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Microfinance is considered to be a promising solution for poverty reduction and entrepreneurship development in the developing as well as developed economies. The central argument of the thesis is that microfinance services to poor entrepreneurial women helps in the development of their enterprise and entrepreneurial abilities. The thesis also investigates the role of microfinance in women’s economic empowerment, well-being of the family and further access to finance from commercial banks. The study is guided by the current state of knowledge and previous research on the impact of microfinance on women’s development. This empirical study is based on 300 questionnaire responses from women borrowers from microfinance institutions. The findings are triangulated with 50 interviews from women entrepreneurs and 14 interviews from loan officers and heads of microfinance providers to gain a deeper insight into finance constraints on women in Pakistan and to seek the lenders’ point of view. The findings suggest that women micro entrepreneurs can only access microfinance; and commercial bank loans are unavailable to them due to the lack of track record and collateral. Findings suggest that the injection of an optimal size of microcredit in female enterprises with training and mentoring facilities improves the profit of enterprise; this enhances the contribution to the household income that increases family welfare. The outcome on the well-being of the family is the same whether the loan is used for business or consumption resulting in the conclusion that the fungibility of the loan also benefits women borrowers. The argument that microfinance programme contributed to the development of entrepreneurial skills is not well supported by the empirical findings. Finally, evidence shows that the objectives of female economic empowerment and entrepreneurship are attained by those microfinance schemes that provide formal training, saving facilities and social benefits with credit in the package. The analysis discloses the issue of multi-borrowing in the semi urban areas of Punjab where there is a concentration of microfinance institutions with no centralised database of borrowers. These empirical results contribute to the wider microfinance literature by studying the relatively less researched developing economy of Pakistan. The study makes a theoretical and methodological contribution to the study of microfinance.

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This thesis studies price behaviour, the determinants of inflation, threshold effects, and policy rules in Indonesia. Using data from January 2002 to April 2008, the study reveals that the relative law of one price holds. This is proven by the variability of one product across cities being lower than the variability of all products in one city. The variability of the product is explained well by the cost of transportation and the level of development. Employing panel regression, this study also shows that prices of 35 sub-categories within 45 cities in Indonesia exhibit convergence. The average speed of convergence for perishable goods is about 9 months, for non-perishable goods 32-36 months, and for services 18-19 months. Inflation in Indonesia is found to be affected by expected inflation (backward-looking expectations and forward-looking expectations), the output gap, exchange rate depreciation, and money growth. Backward-looking inflation expectations dominate the form of inflation expectations. Using data from 1980:1 to 2008:12 and employing Generalized Method of Moments, this study finds non-linearity in the Phillips curve. The effect of exchange rate depreciation on CPI inflation is found to be linear. However, the effect of money growth on inflation is not linear; there are two threshold values identified. The study shows that the higher the growth of money, the less the impact on inflation is. To guide policymakers, this thesis derives simple, but efficient policy rules. Using monthly data from 1980 to 2008 and simulating deterministically the small open macroeconomic model, the study reveals that the inflation forecast-based rule with contemporaneous output gap (IFBG) is the most efficient rule for Indonesia. The rule suggests that the central bank should react strongly on the inflation deviation from the target, react moderately on the output gap and smooth the interest rate. The optimal horizon is 3-4 quarters. Including exchange rate in the policy rule causes deterioration in economic performance.

