LONG MEMORY IN THE VOLATILITY OF AN EMERGING FIXED‐INCOME MARKET: EVIDENCE FROM SOUTH AFRICA

This paper tests for long memory in volatility of fixed‐income returns; specifically, South Africa's local currency 10‐year government bond, given that the characterisation of stochastic long‐memory volatility is of interest and importance in portfolio and risk management. The long‐memory parameter is estimated using methods based on wavelets, which have gained prominence in recent years. Evidence of long memory in fixed‐income return volatility is conclusively demonstrated across a variety of volatility measures and wavelet forms. This finding suggests a pattern of time dependence, which may potentially be exploited to generate improved volatility forecasting performance especially over long horizons. This paper further extends the extant literature by comparing the predictive power of long‐memory forecasts with those obtained from a standard (short‐memory) generalised autoregressive conditional heteroskedasticity (GARCH) process. The results of this exercise suggest that the information content of long‐memory models does not lead to improved forecast accuracy. The GARCH(1,1) model is shown to provide the best forecasts across most horizons (i.e. daily, weekly and monthly). Forecast performance is further revealed to be sensitive to the choice of volatility proxy used. Finally, the derived volatility forecasts are generally very close, and in some cases, almost indistinguishable.     

FORECASTING PERFORMANCE OF AN ESTIMATED DSGE MODEL FOR THE SOUTH AFRICAN ECONOMY

We construct a small open‐economy New Keynesian dynamic stochastic general equilibrium (DSGE) model for South Africa with nominal rigidities, incomplete international risk sharing and partial exchange rate pass‐through. The parameters of the model are estimated using Bayesian methods, and its out‐of‐sample forecasting performance is compared with Bayesian vector autoregression (VAR), classical VAR and random‐walk models. Our results indicate that the DSGE model generates forecasts that are competitive with those from other models, and it contributes statistically significant information to combined forecast measures.     

Re‐Examining the Role of Structural Change and Nonlinearities in a Phillips Curve Model for South Africa

Although studies generally find evidence of a Phillips curve‐type relationship in South Africa, uncertainty remains about the relevance of the model over a relatively long sample period, and whether conventional output gap measures are suitable proxies for demand pressure. This paper reviews research which shows that the Phillips curve model prevails over an extended sample, provided that the benchmark specifications include major structural changes in the balance‐of‐payments and labour market, and account for shifts in the root causes of inflation. When this is done, a linear specification with an output gap in levels correctly predicts the non‐trended inflation pattern over the period 1971(Q1)–1984(Q4), whereas a piecewise concave curve with an output gap in growth rates accurately forecasts the decelerating inflation pattern during 1986(Q1)–2001(Q2). A novel feature of the concave model is that it remains statistically robust and structurally stable when it is estimated until 2015(Q4). The concave model imparts a disinflationary bias, which suggests that monetary policy should be more expansionary during downswing phases of the business cycle and neutral during upswing phases. The analysis also considers how the shape of the Phillips curve might change if the balance‐of‐payments constraint on demand is relaxed in a significant way.     

Direct Evidence on Sticky Information from the Revision Behavior of Professional Forecasters

We provide evidence on the sticky‐information model of Mankiw and Reis ( 2002 ) by examining how often individual professional forecasters revise their forecasts. We draw interest rate and unemployment rate forecasts from the monthly Wall Street Journal surveys. We find evidence that forecasters frequently leave forecasts unchanged but revise more often the larger the changes in the information set; additionally, the information sensitivity of revision frequencies increased after 2007. We also find that, on average, forecasters in our sample revise more frequently than found in previous research but that revised forecasts are not consistently more accurate.     

A BVAR MODEL FOR THE SOUTH AFRICAN ECONOMY

The paper develops a Bayesian vector autoregressive (BVAR) model of the South African economy for the period of 1970:1‐2000:4 and forecasts GDP, consumption, investment, short‐term and long term interest rates, and the CPI. We find that a tight prior produces relatively more accurate forecasts than a loose one. The out‐of‐sample‐forecast accuracy resulting from the BVAR model is compared with the same generated from the univariate and unrestricted VAR models. The BVAR model is found to produce the most accurate out of sample forecasts. The same is also capable of correctly predicting the direction of change in the chosen macroeconomic indicators.     

