On Option Price Information Content and Extraction of Implied Probability Distributions

In this study we used market settlement prices of European call options on stock index futures to extract implied probability distribution function (PDF). The method used produces a PDF of returns of an underlying asset at expiration date from implied volatility smile. With this method, the assumption of lognormal distribution (Black-Scholes model) is tested. The market view of the asset price dynamics can then be used for various purposes (hedging, speculation). We used the so called smoothing approach for implied PDF extraction presented by Shimko (1993). In our analysis we obtained implied volatility smiles from index futures markets (S&P 500 and DAX indices) and standardized them. The method introduced by Breeden and Litzenberger (1978) was then used on PDF extraction. The results show significant deviations from the assumption of lognormal returns for S&P500 options while DAX options mostly fit the lognormal distribution. A deviant subjective view of PDF can be used to form a strategy as discussed in the last section.

Eye Fundus Image Analysis for Automatic Detection of Diabetic Retinopathy

Diabetes is a rapidly increasing worldwide problem which is characterised by defective metabolism of glucose that causes long-term dysfunction and failure of various organs. The most common complication of diabetes is diabetic retinopathy (DR), which is one of the primary causes of blindness and visual impairment in adults. The rapid increase of diabetes pushes the limits of the current DR screening capabilities for which the digital imaging of the eye fundus (retinal imaging), and automatic or semi-automatic image analysis algorithms provide a potential solution. In this work, the use of colour in the detection of diabetic retinopathy is statistically studied using a supervised algorithm based on one-class classification and Gaussian mixture model estimation. The presented algorithm distinguishes a certain diabetic lesion type from all other possible objects in eye fundus images by only estimating the probability density function of that certain lesion type. For the training and ground truth estimation, the algorithm combines manual annotations of several experts for which the best practices were experimentally selected. By assessing the algorithm’s performance while conducting experiments with the colour space selection, both illuminance and colour correction, and background class information, the use of colour in the detection of diabetic retinopathy was quantitatively evaluated. Another contribution of this work is the benchmarking framework for eye fundus image analysis algorithms needed for the development of the automatic DR detection algorithms. The benchmarking framework provides guidelines on how to construct a benchmarking database that comprises true patient images, ground truth, and an evaluation protocol. The evaluation is based on the standard receiver operating characteristics analysis and it follows the medical practice in the decision making providing protocols for image- and pixel-based evaluations. During the work, two public medical image databases with ground truth were published: DIARETDB0 and DIARETDB1. The framework, DR databases and the final algorithm, are made public in the web to set the baseline results for automatic detection of diabetic retinopathy. Although deviating from the general context of the thesis, a simple and effective optic disc localisation method is presented. The optic disc localisation is discussed, since normal eye fundus structures are fundamental in the characterisation of DR.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Aircraft tracking and classification with vhf passive bistatic radar

Since the times preceding the Second World War the subject of aircraft tracking has been a core interest to both military and non-military aviation. During subsequent years both technology and configuration of the radars allowed the users to deploy it in numerous fields, such as over-the-horizon radar, ballistic missile early warning systems or forward scatter fences. The latter one was arranged in a bistatic configuration. The bistatic radar has continuously re-emerged over the last eighty years for its intriguing capabilities and challenging configuration and formulation. The bistatic radar arrangement is used as the basis of all the analyzes presented in this work. The aircraft tracking method of VHF Doppler-only information, developed in the first part of this study, is solely based on Doppler frequency readings in relation to time instances of their appearance. The corresponding inverse problem is solved by utilising a multistatic radar scenario with two receivers and one transmitter and using their frequency readings as a base for aircraft trajectory estimation. The quality of the resulting trajectory is then compared with ground-truth information based on ADS-B data. The second part of the study deals with the developement of a method for instantaneous Doppler curve extraction from within a VHF time-frequency representation of the transmitted signal, with a three receivers and one transmitter configuration, based on a priori knowledge of the probability density function of the first order derivative of the Doppler shift, and on a system of blocks for identifying, classifying and predicting the Doppler signal. The extraction capabilities of this set-up are tested with a recorded TV signal and simulated synthetic spectrograms. Further analyzes are devoted to more comprehensive testing of the capabilities of the extraction method. Besides testing the method, the classification of aircraft is performed on the extracted Bistatic Radar Cross Section profiles and the correlation between them for different types of aircraft. In order to properly estimate the profiles, the ADS-B aircraft location information is adjusted based on extracted Doppler frequency and then used for Bistatic Radar Cross Section estimation. The classification is based on seven types of aircraft grouped by their size into three classes.

