scholarly journals THE PROPERTIES OF CYCLES IN SOUTH AFRICAN FINANCIAL VARIABLES AND THEIR RELATION TO THE BUSINESS CYCLE

2005 ◽  
Vol 73 (4) ◽  
pp. 694-709 ◽  
Author(s):  
WH BOSHOFF
Keyword(s):  
Business Cycle ◽  
South African ◽  
Financial Variables ◽  
The Business Cycle

From the Business Cycle to the Output Cycle: Predicting South African Economic Activity

2017 ◽  
Vol 41 (2) ◽  
pp. 111-133
Author(s):  
C. Vermeulen ◽  
F. Joubert ◽  
A. Bosch ◽  
J. Rossouw
Keyword(s):  
Business Cycle ◽  
South African ◽  
Economic Activity ◽  
The Business Cycle

scholarly journals Industrial Electricity Usage and Stock Returns

2017 ◽  
Vol 52 (1) ◽  
pp. 37-69 ◽  
Author(s):  
Zhi Da ◽  
Dayong Huang ◽  
Hayong Yun
Keyword(s):  
Growth Rate ◽  
Asset Pricing ◽  
Business Cycle ◽  
Stock Returns ◽  
Real Time ◽  
Risk Premium ◽  
Output Gap ◽  
Financial Variables ◽  
The Future ◽  
The Business Cycle

The growth rate of industrial electricity usage predicts future stock returns up to 1 year with an R2 of 9%. High industrial electricity usage today predicts low stock returns in the future, consistent with a countercyclical risk premium. Industrial electricity usage tracks the output of the most cyclical sectors. Our findings bridge a gap between the asset pricing literature and the business cycle literature, which uses industrial electricity usage to gauge production and output in real time. Industrial electricity growth compares favorably with traditional financial variables, and it outperforms Cooper and Priestley’s output gap measure in real time.

scholarly journals The Prediction of Business Cycle Phases: Financial Variables and International Linkages

2002 ◽  
Vol 182 ◽  
pp. 96-105 ◽  
Author(s):  
Denise R. Osborn ◽  
Marianne Sensier
Keyword(s):  
Stock Market ◽  
Business Cycle ◽  
European Countries ◽  
Financial Variables ◽  
The Third ◽  
The Us ◽  
International Linkages ◽  
The Business Cycle ◽  
The Uk

This paper discusses recent research at the Centre for Growth and Business Cycle Research on the prediction of the expansion and recession phases of the business cycle for the UK, US, Germany, France and Italy. Financial variables are important predictors in these models, with the stock market playing a key role in the US but not the European countries, including the UK. In contrast, international linkages are important for the European countries. Our models suggest that the US and German economies have now emerged from the recession of 2001, and that all five countries will be in expansion during the third quarter of this year.

The Dividend Term Structure

2019 ◽  
Vol 55 (3) ◽  
pp. 829-867 ◽  
Author(s):  
Jac Kragt ◽  
Frank de Jong ◽  
Joost Driessen
Keyword(s):  
Business Cycle ◽  
Term Structure ◽  
Factor Model ◽  
Market Returns ◽  
Short Term ◽  
Latent Factors ◽  
Stock Market Returns ◽  
Financial Variables ◽  
Limited Degree ◽  
The Business Cycle

We estimate a model for the term structure of discounted risk-adjusted dividend growth using prices of dividend futures for the Eurostoxx 50. A 2-factor model capturing short-term mean reversion within a year and a medium-term component reverting at the business-cycle horizon gives an excellent fit of these prices. Hence, investors update the valuation of dividends beyond the business cycle only to a limited degree. The 2-factor model, estimated on dividend futures data only, explains a large part of observed daily stock market returns. We also show that the 2 latent factors are related to various economic and financial variables.

scholarly journals Predicting turning points in the South African economy

2003 ◽  
Vol 6 (2) ◽  
pp. 289-303 ◽  
Author(s):  
Elna Moolman
Keyword(s):  
Business Cycle ◽  
South African ◽  
Turning Points ◽  
Economic Indicator ◽  
Macroeconomic Model ◽  
The South ◽  
Macroeconomic Models ◽  
Market Participants ◽  
The Business Cycle

Despite the existence of macroeconomic models and complex business cycle indicators, it would be beneficial to policymakers and market participants if they could look at one well-chosen indicator in predicting business cycle turning points. If one indicator accurately predicts business cycle turning points, it provides an easy way to confirm the predictions of macroeconomic models, or it can eliminate the need for a macroeconomic model if the interest is in the turning points and not in the levels of the business cycle. The objective of this paper is to investigate whether turning points of the South African business cycle can be predicted with only one economic indicator.

