This thesis aims at analyzing the impact of water on economic growth and economic development. We explore different topics that are directly linked to the availability of water, which directly influence economic growth and development. The thesis consists of four studies. The first study models the effect of water utilization and water pollution on economic growth. The second study is based upon reflections on the fixed effects model and makes the distinction between the impact of the mean of a variable X and deviations from that mean on another variable Y. To date it has tended to be assumed that these impacts are the same; we argue that this is not always the case that countries can to an extent adjust to a specific water environment. However having adjusted they face problems when the water environment deviates from the mean. In the third study we explore the effect of different socio economic factors such as labour productivity, agricultural inputs, population density, water resources per land, and variables such as the trade regime, on water withdrawal for the agricultural and non-agricultural sectors. A specific focus is on the interactions between these two sectors. This study is new in its content and its theme of the work. We argue that many global trends will put increasing pressures on agricultural and non-agricultural water use. But there is also potential for increased efficiency in this use. The fourth study tries to fill the gap in the literature that deals with development aid for water and sanitation. We explore the impact of aid and aid volatility on safe access to water and sanitation, using a newly available OECD/DAC data base. Specifically, we analyse both the recipient countries and the donors to determine the role of aid in affecting safe access to water and sanitation.

This paper seeks to unconventionally maximize the volatility of a portfolio through a quartic optimization based on Markowitz’s modern portfolio theory, which generally seeks to do exactly the opposite. It shows that through this method, a daily leveraged exchange traded fund (ETF) strategy investigated by Posterro can be significantly improved upon in terms of its Sharpe ratio. The original strategy seeks to use a combination of momentum trading and tracking error in leveraged ETFs to trade during the last half an hour of the trading day, but it suffers in a low volatility market. By maximizing the volatility to take better advantage of tracking error and momentum, this problem is addressed by both increasing the mean daily return and significantly decreasing the variance of the strategy’s daily returns. GARCH forecasting is also implemented to assist in the maximization of the daily portfolios’ variances, though this does not prove to make a statistically significant difference in the strategy’s performance.

As pesquisas em finanças tem focado nos últimos anos a modelagem da variância devido à dificuldade de se obter boa previsão dos retornos na média, negligenciando o uso deste último, quanto informação necessária, no estabelecimento de estratégias de alocação de ativos em carteiras de renda variável. Seguindo DeMiguel, Nogales e Uppal (2014), esse trabalho adota o VAR como meio de se obter previsões dos retornos um passo à frente, e então, usá-las na alocação dos ativos. Para a inserção do VAR em finanças, foi permitido que os parâmetros variassem no tempo, o que conseguiu captar com sucesso a dinâmica volátil do mercado de ações. Ainda foram incorporadas técnicas bayesianas de estimação, a fim de driblar a sobreparametrização e obter estimações mais suavizadas, evitando deste modo que a variância das carteiras fossem muito altas. O método teve sucesso na aplicação e mostrou que o uso da previsão um passo à frente para os retornos pode ser usada como uma boa estratégia, expressa nos altos índices de Sharpe encontrados. / Research in finance has focused in recent years in variance modelling due to the difficulty of obtaining good forecasts of mean returns, neglecting the use of this latter on the establishment of asset allocation strategies in the equity portfolios. Following DeMiguel, Nogales e Uppal (2014), this work adopts the VAR as a way of obtaining forecasts of returns one step ahead and to get use of them in the allocation of assets. For insertion of the VAR in finance, the parameters was allowed to vary over time, which successfully captured the volatile dynamics of the stock market. Bayesian estimation techniques was incorporated in order to surmount overparameterization and to get more smoothed estimates, thereby preventing the variance of the portfolio to be very high. The method was successful in this application and showed that the making of one step ahead prediction of returns can be used as a good strategy, which can be expressed in high levels of Sharpe.

