>Magister Scientiae - MSc / Purpose:This research investigates the existence of long-term equilibrium relationships among the stock market indices of Brazil, Russia, India, China and South Africa (BRICS). It further investigates cointegrated stock pairs for possible implementation of statistical arbitrage trading techniques.Design:We utilize standard multivariate time series analysis procedures to inspect unit roots to assess stationarity of the series. Thereafter, cointegration is tested by the Johansen and Juselius (1990) procedure and the variables are interpreted by a Vector Error Correction Model (VECM). Statistical arbitrage is investigated through the pairs trading technique.Findings:The five stock indices are found to be cointegrated. Analysis shows that the cointegration rank among the variables is significantly influenced by structural breaks. Two pairs of stock variables are also found to be cointegrated. This guaranteed the mean reversion property necessary for the successful execution of the pairs trading technique. Determining the optimal spread threshold also proved to be highly significant with respect to the success of this trading technique.Value:This research seeks to expand on the literature covering long-run co-movements of the volatile emerging market indices. Based on the cointegration relation shared by the BRICS, the research also seeks to encourage risk taking when investing. We achieve this by showing the potential rewards that can be realized through employing appropriate statistical arbitrage trading techniques in these markets.

Thesis (PhD)--Stellenbosch University, 2013. / A persistent problem for hedge fund researchers presents itself in the form of inconsistent and diverse style classifications within and across database providers. For this paper, single-manager hedge funds from the Hedge Fund Research (HFR) and Hedgefund.Net (HFN) databases were classified on the basis of a common factor, extracted using the factor axis methodology. It was assumed that the returns of all sample hedge funds are attributable to a common factor that is shared across hedge funds within one classification, and a specific factor that is unique to a particular hedge fund. In contrast to earlier research and the application of principal component analysis, factor axis has sought to determine how much of the covariance in the dataset is due to common factors (communality). Factor axis largely ignores the diagonal elements of the covariance matrix and orthogonal factor rotation maximises the covariance between hedge fund return series. In an iterative framework, common factors were extracted until all return series were described by one common and one specific factor. Prior to factor extraction, the series was tested for autoregressive moving-average processes and the residuals of such models were used in further analysis to improve upon squared correlations as initial factor estimates. The methodology was applied to 120 ten-year rolling estimation windows in the July 1990 to June 2010 timeframe. The results indicate that the number of distinct style classifications is reduced in comparison to the arbitrary self-selected classifications of the databases. Single manager hedge funds were grouped in portfolios on the basis of the common factor they share. In contrast to other classification methodologies, these common factor portfolios (CFPs) assume that some unspecified individual component of the hedge fund constituents’ returns is diversified away and that single manager hedge funds should be classified according to their common return components. From the CFPs of single manager hedge funds, pure style indices were created to be entered in a multivariate autoregressive framework. For each style index, a Vector Error Correction model (VECM) was estimated to determine the short-term as well as co-integrating relationship of the hedge fund series with the index level series of a stock, bond and commodity proxy. It was postulated that a) in a well-diversified portfolio, the current level of the hedge fund index is independent of the lagged observations from the other asset indices; and b) if the assumptions of the Efficient Market Hypothesis (EMH) hold, it is expected that the predictive power of the model will be low. The analysis was conducted for the July 2000 - June 2010 period. Impulse response tests and variance decomposition revealed that changes in hedge fund index levels are partially induced by changes in the stock, bond and currency markets. Investors are therefore cautioned not to overemphasise the diversification benefits of hedge fund investments. Commodity trading advisors (CTAs) / managed futures, on the other hand, deliver diversification benefits when integrated with an existing portfolio. The results indicated that single manager hedge funds can be reliably classified using the principal factor axis methodology. Continuously re-balanced pure style index representations of these classifications could be used in further analysis. Extensive multivariate analysis revealed that CTAs and macro hedge funds offer superior diversification benefits in the context of existing portfolios. The empirical results are of interest not only to academic researchers, but also practitioners seeking to replicate the methodologies presented.