Earnings Non‐Synchronicity and Voluntary Disclosure

Earnings non‐synchronicity reflects the extent to which firm‐specific factors determine a firm's earnings. Prior research suggests that high earnings non‐synchronicity impedes corporate outsiders' ability to process information. This study examines the impact of earnings non‐synchronicity on managers' decisions to provide earnings forecasts. We propose that high earnings non‐synchronicity motivates managers to issue earnings forecasts to reduce information asymmetry between managers and investors and to preempt costly information acquisition by outsiders. Consistently, we find a positive relation between earnings non‐synchronicity and managers' propensity to issue earnings forecasts, particularly long‐horizon forecasts. This positive relation is weaker when earnings are easier to predict based on the firm's earnings history and is stronger when the firm has higher institutional ownership and greater analyst following. We also find that the market's reaction to management forecasts increases with earnings non‐synchronicity. Overall, the evidence suggests that managers voluntarily provide earnings forecasts to alleviate the adverse consequences of earnings non‐synchronicity. These findings provide a more complete picture about the impact of earnings non‐synchronicity on a firm's information environment, and highlight the effect of the nature of information asymmetry on voluntary disclosures.     

An Examination of the Statistical Significance and Economic Relevance of Profitability and Earnings Forecasts from Models and Analysts

In this paper, we propose and empirically test a cross‐sectional profitability forecasting model which incorporates two major improvements relative to extant models. First, in terms of model construction, we incorporate mean reversion through the use of a two‐stage partial adjustment model and inclusion of a number of additional relevant determinants of profitability. Second, in terms of model estimation, we employ least absolute deviation (LAD) analysis instead of ordinary least squares because the former approach is able to better accommodate outliers. Results reveal that forecasts from our model are more accurate than three extant models at every forecast horizon considered and more accurate than consensus analyst forecasts at forecast horizons of two through five years. Further analysis reveals that LAD estimation provides the greatest incremental accuracy improvement followed by the inclusion of income subcomponents as predictor variables, and implementation of the two‐stage partial adjustment model. In terms of economic relevance, we find that forecasts from our model are informative about future returns, incremental to forecasts from other models, analysts’ forecasts, and standard risk factors. Overall, our results are important because they document the increased accuracy and economic relevance of a cross‐sectional profitability forecasting model which incorporates improvements to extant models in terms of model construction and estimation.     

Asymmetric Inventory Management and the Direction of Sales Changes*

We study manufacturing firms' asymmetric inventory investment in response to sales changes. Focusing on the costs of resource adjustment and stockout that likely differ in sales‐increasing and sales‐decreasing periods, we predict and find that inventory investment declines less during periods with sales decreases than it rises during periods with sales increases. We validate this claim by showing that managers' expectations of future demand and desire to avoid inventory stockouts are important determinants of this asymmetry. In addition, we find that asymmetric inventory investment provides useful information for predicting future sales growth, and that both managers' and analysts' sales forecasts are positively associated with the asymmetry. Lastly, we document that forecasts of future sales growth that incorporate asymmetric inventory investment are associated with lower absolute forecast errors than benchmark forecasts. Overall, we highlight the importance of inventory information in understanding managers' resource adjustment and utilization decisions that have implications for forecasting future demand. Our findings on asymmetric inventory management provide new insights to fundamental analysis based on inventory signals.     

FORECASTING THE SOUTH AFRICAN ECONOMY WITH VARs AND VECMs

The paper develops a Bayesian Vector Error Correction Model (BVECM) of the South African economy for the period 1970:1‐2000:4 and forecasts GDP, consumption, investment, short and long term interest rates, and the CPI. We find that a tight prior produces relatively more accurate forecasts than a loose one. The out‐of‐sample‐forecast accuracy resulting from the BVECM is compared with those generated from the Classical variant of the VAR and VECM and the Bayesian VAR. The BVECM is found to produce the most accurate out of sample forecasts. It also correctly predicts the direction of change in the chosen macroeconomic indicators.     

Forecasting French and German long-term rates using a rational expectations model

Forecasting French and German Long-Term Rates Using a Rational Expectations Model. — In this paper, the authors study a forecasting model for long-term rates based on the expectations hypothesis of the term structure. The long-term rate is expressed as an average of expected short-term rates, which are modelled using three models: two univariate models (with stationary and nonstationary rates) and one model in which the short-term rate terminal boundary is specified as a function of agents’ expectations. These approaches are used to forecast French and German long-term rates from 1960 to 1996. The authors find that the model based on agents’ expectations gives the best forecasts, especially for short horizons.     

