A stochastic dynamic programming approach for pricing options on stock-index futures

The aim of this thesis is to price options on equity index futures with an application to standard options on S&P 500 futures traded on the Chicago Mercantile Exchange. Our methodology is based on stochastic dynamic programming, which can accommodate European as well as American options. The model accommodates dividends from the underlying asset. It also captures the optimal exercise strategy and the fair value of the option. This approach is an alternative to available numerical pricing methods such as binomial trees, finite differences, and ad-hoc numerical approximation techniques. Our numerical and empirical investigations demonstrate convergence, robustness, and efficiency. We use this methodology to value exchange-listed options. The European option premiums thus obtained are compared to Black's closed-form formula. They are accurate to four digits. The American option premiums also have a similar level of accuracy compared to premiums obtained using finite differences and binomial trees with a large number of time steps. The proposed model accounts for deterministic, seasonally varying dividend yield. In pricing futures options, we discover that what matters is the sum of the dividend yields over the life of the futures contract and not their distribution.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Force potentiation as a modulator of contractile performance: Implications for control of skeletal muscle force and energetics of work

This thesis investigated the modulation of dynamic contractile function and energetics of work by posttetanic potentiation (PTP). Mechanical experiments were conducted in vitro using software-controlled protocols to stimulate/determine contractile function during ramp shortening, and muscles were frozen during parallel incubations for biochemical analysis. The central feature of this research was the comparison of fast hindlimb muscles from wildtype and skeletal myosin light chain kinase knockout (skMLCK-/-) mice that does not express the primary mechanism for PTP: myosin regulatory light chain (RLC) phosphorylation. In contrast to smooth/cardiac muscles where RLC phosphorylation is indispensable, its precise physiological role in skeletal muscle is unclear. It was initially determined that tetanic potentiation was shortening speed dependent, and this sensitivity of the PTP mechanism to muscle shortening extended the stimulation frequency domain over which PTP was manifest. Thus, the physiological utility of RLC phosphorylation to augment contractile function in vivo may be more extensive than previously considered. Subsequent experiments studied the contraction-type dependence for PTP and demonstrated that the enhancement of contractile function was dependent on force level. Surprisingly, in the absence of RLC phosphorylation, skMLCK-/- muscles exhibited significant concentric PTP; consequently, up to ~50% of the dynamic PTP response in wildtype muscle may be attributed to an alternate mechanism. When the interaction of PTP and the catchlike property (CLP) was examined, we determined that unlike the acute augmentation of peak force by the CLP, RLC phosphorylation produced a longer-lasting enhancement of force and work in the potentiated state. Nevertheless, despite the apparent interference between these mechanisms, both offer physiological utility and may be complementary in achieving optimal contractile function in vivo. Finally, when the energetic implications of PTP were explored, we determined that during a brief period of repetitive concentric activation, total work performed was ~60% greater in wildtype vs. skMLCK-/- muscles but there was no genotype difference in High-Energy Phosphate Consumption or Economy (i.e. HEPC: work). In summary, this thesis provides novel insight into the modulatory effects of PTP and RLC phosphorylation, and through the observation of alternative mechanisms for PTP we further develop our understanding of the history-dependence of fast skeletal muscle function.