This study explores the dynamics between stock price and exchange rates through the cointegration methodology proposed by Herwartz and Luetkepohl (2011). Moreover, it consider the vector error correction model (VECM) with conditional heteroscedastic variance. And we use a feasible generalized least squares (FGLS) estimator to estimate the cointegrating vector. This paper analysis some Asian countries' data from 1997 to 2010. The evidence result suggests that Malaysia and Singapor's stock price and exchange rate are positively related. But Hong Kong's stock price is negatively related to exchange rate.

This thesis examines the validity of the cost of carry model for pricing FTSE100 futures contracts and the relationship between FTSE100 spot and futures markets during two sub-periods characterised by different market trading systems employed by the LSE and LIFFE. The empirical work is carried out using three approaches to econometric modeling: a basic VECM for spot and futures prices, a VECM extended with a DCCTGARCH framework to account for the conditional variance-covariance structure for spot and futures prices and a threshold VECM to capture regime-dependent spot-futures price dynamics. Overall, both the basic VECM and the DCC-TGARCH analysis suggest that there are deviations from the cost of carry relationship in the first sub-sample when transactions costs in both markets are relatively high but that the cost of carry relationship tends to be valid in the second sub-sample when transactions costs are lower. This is further confirmed by the evidence of higher conditional correlations between the two markets in the second sub-sample as compared with the first, using the DCC-TGARCH analysis. This implies that the no-arbitrage cost of carry relationship between spot and futures markets is more effectively maintained by index arbitrageurs in the second period when market conditions are closer to perfect market assumptions, and hence the cost of carry model could be more reasonably used as a benchmark for pricing stock index futures. The threshold VECM analysis depicts regime-dependent price dynamics between FTSE100 spot and futures markets and leads to some interesting and important findings: arbitrage may not be practicable under some market conditions, either because it is difficult to find counterparties for the arbitrage transactions, or because there is significant risk associated with arbitrage; as a result, the cost of carry model may not always be suitable for pricing stock index futures. Furthermore, the threshold values yielded from estimating the threshold VECM reflect the average transaction costs for most arbitrageurs that are more reliable and fair than subjective estimations.

Η παρούσα μελέτη εξετάζει τη δυναμική σχέση μεταξύ των τιμών των ζωοτροφών και παραγωγού, καταναλωτή για τέσσερα είδη κρέατος: μοσχάρι, χοιρινό, αρνί και κοτόπουλο. Η σχετική βιβλιογραφία δείχνει ότι πολλοί παράγοντες επιδρούν στις τιμές των αγροτικών προϊόντων αλλά οι τιμές των ζωοτροφών είναι ο κυριότερος. Αυτό συμβαίνει γιατί οι ζωοτροφές αποτελούν πρώτη ύλη για την παραγωγή κρέατος και κατ΄επέκταση θα επηρέασουν τις τιμές παραγωγού και καταναλωτή. Τα δεδομένα αποτελούνται από 279 μηνιαίες τιμές που εκτείνονται από τον Ιανουάριο 1990 έως τον Ιανουάριο 2013. Χρησιμοποιώντας Johansen cointegration tests, Granger causality tests και impulse response functions τα εμπειρικά αποτελέσματα επιβεβαιώνουν πως οι τιμές των ζωοτροφών, οι τιμές παραγωγού και οι τιμές καταναλωτή δεν είναι ανεξάρτητες μεταξύ τους. / The present paper studies the relationship among feed prices, producer prices and consumer prices of meat: beef, pork, poultry and lamb. The literature indicates that there are many factors which affect agricultural commodity prices but the feed prices are the main. This is why feed has a principal role in the production of meat and will affect producer and consumer prices. The data consists of 279 monthly observations extending from January 1990 to January 2013. Using Johansen cointegration tests, Granger causality tests and impulse response functions, the empirical findings confirm that feed prices, consumer prices and producer prices are interdependent.