Is commercial bank lending in South Africa procyclical?

2018 ◽  
Vol 26 (2) ◽  
pp. 203-226
Author(s):  
Foluso Abioye Akinsola ◽  
Sylvanus Ikhide
Keyword(s):  
South Africa ◽  
Business Cycle ◽  
South African ◽  
Commercial Bank ◽  
Bank Lending ◽  
Demand Side ◽  
Content Type ◽  
Long Run ◽  
The Business Cycle ◽  
The Relationship

PurposeThis paper aims to examine the relationship between commercial bank lending and business cycle in South Africa. This paper attempts to know whether commercial bank lending in South Africa is procyclical.Design/methodology/approachThe model assumed that the lending behaviour is related to the business cycle. In this study, vector error correction model (VECM) is used to capture the relationship between bank lending and business cycle to accurately elicit the macroeconomic long-run relationship between business cycle and bank lending, as some banks might slow down bank lending due to some idiosyncratic factors that are not related to the downturn in the economy. This paper uses data from South African Reserve Bank for the period of 1990-2015 using VECM to understand the extent to which business cycle fluctuation can affect credit crunch in the financial system. The Johansen cointegration approach is used to ascertain whether there is indeed a long-run co-movement between credit growth and business cycle.FindingsResults from the VECM show that there are significant linkages among the variables, especially between credit to gross domestic product (GDP) and business cycle. The influence of business cycle is seen vividly after a period of four to five years, where business cycle explains 20 per cent of the variation in the credit to GDP. South African banks tend to change their lending behaviour during upturns and downturns. This result further confirms the assertion in theory that credit follows business cycle and can amplify credit crunch. The result shows that in the long run, fluctuations in the business cycle can influence the credit growth in South Africa.Research limitations/implicationsThe impulse analysis result shows that the impact of business cycle shock is very persistent and lasting. This also demonstrates that the shocks to the business cycle result have a persistent and long-lasting impact on credit. This study finds that commercial bank lending in South Africa is procyclical. It is suggested that the South African economy needs forward-looking policies that will mitigate the flow of credit to the real sector and at the same time ensure financial stability.Originality/valueMost research papers rarely distinguish between the demand side and supply side of credit procyclicality. This report is presented to develop an econometric model that will examine demand side procyclicality. This study adopts more realistic and novel methods that will help in explaining the relationship between bank lending and business cycle in South Africa, especially after the global financial crisis. This report is presented with a concise and detailed analysis and interpretation.

A BIVARIATE AUTOREGRESSIVE PROBIT MODEL: BUSINESS CYCLE LINKAGES AND TRANSMISSION OF RECESSION PROBABILITIES

2013 ◽  
Vol 18 (4) ◽  
pp. 838-862 ◽  
Author(s):  
Henri Nyberg
Keyword(s):  
Business Cycle ◽  
Probit Model ◽  
The United States ◽  
Financial Variables ◽  
Method Of Maximum Likelihood ◽  
Special Cases ◽  
Out Of Sample ◽  
Explicit Formulae ◽  
The Business Cycle ◽  
The U.S

I propose a new binary bivariate autoregressive probit model of the state of the business cycle. This model nests various special cases, such as two separate univariate probit models used extensively in the previous literature. The parameters are estimated by the method of maximum likelihood and forecasts can be computed by explicit formulae. The model is applied to predict the U.S. and German business cycle recession and expansion periods. Evidence of in-sample and out-of-sample predictability of recession periods by financial variables is obtained. The proposed bivariate autoregressive probit model allowing links between the recession probabilities in the United States and Germany turns out to outperform two univariate models.

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