We examine both in-sample and out-of-sample predictability of South African stock return using macroeconomic variables. We base our analysis on a predictive regression framework, using monthly data covering the in-sample period between 1990:01 and 1996:12, and the out-of sample period commencing...

This paper investigates the empirical relevance of structural breaks in forecasting stock return volatility using both in-sample and out-of-sample tests applied to daily returns of the Johannesburg Stock Exchange (JSE) All Share Index from 07/02/1995 to 08/25/2010. We find evidence of structural...

In this paper, we evaluate the role of using consumer price index (CPI) disaggregated data to improve the accuracy of inflation forecasts. Our forecasting approach is based on extracting the factors from the subcomponents of the CPI at the highest degree of disaggregation. The data set contains...

In this paper, we examine the predictive ability, both in-sample and the out-of-sample, for South African stock returns using a number of financial variables, based on monthly data with an in-sample period covering 1990:01 to 1996:12 and the out-of-sample period of 1997:01 to 2010:04. We use the...

Intraday data of 26 German stocks are used to investigate whether the information contained in trading volume and number of trades as well as in various specifications of overnight returns can improve one-step-ahead volatility forecasts. For this purpose, a HAR model of the realized range...

I investigate macro effects of higher bank capital requirements on the Norwegian economy and their use as a macroprudential policy instrument under Basel III. To this end, I develop a macroeconometric model where the capital adequacy ratio, lending rates, asset prices and credit interact with...

Forecasting poverty in the future is mostly a matter of forecasting economic growth. The objective of the study is to examine the inter-temporal link between growth and poverty in Pakistan, over the next 25years period i.e., from the years 2011 to 2035. The generalized version of variance...

The study provides evidence in favor of the price range as a proxy estimator of volatility in financial time series, in the cases that either intra-day datasets are unavailable or they are available at a low sampling frequency.

A spatial error model is classified as a geostatistical model or a weight matrix model on the basis of the method of specification of spatial autocorrelation in the disturbance. Specification errors cannot be assumed to be absent, and the robustness of alternative specifications is useful for...

Although many studies on the directional accuracy of forecasts by international organizations and professional forecasters have been scrutinized, little attention has been paid to forecasts by business leaders. In order to address this gap, we use directional tests to investigate whether...