University of Macau / Faculty of Social Sciences and Humanities / Department of Economics

published_or_final_version / Economics and Finance / Master / Master of Philosophy

In recent years technical trading rules are widely known by more and more people, not only the academics many investors also learn to apply them in financial markets. One approach of constructing technical trading rules is to use technical indicators, such as moving average(MA) and filter rules. These trading rules are widely used possibly because the technical indicators are simple to compute and can be programmed easily. An alternative approach of constructing technical trading rules is to rely on some chart patterns. However, the patterns and signals detected by these rules are often made by the visual inspection through human eyes. As for as I know, there are no universally acceptable methods of constructing the chart patterns. In 2000, Prof. Andrew Lo and his colleagues are the first ones who define five pairs of chart patterns mathematically. They are Head-and-Shoulders(HS) & Inverted Headand- Shoulders(IHS), Broadening tops(BTOP) & bottoms(BBOT), Triangle tops(TTOP) & bottoms(TBOT), Rectangle tops(RTOP) & bottoms( RBOT) and Double tops(DTOP) & bottoms(DBOT). The basic formulation of a chart pattern consists of two steps: detection of (i) extreme points of a price series; and (ii) shape of the pattern. In Lo et al.(2000), the method of kernel smoothing was used to identify the extreme points. It was admitted by Lo et al. (2000) that the optimal bandwidth used in kernel method is not the best choice and the expert judgement is needed in detecting the bandwidth. In addition, their work considered chart pattern detection only but no buy/sell signal detection. It should be noted that it is possible to have a chart pattern formed without a signal detected, but in this case no transaction will be made. In this thesis, I propose a new class of technical trading rules which aims to resolve the above problems. More specifically, each chart pattern is parameterized by a set of parameters which governs the shape of the pattern, the entry and exit signals of trades. Then the optimal set of parameters can be determined by using genetic algorithms (GAs). The advantage of GA is that they can deal with a high-dimensional optimization problems no matter the parameters to be optimized are continuous or discrete. In addition, GA can also be convenient to use in the situation that the fitness function is not differentiable or has a multi-modal surface. / published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

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published_or_final_version / abstract / toc / Business / Master / Master of Philosophy

published_or_final_version / abstract / Economics and Finance / Doctoral / Doctor of Philosophy

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This dissertation is in the form of three essays on the topic of component and long memory GARCH models. The unifying feature of the thesis is the focus on investigating European index option evaluation using these models. / The first essay presents a new model for the valuation of European options. In this model, the volatility of returns consists of two components. One of these components is a long-run component that can be modeled as fully persistent. The other component is short-run and has zero mean. The model can be viewed as an affine version of Engle and Lee (1999), allowing for easy valuation of European options. The model substantially outperforms a benchmark single-component volatility model that is well established in the literature. It also fits options better than a model that combines conditional heteroskedasticity and Poisson normal jumps. While the improvement in the component model's performance is partly due to its improved ability to capture the structure of the smirk and the path of spot volatility, its most distinctive feature is its ability to model the term structure. This feature enables the component model to jointly model long-maturity and short-maturity options. / The second essay derives two new GARCH variance component models with non-normal innovations. One of these models has an affine structure and leads to a closed-form option valuation formula. The other model has a non-affine structure and hence, option valuation is carried out using Monte Carlo simulation. We provide an empirical comparison of these two new component models and the respective special cases with normal innovations. We also compare the four component models against GARCH(1,1) models which they nest. All eight models are estimated using MLE on S&P500 returns. The likelihood criterion strongly favors the component models as well as non-normal innovations. The properties of the non-affine models differ significantly from those of the affine models. Evaluating the performance of component variance specifications for option valuation using parameter estimates from returns data also provides strong support for component models. However, support for non-normal innovations and non-affine structure is less convincing for option valuation. / The third essay aims to investigate the impact of long memory in volatility on European option valuation. We mainly compare two groups of GARCH models that allow for long memory in volatility. They are the component Heston-Nandi GARCH model developed in the first essay, in which the volatility of returns consists of a long-run and a short-run component, and a fractionally integrated Heston-Nandi GARCH (FIHNGARCH) model based on Bollerslev and Mikkelsen (1999). We investigate the performance of the models using S&P500 index returns and cross-sections of European options data. The component GARCH model slightly outperforms the FIGARCH in fitting return data but significantly dominates the FIHNGARCH in capturing option prices. The findings are mainly due to the shorter memory of the FIHNGARCH model, which may be attributed to an artificially prolonged leverage effect that results from fractional integration and the limitations of the affine structure.