FORECASTING THE SOUTH AFRICAN ECONOMY WITH GIBBS SAMPLED BVECMs

The paper uses the Gibbs sampling technique to estimate a heteroscedastic Bayesian Vector Error Correction Model (BVECM) of the South African economy for the period 1970:1‐2000:4, and then forecasts GDP, consumption, investment, short and long term interest rates, and the CPI over the period of 2001:1 to 2005:4. We find that a tight prior produces relatively more accurate forecasts than a loose one. The out‐of‐sample‐forecast accuracy resulting from the Gibbs sampled BVECM is compared with those generated from a Classical VECM and a homoscedastic BVECM. The homoscedastic BVECM is found to produce the most accurate out of sample forecasts.     

A time‐series analysis of Zimbabwe's corn sales to the Grain Marketing Board

This article considers methods of cointegration testing to construct a model of Zimbabwe ‘s corn sector. Corn production, sales of corn to the government, the price of corn, and price of beef are linked together in one long‐run equilibrium (cointegrating) relation. Only the price of beef is not weakly exogenous to perturbations in this relation. That is to say, when these variables are out of long‐run equilibrium, it is through subsequent changes in the price of beef that equilibrium is restored. The other variables do not respond to a long‐run disequilibrium. Short‐run forecasts from this model are compared with expert opinion forecasts made by the government's marketing board. Possibilities for improvement in long‐run forecasting and planning are discussed.     

The Macro Forecasting Record of the Indonesian Financial Authorities, 2004–15

A precondition for financial authorities’ macroeconomic effectiveness is a capacity to foresee economic conditions with reasonable accuracy. This article analyses the performance of Bank Indonesia (the central bank) and Indonesia’s Ministry of Finance in forecasting real GDP growth and consumer inflation over the last decade or so. It compares the quality of their forecasts with those of international institutions, aligning the timing of these forecasts to maximise comparability. Overall, the Indonesian authorities—especially the Ministry of Finance, in its revised central government budget (APBN-P)—perform respectably against their international comparators and there is no statistically significant bias in any of the forecasts. Still, there are mild indications of an optimistic bias in forecasts of inflation and growth, and all institutions tend to miss growth slowdowns. The results raise doubts about the value of fiscal policy for near-term macro stabilisation in Indonesia. Likewise, a wide range of uncertainty in forecasting inflation complicates the use of monetary policy for inflation targeting, at least one year ahead.     

Financial distress and non‐executive director compensation: Evidence from state‐owned enterprises in South Africa post King III

Using a sample of South African state‐owned enterprises (SOEs), we examine the pre‐ and post‐period impact of King III on non‐executive director (NED) compensation with emphasis on financially distressed SOEs. This paper adopts a difference‐in‐differences analysis technique with repeated measures as the basis for testing the hypotheses. The revised Altman Z‐score model which incorporates features unique to emerging markets is used to measure financial distress. Our findings indicate that SOEs that adopted King III will increase NED compensation when the firm has a positive performance and will severely penalize NED when the firm faces financial distress. This study highlights the importance of well‐crafted corporate governance policies. It further sheds light on the importance of King III and how its implementation may prove vital for the success of an enterprise.     

THE PREDICTABILITY OF STOCK MARKET RETURNS IN SOUTH AFRICA: PARAMETRIC VS. NON‐PARAMETRIC METHODS

This paper compares the forecasting performance of a sub‐class of univariate parametric and non‐parametric models in predicting stock market returns in South Africa. To account for conditional heteroskedasticity in stock returns data, the non‐parametric model is generated by the conditional heteroskedastic non‐linear autoregressive (NAR) model, while the parametric model is produced by the generalised autoregressive conditional heteroskedastic in mean (GARCH‐M) model. The results of the paper show that the NAR as a non‐parametric model performs better than the GARCH‐M model in short‐term forecasting horizon, and this indicates the importance of a distribution‐free model in predicting stock returns in South Africa.     

A Flexible Neuro‐Fuzzy Approach for Improvement of Seasonal Housing Price Estimation in Uncertain and Non‐Linear Environments

Changes in housing price affect both individuals and government since they have substantial influence on the socio‐economic conditions. Valuations of housing are necessary in order to assess the benefits and liabilities in housing sector. This study presents a flexible meta‐modelling approach for improvement of housing price estimation in ambiguous and complex environments. It is composed of artificial neural network (ANN) and fuzzy linear regression (FLR). Seven FLR models are considered to cover latest approaches and viewpoints. Also, ANN is applied to data sets. The preferred FLR model is selected via mean absolute percentage of error (MAPE) for further considerations, and then the preferred FLR model and the best structure of ANN are applied to the data set. Finally, the preferred model is selected based on MAPE. The intelligent approach of this study is applied for estimation and forecasting housing price in Iran. The housing price in Iran mainly is based on eight economic indices including currency, oil income, general index, house service pricing index, rate of informal market, gross domestic production in basic price, added value of oil group and construction materials price. FLR is identified as the preferred model with lowest MAPE for housing price forecasting in Iran. This shows that the housing market of Iran is associated with severe environmental fuzziness and ambiguity. This is the first study that introduces a flexible neuro‐fuzzy approach for improved estimation and forecasting of housing price in noisy, complex and uncertain environments.     