Tourism is currently the third largest industry within South Africa. Many African countries, including South Africa, have the potential to achieve increased economic growth and development with the aid of the tourism sector. As tourism is a great earner of foreign exchange and also creates employment opportunities, especially low–skilled employment, it is identified as a sector that can aid developing countries to increase economic growth and development. Accurate forecasting of tourism demand is important due to the perishable nature of tourism products and services. Little research on forecasting tourism demand in South Africa can be found. The aim of this study is to forecast tourism demand (international tourist arrivals) to South Africa by making use of different causal models and to compare the forecasting accuracy of the causal models used. Accurate forecasts of tourism demand may assist policy–makers and business concerns with decisions regarding future investment and employment. An overview of South African tourism trends indicates that although domestic arrivals surpass foreign arrivals in terms of volume, foreign arrivals spend more in South Africa than domestic tourists. It was also established that tourist arrivals from Africa (including the Middle East), form the largest market of international tourist arrivals to South Africa. Africa is, however, not included in the empirical analysis mainly due to data limitations. All the other markets namely Asia, Australasia, Europe, North America, South America and the United Kingdom are included as origin markets for the empirical analysis and this study therefore focuses on intercontinental tourism demand for South Africa. A review of the literature identified several determinants of tourist arrivals, including income, relative prices, transport cost, climate, supply–side factors, health risks, political stability as well as terrorism and crime. Most researchers used tourist arrivals/departures or tourist spending/receipts as dependent variables in empirical tourism demand studies. The first approach used to forecast tourism demand is a single equation approach, more specifically an Autoregressive Distributed Lag Model. This relationship between the explanatory variables and the dependent variable was then used to ex post forecast tourism demand for South Africa from the six markets identified earlier. Secondly, a system of equation approach, more specifically a Vector Autoregressive Model and Vector Error Correction Model were estimated for each of the identified six markets. An impulse response analysis was undertaken to determine the effect of shocks in the explanatory variables on tourism demand using the Vector Error Correction Model. It was established that it takes on average three years for the effect on tourism demand to disappear. A variance decomposition analysis was also done using the Vector Error Correction Model to determine how each variable affects the percentage forecast variance of a certain variable. It was found that income plays an important role in explaining the percentage forecast variance of almost every variable. The Vector Autoregressive Model was used to estimate the short–run relationship between the variables and to ex post forecast tourism demand to South Africa from the six identified markets. The results showed that enhanced marketing can be done in origin markets with a growing GDP in order to attract more arrivals from those areas due to the high elasticity of the real GDP per capita in the long run and its positive impact on tourist arrivals. It is mainly up to the origin countries to increase their income per capita. Focussing on infrastructure development and maintenance could contribute to an increase in future tourist arrivals. It is evident that arrivals from Europe might have a negative relationship with the number of hotel rooms available since tourists from this region might prefer accommodation with a safari atmosphere such as bush lodges. Investment in such accommodation facilities and the marketing of such facilities to Europeans may contribute to an increase in arrivals from Europe. The real exchange rate also plays a role in the price competitiveness of the destination country. Therefore, in order for South Africa to be more price competitive, inflation rate control can be a way to increase price competitiveness rather than to have a fixed exchange rate. Forecasting accuracy was tested by estimating the Mean Absolute Percentage Error, Root Mean Square Error and Theil’s U of each model. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was estimated for each origin market as a benchmark model to determine forecasting accuracy against this univariate time series approach. The results showed that the Seasonal Autoregressive Integrated Moving Average model achieved more accurate predictions whereas the Vector Autoregressive model forecasts were more accurate than the Autoregressive Distributed Lag Model forecasts. Policy–makers can use both the SARIMA and VAR model, which may generate more accurate forecast results in order to provide better policy recommendations. / Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2011.