This thesis aims to back-test the ability of implied volatility carry trade strategies to outperform the carry trade strategies in the FX markets. Recent research has shown that the profitability of the strategies is partly attributable to the market mispricings of the forward volatility agreements and a tendency of the forward implied volatility to overestimate the future spot implied volatility. This thesis uses a similar approach to construct portfolios containing 10 developed as well as 9 emerging market currencies. Our approach is based on the assumption that Uncovered Interest rate Parity (UIP), Forward Unbiasedness Hypothesis (FUH) and Forward Volatility Unbiasedness Hypothesis (FVUH) do not hold and therefore providing investors with several opportunities to construct trading strategies taking advantage of these market mispricings. In this thesis, we show that the foreign exchange carry trade strategy composed of the specific developed and emerging country's currencies can be outperformed by portfolio consisting of the implied volatility carry trade strategies in the FX market over the analysed period. The portfolios are adjusted to the riskiness which is accounted for by the VIX and VXY-G7 index for developed and VIX and VXY-EM index for emerging economies. The strong performance of the strategies outlined in this thesis can be of significant value to FX traders and portfolio managers.

Thesis (M.M. (Finance & Investment))--University of the Witwatersrand, Faculty of Commerce, Law and Management, Graduate School of Business Administration, 2013. / Recent unexpected variability in the earnings of agribusinesses in South Africa has led stakeholders to ask as to why projected financial performance tended to be so different from the actual results achieved. This paper aims to make an empirical contribution to the discussion on the effects of soft commodity price volatility on the returns of entities whose major business involves derivatives trading in agricultural commodity products. Firstly, mathematical models for commodity price volatility are determined for the major agricultural commodities on the South African Futures Exchange (SAFEX) using the autoregressive conditional heteroskedasticity (ARCH) and the generalised autoregressive conditional heteroskedasticity (GARCH) type of approaches. Secondly, the study then seeks to ascertain whether there are causality links between the commodity price volatility and the returns or earnings realised by selected agribusinesses over time. The paper then discusses some trading strategies that are applicable given that commodity price volatility can be forecasted using the statistical models identified under the study.

The dynamic conditional correlation (DCC) model and its variants have been widely used in modeling the volatility of multivariate time series, with applications in portfolio construction and risk management. While popular for its simplicity, the DCC uses only two parameters to model the correlation dynamics, regardless of the number of assets. The flexible dynamic conditional correlation (FDCC) model attempts to remedy this by grouping the stocks into various clusters, each with its own set of parameters. However, it assumes the grouping is known apriori. In this thesis we develop a systematic method to determine the number of groups to use as well as how to allocate the assets to groups. We show through simulation that the method does well in identifying the groups, and apply the method to real data, showing its performance. We also develop and apply a Bayesian approach to this same problem. Furthermore, we propose an instantaneous measure of correlation that can be used in many volatility models, and in fact show that it outperforms the popular sample Pearson's correlation coefficient for small sample sizes, thus opening the door to applications in fields other than finance.

Thesis (M.Sc. (Statistics)) -- University of Limpopo. 2014 / A number of previous research studies have investigated volatility and financial risks in the ermeging markets. This dissertation investigates stock returns volatility and financial risks in the Johannesburg Stock Exchange (JSE). The investigation is con- ducted in modelling volatility using Autoregressive Moving Average-Generalised Au- toregressive Conditional Heteroskedastic (ARMA-GARCH)-type models. Daily data of the log returns at the JSE over the period 08 January, 2002 to 30 December, 2011 is used. The results suggest that daily returns can be characterised by an ARMA (1, 0) process. Empirical results show that ARMA (1, 0)-GARCH (1, 1) model achieves the most accurate volatility forecast. Modelling tail behaviour of rare and extreme events is an important issue in the risk management of a financial portfolio. Extreme Value Theory (EVT) is applied to quantify upper extreme returns. Generalised Ex- treme Value (GEV) distribution is used to model the behaviour of extreme returns. Empirical results show that the Weibull distribution can be used to model stock re- turns on the JSE. In using the Generalised Pareto Distribution (GPD), the modelling framework used accommodates ARMA and GARCH models. The GPD is applied to ARMA-GARCH filtered returns series and the model is referred to as the ARMA- GARCH-GPD model. The threshold value is estimated using Pareto quantile plot while peak-over-threshold approach is used to model the upper extreme returns. In general, the ARMA-GARCH-GPD model produces more accurate estimates of ex- treme returns than the ARMA-GARCH model. The out of sample forecast indicates that the ARMA (1, 3)-GARCH (1, 1) model provides the most accurate results.