This dissertation examines four specific aspects of capital structure and dividend policy. The first issue concerns the determinants of capital structure dynamics. The primary objective is to examine whether stock returns are important factors in firm???s capital structure choice, and if so, whether this effect is persistent. In so doing, we use a data set which (1) avoids the complexity of tax rates faced by previous studies, (2) we introduce new variables that are unique to Oman, and (3) we distinguish empirically between bank debt and non-bank debt. We find stock returns are a first order determinant of capital structure. Firms do show some tendency to rebalance towards their target capital structure. However, the impact of stock returns dominates the effects of rebalancing. We also find new evidence that firms do take countermeasures to offset changes in their leverage that stem from equity value variations, but do so at a low speed. The next topic studied concerns the ex-dividend day behaviour. We investigate this issue using a unique data set where there are no taxes on dividends and capital gains and stock prices are decimalized. In this economy, any price decline that is smaller than the dividends can not be attributed to taxes and price discreteness. We find that the stock price drops by less than the amount of dividends and there is a significant positive ex-day return. We are able to account for our results using market microstructure models. The third issue investigated is the stock price reaction to dividend announcements. Tax-based signaling models argue that dividends would not have information and be informative if it is not for the higher taxes on dividends relative to capital gains that they apply to shareholders. The absence of personal taxes in Oman presents a valuable opportunity to test this prediction. Our results show that the announcements of dividend increases (decreases) are associated with a stock price increase (decrease) which contradicts the tax-based signaling models. The final chapter analyzes the determinants and stability of dividend policy of financial and non-financial firms. Investigating this issue is important for at least two reasons. First, Omani firms distribute almost 100% of their profits in dividends which led the Capital Market Authority (CMA) to issue a circular (number 12/2003) arguing that firms should retain some of their earnings for ???rainy days???. This allows us understand the characteristics of firms that pay dividends. Second, firms are highly levered mainly through bank loans which render the role of dividends in reducing the agency costs less important. Unlike most previous studies, we include both dividend paying and non-dividend paying firms to avoid a selection bias. We find that there are some common factors that determine dividend policy of both financial and non-financial firms and there are some factors that affect only non-financial firms. We also find that the factors that influence the probability to pay dividends are the same factors that drive the amount of dividends paid for both financial and non-financial firms. We document that non-financial firms adopt a policy of smoothing dividends while financial firms do not have a stable dividend policy.

This dissertation examines four specific aspects of capital structure and dividend policy. The first issue concerns the determinants of capital structure dynamics. The primary objective is to examine whether stock returns are important factors in firm???s capital structure choice, and if so, whether this effect is persistent. In so doing, we use a data set which (1) avoids the complexity of tax rates faced by previous studies, (2) we introduce new variables that are unique to Oman, and (3) we distinguish empirically between bank debt and non-bank debt. We find stock returns are a first order determinant of capital structure. Firms do show some tendency to rebalance towards their target capital structure. However, the impact of stock returns dominates the effects of rebalancing. We also find new evidence that firms do take countermeasures to offset changes in their leverage that stem from equity value variations, but do so at a low speed. The next topic studied concerns the ex-dividend day behaviour. We investigate this issue using a unique data set where there are no taxes on dividends and capital gains and stock prices are decimalized. In this economy, any price decline that is smaller than the dividends can not be attributed to taxes and price discreteness. We find that the stock price drops by less than the amount of dividends and there is a significant positive ex-day return. We are able to account for our results using market microstructure models. The third issue investigated is the stock price reaction to dividend announcements. Tax-based signaling models argue that dividends would not have information and be informative if it is not for the higher taxes on dividends relative to capital gains that they apply to shareholders. The absence of personal taxes in Oman presents a valuable opportunity to test this prediction. Our results show that the announcements of dividend increases (decreases) are associated with a stock price increase (decrease) which contradicts the tax-based signaling models. The final chapter analyzes the determinants and stability of dividend policy of financial and non-financial firms. Investigating this issue is important for at least two reasons. First, Omani firms distribute almost 100% of their profits in dividends which led the Capital Market Authority (CMA) to issue a circular (number 12/2003) arguing that firms should retain some of their earnings for ???rainy days???. This allows us understand the characteristics of firms that pay dividends. Second, firms are highly levered mainly through bank loans which render the role of dividends in reducing the agency costs less important. Unlike most previous studies, we include both dividend paying and non-dividend paying firms to avoid a selection bias. We find that there are some common factors that determine dividend policy of both financial and non-financial firms and there are some factors that affect only non-financial firms. We also find that the factors that influence the probability to pay dividends are the same factors that drive the amount of dividends paid for both financial and non-financial firms. We document that non-financial firms adopt a policy of smoothing dividends while financial firms do not have a stable dividend policy.