Tourism is currently the third largest industry within South Africa. Many African countries, including South Africa, have the potential to achieve increased economic growth and development with the aid of the tourism sector. As tourism is a great earner of foreign exchange and also creates employment opportunities, especially low–skilled employment, it is identified as a sector that can aid developing countries to increase economic growth and development. Accurate forecasting of tourism demand is important due to the perishable nature of tourism products and services. Little research on forecasting tourism demand in South Africa can be found. The aim of this study is to forecast tourism demand (international tourist arrivals) to South Africa by making use of different causal models and to compare the forecasting accuracy of the causal models used. Accurate forecasts of tourism demand may assist policy–makers and business concerns with decisions regarding future investment and employment. An overview of South African tourism trends indicates that although domestic arrivals surpass foreign arrivals in terms of volume, foreign arrivals spend more in South Africa than domestic tourists. It was also established that tourist arrivals from Africa (including the Middle East), form the largest market of international tourist arrivals to South Africa. Africa is, however, not included in the empirical analysis mainly due to data limitations. All the other markets namely Asia, Australasia, Europe, North America, South America and the United Kingdom are included as origin markets for the empirical analysis and this study therefore focuses on intercontinental tourism demand for South Africa. A review of the literature identified several determinants of tourist arrivals, including income, relative prices, transport cost, climate, supply–side factors, health risks, political stability as well as terrorism and crime. Most researchers used tourist arrivals/departures or tourist spending/receipts as dependent variables in empirical tourism demand studies. The first approach used to forecast tourism demand is a single equation approach, more specifically an Autoregressive Distributed Lag Model. This relationship between the explanatory variables and the dependent variable was then used to ex post forecast tourism demand for South Africa from the six markets identified earlier. Secondly, a system of equation approach, more specifically a Vector Autoregressive Model and Vector Error Correction Model were estimated for each of the identified six markets. An impulse response analysis was undertaken to determine the effect of shocks in the explanatory variables on tourism demand using the Vector Error Correction Model. It was established that it takes on average three years for the effect on tourism demand to disappear. A variance decomposition analysis was also done using the Vector Error Correction Model to determine how each variable affects the percentage forecast variance of a certain variable. It was found that income plays an important role in explaining the percentage forecast variance of almost every variable. The Vector Autoregressive Model was used to estimate the short–run relationship between the variables and to ex post forecast tourism demand to South Africa from the six identified markets. The results showed that enhanced marketing can be done in origin markets with a growing GDP in order to attract more arrivals from those areas due to the high elasticity of the real GDP per capita in the long run and its positive impact on tourist arrivals. It is mainly up to the origin countries to increase their income per capita. Focussing on infrastructure development and maintenance could contribute to an increase in future tourist arrivals. It is evident that arrivals from Europe might have a negative relationship with the number of hotel rooms available since tourists from this region might prefer accommodation with a safari atmosphere such as bush lodges. Investment in such accommodation facilities and the marketing of such facilities to Europeans may contribute to an increase in arrivals from Europe. The real exchange rate also plays a role in the price competitiveness of the destination country. Therefore, in order for South Africa to be more price competitive, inflation rate control can be a way to increase price competitiveness rather than to have a fixed exchange rate. Forecasting accuracy was tested by estimating the Mean Absolute Percentage Error, Root Mean Square Error and Theil’s U of each model. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was estimated for each origin market as a benchmark model to determine forecasting accuracy against this univariate time series approach. The results showed that the Seasonal Autoregressive Integrated Moving Average model achieved more accurate predictions whereas the Vector Autoregressive model forecasts were more accurate than the Autoregressive Distributed Lag Model forecasts. Policy–makers can use both the SARIMA and VAR model, which may generate more accurate forecast results in order to provide better policy recommendations. / Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2011.

Dividend policy is widely researched in financial management, but determining whether it affects the market price per share is difficult. There has been much published on the subject, which presented theories such as the Modigliani, Miller, Gordon, Lintner, Walter and Richardson propositions and the relevance and irrelevance theories. However, little research has been done on the impact of dividend policy on shareholders’ wealth while considering the short- and long-run effects. The Vector Error Correction Model (VECM) was used to describe the short-run and long-run dynamics or the adjustment of the cointegrated variables towards their equilibrium values in South Africa. This study attempts to explain the effect of dividend policy on the market price per share. A sample of 46 companies listed on the Johannesburg Securities Exchange (JSE) was selected for the period 1995-2010. Three variables were used, namely the market price per share, the dividend per share and the earnings per share. The market price per share was used as a proxy in measuring shareholders’ wealth and the dividend per share was used as a proxy in measuring the dividend policy. Fixed and random effects models were applied to panel data to determine the relation between dividend policy and market price per share. The fixed effects method was used to control the stable characteristics of the companies over a fixed period. The random effects model was applied when the companies’ characteristics differed. Results for both models indicated that dividend yield is positively related to market price per share, while earnings per share do not have a significant impact on the market price per share. To test the strength of the long-run relationship, the VECM was applied. The coefficient for dividend per share in the co-integrating equation was positive, while the coefficient for earnings per share was negative. This confirms previous research findings. The results suggest that there is a long-run relationship between dividend per share and market price per share. The Granger causality test indicates there is bi-directional Granger causality between market price per share and dividend per share in South Africa. Therefore dividend policy does have a significant long-run impact on the share price and therefore provides a signal about the company’s financial success. / Dissertation (MCom)--University of Pretoria, 2012. / Financial Management / Unrestricted