This thesis comprises of four empirical studies. The first three empirical studies identify and investigate the role of different factors explaining the cross sectional and temporal variation of foreign equity portfolio holdings for thirty-six developed and developing host countries. The fourth empirical study demonstrates the impact of foreign equity flows on global financial linkages of four Asian emerging markets. Our first three empirical studies use foreign equity portfolio holding data on 36 host countries and employ different panel data models. Our survey of the literature shows that only few studies (two to the best of our knowledge) have modelled the bilateral cross-country foreign equity portfolio holdings on a global basis. Further, unlike previous studies, which use cross-section models, we test all our hypotheses using relatively more efficient random effect and more robust fixed effect panel data models. The first empirical study examines three hypotheses demonstrating the association between three different components of transaction costs (commission, fees and market impact) and foreign equity portfolio allocation (FEPA). To the best of our knowledge, we are first to comprehensively test the role of each of the components individually and collectively in modelling FEPA. Addressing several robustness issues, we show significant and robust effect of transaction costs with clear evidence that foreign investors tend to underweight countries with higher transaction costs. In our second empirical study we test five hypotheses investigating the role of country specific equity market characteristics (CSEMC) in explaining FEPA. We use five different variables as proxy of CSEMC, such as stock market development/size, market liquidity, emerging market dummy, equity return volatility and exchange rate volatility. We are first to use the later two volatility measures in modelling FEPA. Consistent with theory, the results show that all the CSEMC factors tend to have strong and statistically significant effect on foreign equity portfolio allocation decisions. Our third empirical study investigates the relationship between investor protection and FEPA. The existing findings on the role of investor protection are highly controversial with divided views and contrasting conclusions. By including three different measures, we demonstrate that investor protection right, particularly the one specific to foreign investments, is also an important feature influencing allocation decisions. Finally, in our fourth empirical study we use daily foreign equity flow data for four Asian emerging markets. Application of co-integration and vector error correction (VEC) models provide strong indication that the increase in foreign equity flows is driving the global financial linkages of the Asian emerging markets. Using different variants of VEC model, our investigation also demonstrates that foreign investors in the selected Asian emerging markets engage in momentum trading strategy and flows have significant effect on the local equity market (price pressure hypothesis). Overall, our study concludes that stock market development features are the most important inputs in the worldwide foreign equity portfolio allocation decision. Furthermore, there is an indication that the growing foreign equity portfolio flows are, in part, responsible for the increasing global financial linkages of the Asian emerging markets.

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This thesis presents a collection of practical techniques for analysing various market properties in order to design advanced self-evolving trading systems based on neural networks combined with a genetic algorithm optimisation approach. Nonlinear multivariate statistical models have gained increasing importance in financial time series analysis, as it is very hard to fmd statistically significant market inefficiencies using standard linear modes. Nonlinear models capture more of the underlying dynamics of these high dimensional noisy systems than traditional models, whilst at the same time making fewer restrictive assumptions about them. These adaptive trading systems can extract information about associated time varying processes that may not be readily captured by traditional models. In order to characterise the fmancial time series in terms of its dynamic nature, this research employs various methods such as fractal analysis, chaos theory and dynamical recurrence analysis. These techniques are used for evaluating whether markets are stochastic and deterministic or nonlinear and chaotic, and to discover regularities that are completely hidden in these time series and not detectable using conventional analysis. Particular emphasis is placed on examining the feasibility of prediction in fmancial time series and the analysis of extreme market events. The market's fractal structure and log-periodic oscillations, typical of periods before extreme events occur, are revealed through recurrence plots. Recurrence qualification analysis indicated a strong presence of structure, recurrence and determinism in the fmancial time series studied. Crucial fmancial time series transition periods were also detected. This research performs several tests on a large number of US and European stocks using methodologies inspired by both fundamental analysis and technical trading rules. Results from the tests show that profitable trading models utilising advanced nonlinear trading systems can be created after accounting for realistic transaction costs. The return achieved by applying the trading model to a portfolio of real price series differs significantly from that achieved by applying it to a randomly generated price series. In some cases, these models are compared against simpler alternative approaches to ensure that there is an added value in the use of these more complex models. The superior performance of multivariate nonlinear models is also demonstrated. The long-short trading strategies performed well in both bull and bear markets, as well as in a sideways market, showing a great degree of flexibility and adjustability to changing market conditions. Empirical evidence shows that information is not instantly incorporated into market pnces and supports the claim that the fmancial time series studied, for the periods analysed, are not entirely random. This research clearly shows that equity markets are partially inefficient and do not behave along lines dictated by the efficient market hypothesis.