Basket Options Pricing for Jump Diffusion Models

In this thesis we discuss basket option valuation for jump-diffusion models. We suggest three new approximate pricing methods. The first approximation method is the weighted sum of Rogers and Shi’s lower bound and the conditional second moment adjustments. The second is the asymptotic expansion to approximate the conditional expectation of the stochastic variance associated with the basket value process. The third is the lower bound approximation which is based on the combination of the asymptotic expansion method and Rogers and Shi’s lower bound. We also derive a forward partial integro-differential equation (PIDE) for general asset price processes with stochastic volatilities and stochastic jump compensators. Numerical tests show that the suggested methods are fast and accurate in comparison with Monte Carlo and other methods in most cases

Basket Options Valuation for a Local Volatility Jump-Diffusion Model with the Asymptotic Expansion Method

In this paper we discuss the basket options valuation for a jump-diffusion model. The underlying asset prices follow some correlated local volatility diffusion processes with systematic jumps. We derive a forward partial integral differential equation (PIDE) for general stochastic processes and use the asymptotic expansion method to approximate the conditional expectation of the stochastic variance associated with the basket value process. The numerical tests show that the suggested method is fast and accurate in comparison with the Monte Carlo and other methods in most cases.Comment: 16 pages, 4 table

Mining Recent Frequent Itemsets in Sliding Windows over Data Streams

This paper considers the problem of mining recent frequent itemsets over data streams. As the data grows without limit at a rapid rate, it is hard to track the new changes of frequent itemsets over data streams. We propose an efficient one-pass algorithm in sliding windows over data streams with an error bound guarantee. This algorithm does not need to refer to obsolete transactions when they are removed from the sliding window. It exploits a compact data structure to maintain potentially frequent itemsets so that it can output recent frequent itemsets at any time. Flexible queries for continuous transactions in the sliding window can be answered with an error bound guarantee

Quality problems and countermeasures in construction process

Quality is the life of architecture. Without quality there is nothing. Engineering projects have the characteristics of large investment and long construction period, so the quality of construction projects must be strictly controlled. The control of construction quality of engineering projects is the quality control of the whole process and the participation of all employees.It is the implementation of construction engineering quality regulations and mandatory standards, the correct configuration of construction production management elements and the use of scientific management methods to achieve the expected use function of engineering projects And quality standards, deliver the owner a satisfactory quality project. Most of the quality problems of construction projects appear in the construction stage. Therefore, we must strictly control the quality in project construction, strengthen the whole process management from the organization and management, find a project quality management system suitable for China's national conditions, and be able to eliminate the hidden quality hazards in the project in time to ensure the project The construction project can meet the target requirements, and the project quality can be effectively controlled.This article mainly analyzes the current problems affecting the construction quality, combined with the actual analysis, and then find some countermeasures to solve the problem

Deep Reinforcement Learning based Patch Selection for Illuminant Estimation

Previous deep learning based approaches to illuminant estimation either resized the raw image to lower resolution or randomly cropped image patches for the deep learning model. However, such practices would inevitably lead to information loss or the selection of noisy patches that would affect estimation accuracy. In this paper, we regard patch selection in neural network based illuminant estimation as a controlling problem of selecting image patches that could help remove noisy patches and improve estimation accuracy. To achieve this, we construct a selection network (SeNet) to learn a patch selection policy. Based on data statistics and the learning progression state of the deep illuminant estimation network (DeNet), the SeNet decides which training patches should be input to the DeNet, which in turn gives feedback to the SeNet for it to update its selection policy. To achieve such interactive and intelligent learning, we utilize a reinforcement learning approach termed policy gradient to optimize the SeNet. We show that the proposed learning strategy can enhance the illuminant estimation accuracy, speed up the convergence and improve the stability of the training process of DeNet. We evaluate our method on two public datasets and demonstrate our method outperforms state-of-the-art approaches