Revisiting Growth‐Poverty Relationship: A Medium‐Term Causality Approach

This article examines the potential medium‐term causal relationship between changes in Gross Domestic Product (GDP) per capita and poverty in developing countries during the 1970s–1990s. For this purpose, we use panel data model evaluation techniques to test the out‐of‐sample forecasting performance of competing models. We conclude that the evidence supports the hypothesis that increases in GDP per capita cause unidirectional poverty reduction, measured by the $1/day poverty rate, in the period 1970s–1980s. The results are similar when analysing low‐ and middle‐income countries and mid‐high‐ and very high‐inequality countries separately. However, in the period 1980s–1990s, it is only statistically significant for low‐income countries.     

Do Monetary Policy Announcements Affect Exchange Rate Returns and Volatility of Returns? Some Evidence from High‐Frequency Intra‐Day South African Data

This paper examines the temporal effect of domestic monetary policy surprises on both returns and volatility of returns of the South African rand/U.S. dollar exchange rate. The analysis in this “event study” proceeds using intra‐day minute‐by‐minute exchange rate data, repo rate data from the South African Reserve Bank's scheduled monetary policy announcements, and market consensus repo rate forecasts. A carefully selected sample over the period August 2003 to November 2017 ensures that the change in monetary policy is exogenous to the exchange rate. We find statistically and economically significant responses in intra‐day high‐frequency South African rand/U.S. dollar exchange rate returns and volatility of exchange rate returns to domestic interest rate surprises, but anticipated changes have no bearing on exchange rate returns and their volatility. The empirical results also indicate that there is an instantaneous response of the rand/dollar exchange rate to monetary policy surprises and that monetary policy news is an important determinant of the exchange rate until at least 42 minutes after the pronouncement – suggesting a high degree of market “efficiency” in its mechanical sense (although not necessarily in the deeper economic‐informational sense) in processing this information. Essentially, the asymmetric GARCH results exhibit no leverage effects – positive and negative information shocks have symmetric effects on conditional variance.     

The Short‐ and Long‐Run Determinants of Less‐Educated Immigrant Flows into U.S. States

We use a gravity model of migration and alternative estimation strategies to analyze how income differentials affect the flow of immigrants into U.S. states using annual data from the American Community Survey. We add to existing literature by decomposing income differentials into short‐ and long‐term components and by focusing on newly arrived less‐educated immigrants between 2000 and 2009. Our sample is unique in that the vast majority of our observations take zero values. Models that include observations with zero‐flow values find that recent male immigrants respond to differences in (short‐term) GDP fluctuations between origin countries and U.S. states, and perhaps to (long‐term) trend GDP differences as well. More specifically, GDP fluctuations pull less‐educated male immigrants into certain U.S. states, whereas GDP trends push less‐educated male immigrants out of their countries of origin. Effects for less‐educated women are less robust, as GDP coefficients tend to be much smaller than for men.     

Horizon‐Dependent Underreaction in Financial Analysts' Earnings Forecasts*

This paper provides empirical evidence that underreaction in financial analysts' earnings forecasts increases with the forecast horizon, and offers a rational economic explanation for this result. The empirical portion of the paper evaluates analysts' responses to earnings‐surprise and other earnings‐related information. Our empirical evidence suggests that analysts' earnings forecasts underreact to both types of information, and the underreaction increases with the forecast horizon. The paper also develops a theoretical model that explains this horizon‐dependent analyst underreaction as a rational response to an asymmetric loss function. The model assumes that, for a given level of inaccuracy, analysts' reputations suffer more (less) when subsequent information causes a revision in investor expectations in the opposite (same) direction as the analyst's prior earnings‐forecast revision. Given this asymmetric loss function, underreaction increases with the risk of subsequent disconfirming information and with the disproportionate cost associated with revision reversal. Assuming that market frictions prevent prices from immediately unraveling these analyst underreac‐tion tactics, investors buying (selling) stock on the basis of analysts' positive (negative) earnings‐forecast revisions also benefit from analyst underreaction. Therefore, the asymmetric cost of forecast inaccuracy could arise from rational investor incentives consistent with a preference for analyst underreaction. Our incentives‐based explanation for underreaction provides an alternative to psychology‐based explanations and suggests avenues for further research.