Abstract

Volatility, as a measure of uncertainty, plays a crucial role in numerous financial activities such as risk management. The Econometrics and Machine Learning communities have developed two distinct approaches for financial volatility forecasting: the stochastic approach and the neural network (NN) approach. