Masters Theses

Abstract

"Forecasting financial product volatility and price is crucial for informed decision-making in investment and risk management. The models considered include GARCH, LSTM, GRU, BiLSTM, and hybrid models that incorporate various combinations of these models. We present a comparative analysis of forecasting volatility and price using the aforementioned models.

We also introduce a user-friendly dashboard for model training and evaluation, enabling users to upload datasets and customize model parameters. The dashboard allows users to select the type of model, specify the dataset range for training, determine the number of epochs, adjust the number of layers for deep learning methods, and set the window size for data processing. After selection of parameters, models are trained in the backend and the dashboard presents comprehensive results that includes graphical representations and performance metrics. These metrics facilitate comparison between the models in terms of accuracy, robustness, and computational efficiency.

Through empirical analysis, we demonstrate the effectiveness of different models in forecasting financial product volatility and price. Our findings provide insights into the strengths and limitations of single and hybrid models" -- Abstract, p. iii

Advisor(s)

Adekpedjou, Akim

Committee Member(s)

Bohner, Martin, 1966-
Samaranayake, V. A.

Department(s)

Mathematics and Statistics

Degree Name

M.S. in Applied Mathematics

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2024

Pagination

x, 70 pages

Note about bibliography

Includes_bibliographical_references_(pages 68-69)

Rights

©2024 Abhishek Kafle , All Rights Reserved

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 12388

Electronic OCLC #

1477826334

Download

Share

 
COinS