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About Thesis
This document appears to be a master's thesis that evaluates different nonlinear time series models for forecasting stock index returns. It introduces threshold autoregressive (TAR) models and artificial neural networks, and specifically evaluates self-exciting threshold autoregressive (SETAR), logistic smooth transition autoregressive (LSTAR), and single-layer feedforward neural network (SLFN) models. It describes the methodology, model implementation, forecast results and evaluation metrics used to compare the nonlinear models to a linear autoregressive benchmark model. Key findings and conclusions are discussed. Relevant tables and figures are provided in the appendices.
Detailed Information
| Author: | ['Mandar Priya Phatak'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 82 |
| Language: | English |
| Format: | |
| Price: | FREE |
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