American option prices and optimal exercise boundaries under Heston Model–A Least-Square Monte Carlo approach

Pricing American options has always been problematic due to its early exercise characteristic. As no closed-form analytical solution for any of the widely used models exists, many numerical approximation methods have been proposed and studied. In this thesis, we investigate the Least-Square Monte Carlo Simulation (LSMC) method of Longstaff & Schwartz for pricing American options under the two-dimensional Heston model. By conducting extensive numerical experimentation, we put the LSMC to test and investigate four different continuation functions for the LSMC. In addition, we consider investigating seven different combination of Heston model parameters. We analyse the results and select the optimal continuation function according to our criteria. Then we uncover and study the early exercise boundary foran American put option upon changing initial volatility and other parameters of the Heston model

A Deep Learning Approach To Vehicle Fault Detection Based On Vehicle Behavior

Vehicles and machinery play a crucial role in our daily lives, contributing to our transportationneeds and supporting various industries. As society strives for sustainability, the advancementof technology and efficient resource allocation become paramount. However, vehicle faultscontinue to pose a significant challenge, leading to accidents and unfortunate consequences.In this thesis, we aim to address this issue by exploring the effectiveness of an ensemble ofdeep learning models for supervised classification. Specifically, we propose to evaluate the performance of 1D-CNN-Bi-LSTM and 1D-CNN-Bi-GRU models. The Bi-LSTM and Bi-GRUmodels incorporate a multi-head attention mechanism to capture intricate patterns in the data.The methodology involves initial feature extraction using 1D-CNN, followed by learning thetemporal dependencies in the time series data using Bi-LSTM and Bi-GRU. These models aretrained and evaluated on a labeled dataset, yielding promising results. The successful completion of this thesis has met the objectives and scope of the research, and it also paves the way forfuture investigations and further research in this domain

A Deep Learning Approach To Vehicle Fault Detection Based On Vehicle Behavior

Vehicles and machinery play a crucial role in our daily lives, contributing to our transportationneeds and supporting various industries. As society strives for sustainability, the advancementof technology and efficient resource allocation become paramount. However, vehicle faultscontinue to pose a significant challenge, leading to accidents and unfortunate consequences.In this thesis, we aim to address this issue by exploring the effectiveness of an ensemble ofdeep learning models for supervised classification. Specifically, we propose to evaluate the performance of 1D-CNN-Bi-LSTM and 1D-CNN-Bi-GRU models. The Bi-LSTM and Bi-GRUmodels incorporate a multi-head attention mechanism to capture intricate patterns in the data.The methodology involves initial feature extraction using 1D-CNN, followed by learning thetemporal dependencies in the time series data using Bi-LSTM and Bi-GRU. These models aretrained and evaluated on a labeled dataset, yielding promising results. The successful completion of this thesis has met the objectives and scope of the research, and it also paves the way forfuture investigations and further research in this domain