
0 22.12 20.8
1 10.19 14.0
• Conclusion: there is one cointegrating relationship.

Example 2: Purchasing Power Parity (PPP)
• PPP states that the equilibrium exchange rate between 2 countries is
equal to the ratio of relative prices
• A necessary and sufficient condition for PPP is that the log of the
exchange rate between countries A and B, and the logs of the price
levels in countries A and B be cointegrated with cointegrating vector
[ 1 –1 1] .
• Chen (1995) uses monthly data for April 1973-December 1990 to test
the PPP hypothesis using the Johansen approach.

Cointegration Tests of PPP with European Data
Tests for
cointegration between
r = 0 r  1 r  2 1 2
FRF – DEM 34.63* 17.10 6.26 1.33 -2.50
FRF – ITL 52.69* 15.81 5.43 2.65 -2.52
FRF – NLG 68.10* 16.37 6.42 0.58 -0.80
FRF – BEF 52.54* 26.09* 3.63 0.78 -1.15
DEM – ITL 42.59* 20.76* 4.79 5.80 -2.25
DEM – NLG 50.25* 17.79 3.28 0.12 -0.25
DEM – BEF 69.13* 27.13* 4.52 0.87 -0.52
ITL – NLG 37.51* 14.22 5.05 0.55 -0.71
ITL – BEF 69.24* 32.16* 7.15 0.73 -1.28
NLG – BEF 64.52* 21.97* 3.88 1.69 -2.17
Critical values 31.52 17.95 8.18 - -
Notes: FRF- French franc; DEM – German Mark; NLG – Dutch guilder; ITL – Italian lira; BEF –
Belgian franc. Source: Chen (1995). Reprinted with the permission of Taylor and Francis Ltd.
(www.tandf.co.uk).

Example 3: Are International
Bond Markets Cointegrated?
• Mills & Mills (1991)
• If financial markets are cointegrated, this implies that they have a
“common stochastic trend”.
Data:
• Daily closing observations on redemption yields on government bonds for
4 bond markets: US, UK, West Germany, Japan.
• For cointegration, a necessary but not sufficient condition is that the yields
are nonstationary. All 4 yields series are I(1).

Testing for Cointegration Between the Yields
• The Johansen procedure is used. There can be at most 3 linearly independent
cointegrating vectors.
• Mills & Mills use the trace test statistic:
where i are the ordered eigenvalues.






g
r
i
i
trace T
r
1
)
ˆ
1
ln(
)
( 

Johansen Tests for Cointegration between International Bond Yields
Test statistic Critical Values
r (number of cointegrating
vectors under the null hypothesis) 10% 5%
0 22.06 35.6 38.6
1 10.58 21.2 23.8
2 2.52 10.3 12.0
3 0.12 2.9 4.2
Source: Mills and Mills (1991). Reprinted with the permission of Blackwell Publishers.

Testing for Cointegration Between the Yields
(cont’d)
• Conclusion: No cointegrating vectors.
• The paper then goes on to estimate a VAR for the first differences of the
yields, which is of the form
where
They set k = 8.
X
X US
X UK
X WG
X JAP
t
t
t
t
t
i
i i i i
i i i i
i i i i
i i i i
t
t
t
t
t







































( )
( )
( )
( )
, ,

   
   
   
   
11 12 13 14
21 22 23 24
31 32 33 34
41 42 43 44
1
2
3
4







 




k
i
t
i
t
i
t X
X
1


Variance Decompositions for VAR
of International Bond Yields
Variance Decompositions for VAR of International Bond Yields
Explained by movements in
Explaining
movements in
Days
ahead US UK Germany Japan
US 1 95.6 2.4 1.7 0.3
5 94.2 2.8 2.3 0.7
10 92.9 3.1 2.9 1.1
20 92.8 3.2 2.9 1.1
UK 1 0.0 98.3 0.0 1.7
5 1.7 96.2 0.2 1.9
10 2.2 94.6 0.9 2.3
20 2.2 94.6 0.9 2.3
Germany 1 0.0 3.4 94.6 2.0
5 6.6 6.6 84.8 3.0
10 8.3 6.5 82.9 3.6
20 8.4 6.5 82.7 3.7
Japan 1 0.0 0.0 1.4 100.0
5 1.3 1.4 1.1 96.2
10 1.5 2.1 1.8 94.6
20 1.6 2.2 1.9 94.2

Impulse Responses for VAR of
International Bond Yields
Impulse Responses for VAR of International Bond Yields
Response of US to innovations in
Days after shock US UK Germany Japan
0 0.98 0.00 0.00 0.00
1 0.06 0.01 -0.10 0.05
2 -0.02 0.02 -0.14 0.07
3 0.09 -0.04 0.09 0.08
4 -0.02 -0.03 0.02 0.09
10 -0.03 -0.01 -0.02 -0.01
20 0.00 0.00 -0.10 -0.01
Response of UK to innovations in
0 0.19 0.97 0.00 0.00
1 0.16 0.07 0.01 -0.06
2 -0.01 -0.01 -0.05 0.09
3 0.06 0.04 0.06 0.05
4 0.05 -0.01 0.02 0.07
10 0.01 0.01 -0.04 -0.01
20 0.00 0.00 -0.01 0.00
Response of Germany to innovations in
0 0.07 0.06 0.95 0.00
1 0.13 0.05 0.11 0.02
2 0.04 0.03 0.00 0.00
3 0.02 0.00 0.00 0.01
4 0.01 0.00 0.00 0.09
10 0.01 0.01 -0.01 0.02
20 0.00 0.00 0.00 0.00
Response of Japan to innovations in
0 0.03 0.05 0.12 0.97
1 0.06 0.02 0.07 0.04
2 0.02 0.02 0.00 0.21
3 0.01 0.02 0.06 0.07
4 0.02 0.03 0.07 0.06
10 0.01 0.01 0.01 0.04
20 0.00 0.00 0.00 0.01

modelling lon run relationship in finance

More Related Content

Similar to modelling lon run relationship in finance (20)

Recently uploaded (20)

modelling lon run relationship in finance

Editor's Notes