Thesis (M. Com.(Economics)) -- University of Limpopo, 2017 / The relationship between unemployment, exchange rate and inflation has been a subject of debate for many years. Given the fact that South Africa is faced with a very low economic growth rate, inflation rate which is likely to go beyond the upper band of 6 percent and a high level of unemployment, policy makers are often faced with the trade-off between unemployment and inflation rate in the country. The purpose of this study is to determine the relationship between exchange rate, unemployment and inflation in South Africa. The study employed Johansen cointegration procedures and the vector error correction model (VECM) to capture the relationship between the variables. The Engle-Granger causality test was also employed to analyse causality amongst the variables. The results of Johansen cointegration test indicate that there is a long-run equilibrium relationship between the variables. The VECM also confirmed the existence of short-run equilibrium relationship between the variables. The nature of the relationship indicates that there is a significant negative relationship between unemployment and inflation in South Africa. This implies that policy makers are been faced with the trade-off between these two variables. The results further indicate that inflation is positively related to exchange rate, meaning a depreciation of the Rand (South African currency) in the foreign exchange market will feed to inflation in the home country. Furthermore, it is also indicated that unemployment is positively related to exchange rate. Meaning, a depreciation of the Rand in the foreign exchange market increases the level of unemployment in South Africa. All the results appeared to be significant. Policies aimed at lowering unemployment and inflation rate are recommended. It is also recommended that policy makers in South Africa take measures to improve the quality of education, skills training and steps to increase the labour intensity of production.

90年代末東亞金融危機造成多國貨幣大幅貶值，銀行紛紛倒閉。基本上金融危機可分為通貨危機(Currency Crisis)與銀行危機(Banking Crisis)；通貨危機是指當年中任一季名目匯率貶值超過25%，且貶值幅度比前一季超過10個百分點。諸多實證文獻顯示，高估一國匯率為其通貨崩潰之先驅；又由於近年來新興國家快速開放資本市場，以致於成為危機之導火線。為分析此一現象，本文首先編製金融帳權數之新台幣實質有效匯率指數，並將東亞之台灣、印尼、韓國、菲律賓、泰國等五國之匯率、相對物價（各國與美國物價）、金融帳餘額等變數做共整合關係檢定，觀察三個變數的長期均衡關係，再將誤差項加入模型中，建構向量誤差模型。實證結果發現，金融帳與相對物價對匯率有顯著之影響力。 / The 1997 East Asian Crises had made exchange rate depreciations and bank bankruptcies. Broadly speaking, it can be divided into currency crisis and banking crisis. Nominal exchange rate of any season in a year, which is depreciated over 25% and 10% than last season, is called a currency crisis. Lots of papers demonstrate that overvaluation is a precursor of a currency crash. Furthermore, developing countries have opened capital markets so rapidly that it became the tinderbox of crises. To analyze the phenomenon, this thesis first compile Taiwan’s financial weighted real effective exchange rate index, then examine exchange rates, relative prices (compare to American consumer price index), and net financial account of Taiwan, Indonesia, Korea, Philippine, and Thailand with cointegrated test to identify the long run equilibrium relationships between variables; then adding error terms into models to estimates vector error correction model (VECM). The empirical results show that financial account and relative price influence exchange rate significantly.