The molecular mechanisms and evolutionary significance of plumage colour variation in pied flycatchers (Ficedula hypoleuca)

A common feature of natural populations is that individuals differ in morphology, physiologyand behavior (i.e .phenotype). A thorough understanding of the molecular mechanisms and evolutionary forces behind this phenotypic variation is a prerequisite for understanding evolution.This thesis examines the molecular mechanism and the roles of the different evolutionary forces in plumage colour variation in pied flycatchers (Ficedulahypoleuca). Malepied flycatchers exhibit marked variation in both pigmentary and structural plumage colourand the trait has repeatedly been suggested to be of adaptive significance. An examination of plumage colour variation on reproductive output trevealed that structural colouration, and more specifically the degree of ultraviolet (UV) reflectance had an effect on number of young sired. Paternity analyses of breeding males revealed that males that had been cuckolded by their social mate tended to be less UV reflectant than males that had not been cuckolded.Neither pigment-based norstructural colouration was found to affect the probability of siring young in other nests. Phenotypic differentiation was found to be markedly greater than differentiation at neutralgenetic markers across the pied flycatcher breeding range. Furthermore patterns of differentiationin phenotypes and selectively neutral genes were not uniform. Outlier tests searching for genomic footprints of selection revealed elevated levels of genetic divergence in a gene associated with feather development (and thus potentially structural colouration) and ultraviolet vision. Th eobserved differentiation in allelic frequencies was particularly pronounced in the Spanish piedflycatcher populations. Examining gene expression during feather development indicated that the TYRP1 gene (known to be involved in the production of black pigment) may be relevant in generating phenotypic variation in pied flycatcher plumage. Also, energy homeostasis related genesfeatured prominently among the genes found to be expressed in one extreme phenotype but not the other. This is of particular interest in light of what is known about the pleiotropy ofthe melanocortin system which underlies brown-black pigment production. The melanocortinsystem is also associated with energy homeostasis (among a number of other physiological functions) and thus the results could be pointing to the signalling function of brown-blackplumage. Plumage colour variation in pied flycatchers, both structural and pigmentary, can thus beconcluded to be exhibiting signals of non-neutral evolution. Structural colouration was found to play a role in sexual selection and putative signals of selection were further detected in acandidate gene for this trait. Evidence for non-neutral evolution of pigmentary colouration was also detected. These findings, together with the fact that preliminary evidence for an energy balance associated signalling function for plumage was found, present good starting points for further investigations into the meaning and mechanisms of plumage colour variation in piedflycatchers.

Generating competitive advantage through competitor information systems

This pro gradu –thesis discusses generating competitive advantage through competitor information systems. The structure of this thesis follows the structure of the WCA model by Alter (1996). In the WCA model, business process is influenced by three separate but connected elements: information, technology, and process participants. The main research question is how competitor information can be incorporated into or made into a tool creating competitive advantage. Research subquestions are: How does competitor information act as a part of the business process creating competitive advantage? How is a good competitor information system situated and structured in an organisation? How can management help information generate competitive advantage in the business process with participants, information, and technology? This thesis discusses each of the elements separate, but the elements are connected to each other and to competitive advantage. Information is discussed by delving into competitor information and competitor analysis. Competitive intelligence and competitor analysis requires commitment throughout the organisation, including top management, the desire to perform competitive intelligence and the desire to use the end products of that competitive intelligence. In order to be successful, systematic competitive intelligence and competitor analysis require vision, willingness to strive for the goals set, and clear strategies to proceed. Technology is discussed by taking a look into the function of the competitor information systems play and the place they occupy within an organization. In addition, there is discussion about the basic infrastructure of competitor information systems, and the problems competitor information systems can have plaguing them. In order for competitor information systems to be useful and worthy of the resources it takes to develop and maintain them, competitor information systems require on-going resource allocation and high quality information. In order for competitor information systems justify their existence business process participants need to maintain and utilize competitor information systems on all levels. Business process participants are discussed through management practices. This thesis discusses way to manage information, technology, and process participants, when the goal is to generate competitive advantage through competitor information systems. This is possible when information is treated as a resource with value, technology requires strategy in order to be successful within an organization, and process participants are an important resource. Generating competitive advantage through competitor information systems is possible when the elements of information, technology, and business process participants all align advantageously.