Transcript BER • VOLATILITY AS AN INDICATOR OF UNCERTAINTY IN AND ITS IMPACT ON THE REALIZATION OF INDUSTRIAL BUSINESS EXPECTATIONS - Murray Pellissier * Stellenbosch University, South.

BER
• VOLATILITY AS AN INDICATOR OF
UNCERTAINTY IN AND ITS IMPACT ON
THE REALIZATION OF INDUSTRIAL
BUSINESS EXPECTATIONS
- Murray Pellissier
* Stellenbosch University, South Africa
VOLATILITY AS AN INDICATOR OF
UNCERTAINTY IN AND ITS IMPACT ON THE
REALIZATION OF INDUSTRIAL BUSINESS
EXPECTATIONS
Research Objectives :
• To provide additional information on
the elaboration of micro BTS data
• To derive survey expectations
volatility (uncertainty)
• To derive survey expectations
realizations
• To evaluate the impact of uncertainty
on the realizations of business
expectations
VOLATILITY AS AN INDICATOR OF
UNCERTAINTY IN AND ITS IMPACT ON THE
REALIZATION OF INDUSTRIAL BUSINESS
EXPECTATIONS
Keywords :
• Volatility
• Uncertainty
• Business Expectations
• Realization of Expectations
Volatility/Uncertainty
• Volatility is seen as the
quantification of historic
movements in business
expectations and radical
(true) ‘Uncertainty’ a
subjective situations linked
to the relevant ‘Volatility’
where no objective
classification is possible
Business Expectations
• Expectations can be described
as a subjective feeling or
perception about an incident to
happen in future
• One way to measure business
expectations on an ongoing
basis is to ask business people –
Business Tendency Surveys
BER’s survey on Industrial
Business Conditions
• The BER evaluates the cyclical
stance on business conditions
within the South African
Manufacturing sector, by
quarterly BT surveys, based on
the ex-post (survey quarter) and
ex-ante (forecast quarter)
survey questions
BER’s survey question evaluating
expectations on general Industrial
Business Conditions
• “Compared to the same period a year
ago, do you expect next quarter
general business conditions to be” ?
Up
Same
Down
• The individual modular responses to each
survey run are captured as :
‘1’ for UP , ‘2’ for SAME and ‘3’ for DOWN
Comparing relative survey
period-on-period changes in
micro survey data
• Movements in individual modular
responses over adjacent survey
periods can be classified in micro
data terms as :
R11
Up  Up
R12
Same  Up
R13
Down  Up
R21
Up  same
R22
Same  Same
R23
Down  Same
R31
Up  Down
R32
Same  Down
R33
Down  Down
Example : Relative survey periodon-period modular evaluation
matrix over five survey runs
Survey
Relative Percentage Changes
T : T-1
R11 R12 R13 R21 R22 R23 R31 R32 R33 Tot
2:1
2.5
4.1
1.6
3.3
45.9
11.5
1.6
14.8
14.8
100
3:2
1.5
4.6
3.1
4.6
48.9
9.9
0.8
9.9
16.8
100
4:3
8.5
1.9
0.9
7.5
45.3
7.5
1.9
9.4
17.0
100
5:4
5.2
1.0
0.0
5.2
44.8
14.6
2.1
11.5
15.6
100
BER’s survey questions
considered for industrial
expectations analysis
•
•
•
•
•
•
General Business Conditions
Volume of Production
Volume of Sales
Volume of New Orders
Fixed Investments
Purchasing Prices
Deriving Expectations Volatility &
Expectations Realization
Survey Question
Responses
Responses
Period T-1
Period T
Survey-Q
Survey-Q
Forecast-Q
Volatility
Forecast-Q
Evaluation of expectations volatility of
the BER’s micro survey data on
industrial business conditions
• By analyzing changes in micro
survey data in period T-1 (Forecast
Quarter) compared to period T
(Forecast Quarter), directional
movements in individual response
expectations (R12, R13, R21, R23,
R31 and R32) over the sample period
1992q3:2005q3 were aggregated as
Expectations Volatility (EV)
Evaluation of expectations
realizations of the BER’s micro survey
data on industrial business conditions
• By analyzing changes in micro
survey data in period T-1 (Forecast
Quarter) of individual response
expectations, compared to
directional realizations in period T
(Survey Quarter) of individual
responses estimations (R11, R22 and
R33) over the sample period
1992q3:2005q3 were aggregated as
Expectations Realization (ER)
BER’s Industry survey question on
general business conditions
Expectations Volatility (EV) vs Expectations Realization (ER)
60
80
grbcever
55
75
50
70
45
65
40
60
35
55
30
50
25
45
R = -0,85
20
94:1
96:1
98:1
00:1
02:1
BCEV(Expectations Volatility) ls
BCER(Expectations Realization) rs
04:1
40
General Business Conditions :
Comparison between Expectations Volatility &
Realization