This dissertation consists of three stand-alone essays on fundamental uncertainty, stock return volatility and earnings management. The first study investigates the role of information about firms?? fundamentals contained in analysts?? forecasts (which I label ??non-accounting information??) in understanding stock return volatility. When combined with Ohlson??s (1995) linear information dynamics, the accounting version of the Campbell-Shiller model (Campbell and Shiller 1988a, 1988b; Vuolteenaho 2002) implies that if current non-accounting information is more uncertain, then future stock returns are expected to be more volatile. The empirical evidence supports the theoretical predictions, and the results are valid for measures of both systematic and idiosyncratic volatility. Additional analysis yields some evidence that both favourable and unfavourable news from non-accounting information increases future stock return volatility. Overall, the results highlight the value relevance of information in analysts?? forecasts beyond what is contained in the current financial statements. The second essay extends the theoretical framework of Callen and Segal (2004) and Vuolteenaho (2002) to investigate the association between the uncertainty of accrual information and stock return volatility. The empirical evidence supports the theoretical prediction that the extent of uncertainty in accounting accruals is increasing with the volatility of future stock returns, and the results are valid for measures of both systematic and idiosyncratic volatility. However, when accrual variability is decomposed into fundamental and unexpected portions, I find that the positive relationship between accrual variability and future stock return volatility is dominated by the fundamental component of accrual variability. The findings therefore suggest that the market places little weight on information conveyed by that component of accounting accruals that is most likely to reflect accounting choices, implementation decisions and managerial opportunism. The final essay argues that the presumed articulation among accruals, cash flows and revenues does not capture decisions on expected accruals when large external financing activities are present. The analysis provides evidence that managers?? ??normal?? operating decisions associated with net external financing activities are likely to lead to measurement errors in unexpected accruals that are part of expected accruals, and erroneous conclusions that significant earnings management exists when in fact there is none. This is especially pertinent in cases where the partitioning variable used to identify instances of earnings management is supposed to be uncorrelated with external financing, when in fact it is correlated. The results underscore the importance of additional specification tests being conducted to control for estimation biases in unexpected accruals associated with external financing. I suggest the use of matched-firm approach using industry and external financing matches in order that reliable and warranted inferences are made.

Roughly all the previous empirical research, focusing on the information effects on volatility, has investigated the volatility dynamics during and after the release of public information. Researchers use ARCH-type or realized volatility models and they proxy public information by market news announcements. So far, studies focusing on the effect of noise or technical trading on volatility have been limited to theoretical results without any empirical evidence. Technical trading is trading based on technical signals. As a consequence, the aim of this dissertation is to answer to the following question: how does foreign exchange volatility behave, in the very short term, around public information and technical signals ? To answer to this question we study the volatility dynamics before, during and after public news announcements and technical chart pattern signals. In order to meet this objective, we implement different methodologies specific to the different chapters of the dissertation. Each chapter tries to answer to a sub-question emerging from the main question of the thesis. This thesis contributes to the empirical finance literature on intradaily exchange rate volatility as follows. First we present evidence that volatility increases in the pre-announcement period of scheduled news. Second, we show that foreign exchange dealers quoting activity reacts to news announcements and it conveys useful information. The third contribution consists in presenting a new approach to recognize technical chart patterns from a time series, and shedding light on the predictive success of the technical chart signals. Finally, the last contribution consists in the finding that technical signals, considered by economists as "noise", have a significant effect on volatility.

<p>This paper uses a new measure of volatility based on extreme day return occurrences and examines the relative prevailing volatility among worldwide stock markets during 1997-2009. Using several global stock market indexes of countries categorized as an emerging and developed capital markets are utilized. Additionally this study investigates well known anomalies namely Monday effect and January effect. Further using correlation analysis of co movement and extent of integration highlights the opportunities for international diversification among those markets. Evidences during this time period suggest volatility is not the only phenomena of emerging capital markets. Emerging markets offer opportunities of higher returns during volatility. Cross correlation analysis depicts markets have become more integrated during this time frame; still opportunities for higher returns prevail through global portfolio diversification.</p>