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

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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. BER’s survey question evaluating expectations on general Industrial Business Conditions • “Compared to the same period a year ago, do you expectnext quarter general business conditions to be” ? • The individual modular responses to each survey run are captured as :‘1’ for UP , ‘2’ for SAME and ‘3’ for DOWN

  8. 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 :

  9. Example : Relative survey period-on-period modular evaluation matrix over five survey runs

  10. 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

  11. Deriving Expectations Volatility & Expectations Realization Realization Volatility

  12. 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)

  13. 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)

  14. BER’s Industry survey question on general business conditions Expectations Volatility (EV) vs Expectations Realization (ER)

  15. GeneralBusiness Conditions :Comparison between Expectations Volatility & Realization

  16. New Orders :Expectations Volatility (EV) vs Expectations Realization (ER)

  17. New Orders :Comparison between Expectations Volatility & Realization

  18. Ascending order indications of Expectations Volatility

  19. Correlationsbetween Expectations Volatility & Realizations

  20. Causalitybetween 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

  21. Directional Causalitybetween Expectations Volatility & Realizations

  22. 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.

  23. 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

  24. 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

  25. Composite Uncertainty vs composite Expectations Realizations

  26. 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

  27. 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

  28. VOLATILITY AS AN INDICATOR OF UNCERTAINTY IN AND ITS IMPACT ON THE REALIZATION OF INDUSTRIAL BUSINESS EXPECTATIONS

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