Volatility
Realization
Obs
BCEV
BCER
1993
32.1
67.8
1994
30.6
68.0
1995
30.6
#
68.1 *
1996
40.1
61.4
1997
42.5
1998
33.0
59.4
1999
42.3
61.1
2000
47.3 *
51.9
2001
45.2
52.8
2002
45.2
57.0
2003
46.6
52.5
2004
45.4
52.7
Mean
40.1
58.7
Std.D
6.6
6.6
51.6
#
New Orders :
Expectations Volatility (EV) vs
Expectations Realization (ER)
65
68
grorever
60
64
55
60
50
56
45
52
40
48
35
44
30
40
R = -0,16
25
94:1
96:1
98:1
00:1
02:1
OREV(Expectations Volatility) ls
ORER(Expectations Realization) rs
04:1
36
New Orders :
Comparison between Expectations Volatility &
Realization
Volatility
Realization
Obs
OREV
ORER
1993
48.9
50.0
1994
45.3
61.1 *
1995
39.1
#
54.5
1996
43.2
55.3
1997
47.8
1998
49.6 *
57.8
1999
47.0
48.7
2000
48.0
51.8
2001
46.4
51.6
2002
45.6
53.4
2003
48.2
51.0
2004
46.8
50.6
Mean
46.3
52.8
Std.D
2.9
3.9
47.7
#
Ascending order indications
of Expectations Volatility
Expectations
BTS Evaluation
Volatility
Realization
EV
ER
1
IV (Investment)
32.4
68.1
2
PP (Prices)
36.9
64.4
3
BC (Buss Conditions)
40.1
58.7
4
PO (Production)
45.7
54.5
5
SL (Sales)
46.3
53.4
6
OR (Orders)
46.3
52.8
Mean
41.3
58.6
Correlations between Expectations
Volatility & Realizations
Relationship
R
t- value
Buss Conditions
BCEV : BCER
-0.85
-11.56
Fixed Investment
IVEV : IVER
-0.88
-13.04
New Orders
OREV : ORER
-0.16
-1.11
Production
POEV : POER
-0.69
-6.78
Prices
PPEV : PPER
-0.77
-8.43
Sales
SLEV : SLER
-0.75
-8.04
Evaluation
Causality between Expectations
Volatility & Realizations
• Granger causality analysis was
implemented to test the hypothesis,
which comes first during the forecast
survey assessment of an industrial
economic variable, prevailing
‘uncertainty’ or expected ‘realization’
of outcome
• Granger causality establishes
precedence and information content,
although it does not imply causality
in the more common use of the term
Directional Causality between
Expectations Volatility &
Realizations
Evaluation
Granger Causality
Buss Conditions
Uncertainty
Realization
Fixed Investment
Uncertainty
Realization
New Orders
Uncertainty
Realization
Production
Uncertainty
Realization
Prices
Uncertainty
Realization
Sales
Uncertainty
Realization
Research Findings
• That uncertainty does impact negatively on
the realizations of industrial business
expectations
• That directional causality from uncertainty,
to the corresponding realization of
expectations is noted in the case of
general business conditions, production
and sales
• That un-directional causality is noted in the
case of fixed investments and prices
• That strong feedback causality in the case
of new orders confirms that the directional
causality goes from realization of historic
expectations to prevailing uncertainty.
Component Factor Analysis of
expectations volatility variables
• The six EV variables can be reduced
to two main components
(Eigenvalues>’1’)
• Component1 is mainly loaded by New
orders, Production and Sales factors.
• Component2 is mainly loaded by Fixed
Investments and inverted Business
Conditions factors
• Component3 also loads relatively high
on Eigenvalues and mainly embraces
inverted Price factors
Composite Uncertainty Indicator
• Accepting Component1 of the
Factor Analysis as indicative of
an un-weighted composite
uncertainty (EV) indicator, a
similar expectations realizations
(ER) indicator was developed for
comparison reasons
Composite Uncertainty vs composite
Expectations Realizations
200
180
grpri never00
160
160
120
120
100
80
80
40
60
R = -0,78
0
94:1
96:1
98:1
Uncertainty(EV)
00:1
02:1
04:1
Realizations(ER)
40
Index '92=100
Index '92=100
140
Components of expectations
volatility variables
• Based on the fact that
Component1 only explains 50% of
the variance, the six EV variables
load quite differently in
comparison to each other and
has to be further investigated in
terms of weights in compiling an
acceptable composite
‘Uncertainty’ indicator
Conclusions
• It can be concluded that in the South
African Industrial case, prevailing
uncertainty surrounding business
expectations do impact negatively on
the realization of expectations
• The possibility exist to compile an
industrial business uncertainty
indicator, provided the relevant
component weights be further
analyzed
VOLATILITY AS AN INDICATOR OF
UNCERTAINTY IN AND ITS IMPACT
ON THE REALIZATION OF
INDUSTRIAL BUSINESS
EXPECTATIONS