Predicting Stock Price Volatility within a Month – A Functional Data Analysis Approach

Modeling and predicting the stock price volatility is one of the key research fields for risk management. This project aims to predict the stock price volatility base on the idea of Functional Data Analysis (FDA). We are trying to use a functional AR(1) model to predict the  stock price volatility within a particular month. We wish to examine the effectiveness of the model for forecasting across various stock markets (e.g., Hong Kong market vs. US market).

Runqiu Fei

The University of Melbourne

Runqiu Fei is a third-year Bachelor of Science student from the University of Melbourne. Majoring in Mathematics and Statistics, Runqiu has a particular interest in Mathematical reasoning, problem solving, and Statistical modeling. Typically, during his second-year study, Runqiu completed a summer project focusing on Statistical model selection using the MCMC methods. Recently, Runqiu has been inspired by Functional Data Analysis and wishes to further explore the idea with some more sophisticated Statistical models. Another highlight of Runqiu’s undergraduate study is a place on the Dean’s Honour List awarded by the Faculty of Science, in recognition of his academic excellence. In the coming year, Runqiu would like to continue his journey in Mathematics and Statistics through postgraduate studies at the university.

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