Randomized Binomial Tree and Pricing of American-Style Options

Randomized binomial tree and methods for pricing American options were studied. Firstly, both the completeness and the no-arbitrage conditions in the randomized binomial tree market were proved. Secondly, the description of the node was given, and the cubic polynomial relationship between the number of nodes and the time steps was also obtained. Then, the characteristics of paths and storage structure of the randomized binomial tree were depicted. Then, the procedure and method for pricing American-style options were given in a random binomial tree market. Finally, a numerical example pricing the American option was illustrated, and the sensitivity analysis of parameter was carried out. The results show that the impact of the occurrence probability of the random binomial tree environment on American option prices is very significant. With the traditional complete market characteristics of random binary and a stronger ability to describe, at the same time, maintaining a computational feasibility, randomized binomial tree is a kind of promising method for pricing financial derivatives

Characterization of age-related microstructural changes in locus coeruleus and substantia nigra pars compacta.

Locus coeruleus (LC) and substantia nigra pars compacta (SNpc) degrade with normal aging, but not much is known regarding how these changes manifest in MRI images, or whether these markers predict aspects of cognition. Here, we use high-resolution diffusion-weighted MRI to investigate microstructural and compositional changes in LC and SNpc in young and older adult cohorts, as well as their relationship with cognition. In LC, the older cohort exhibited a significant reduction in mean and radial diffusivity, but a significant increase in fractional anisotropy compared with the young cohort. We observed a significant correlation between the decrease in LC mean, axial, and radial diffusivities and measures examining cognition (Rey Auditory Verbal Learning Test delayed recall) in the older adult cohort. This observation suggests that LC is involved in retaining cognitive abilities. In addition, we observed that iron deposition in SNpc occurs early in life and continues during normal aging

Bit Rate Control for Real-time Multipoint Video Conferencing

With the rapid development of video compression and network technology, real-time video communications has become a popular part of our daily life. Rate control is needed to satisfy the expectation of high quality and to make it possible to transmit over limited bandwidth. The objective of this thesis is to design a rate control scheme for a real-time Transcoding-Compositing Multipoint Video Conferencing System, which operates exclusively in the DCT domain. In this Transcoding-Compositing system, the mode of the composited frame should firstly be decided before encoding the composited image. A mode decision method relying on Karhunen-Loeve scene change detection is proposed. A new linear source Rate-Distortion model is developed in the - domain ( is the percentage of zero), based on which rate control scheme is designed. The designed rate control scheme is parted into three levels: Frame Level, Sub-frame Level, and Macroblock Level. Frame Level rate control decides the bit budget for each frame based on the buffer fullness. Sub-frame Level rate control optimizes the distribution of the bit budget among the decimated sub-images. Based on the linear source model, Macroblock Level rate control carries out an adaptive procedure to precisely control the number of encoding bits for each sub-image

Application of Convolutional Recurrent Neural Network for Individual Recognition Based on Resting State fMRI Data

In most task and resting state fMRI studies, a group consensus is often sought, where individual variability is considered a nuisance. None the less, biological variability is an important factor that cannot be ignored and is gaining more attention in the field. One recent development is the individual identification based on static functional connectome. While the original work was based on the static connectome, subsequent efforts using recurrent neural networks (RNN) demonstrated that the inclusion of temporal features greatly improved identification accuracy. Given that convolutional RNN (ConvRNN) seamlessly integrates spatial and temporal features, the present work applied ConvRNN for individual identification with resting state fMRI data. Our result demonstrates ConvRNN achieving a higher identification accuracy than conventional RNN, likely due to better extraction of local features between neighboring ROIs. Furthermore, given that each convolutional output assembles in-place features, they provide a natural way for us to visualize the informative spatial pattern and temporal information, opening up a promising new avenue for analyzing fMRI data

Influence of Au nanoparticles on the properties of TiO2 film for use in DSSC

Ph.DDOCTOR OF PHILOSOPH

An Optimization Approach for pricing of Discrete European Call options Based on the Preference of Investors

Firstly, a method for measuring the risk aversion of investors was proposed based on the prospect theory. Secondly, under a sole hypothetical condition in which the risk aversion degree for different assets is the same in a market, the pricing of discrete European options was given based on the objective probability. Thirdly, it was proven that the European option price obtained was a non-arbitrate price. And then, both for the binomial tree, which is a complete market, and for the trinomial tree, which is an incomplete market, pricing European options were discussed by implementing the method provided in this paper. Lastly, an illustration is used to demonstrate how to estimate preference parameters from market data and how to calculate options prices. The result states that the method in this paper is the same as the traditional risk-neutral methods in a complete market, but it is different from the traditional risk-neutral methods in an incomplete market, and more, the price obtained in this paper is affected by the objective probability and also contains the risk attitude of the investors

Self-learning PID Control for X-Y NC Position Table with Uncertainty Base on Neural Network

An adaptive radical basis function (RBF) neural network PID control scheme for X-Y position table is proposed by the paper. Firstly, X-Y position table model is established, controller based on neutral network is used to learn adaptive and compensate uncertainty model of X-Y position table, neutral network is used to study model. PID neural network controller base on augmented variable method is designed. PID controller is used as assistant direction error controller, neural network parameters base on stochastic gradient algorithm can be adjust adaptive on line. The simulation results show that the presented controller has important engineering value

An Optimization Approach for Pricing Analysis on a Bank Wealth-Management Equity Structured Product

This paper researches on the pricing and design of a certain stock-type structured product. Firstly, a semi-analytic pricing model is deduced by discounting the payoff function of the product. Secondly, the difference between publishers\u27 and investors\u27 required rate of return is explained with market segmentation theory when estimating the pricing model’s parameters, which defines the cost and sale price of a product. Finally, with sensitivity analysis, it is concluded that publishers can increase their profits by extending the due date of the product or publishing it with relatively large asset volatility. The study aims to help publishers make reasonable product design and pricing decisions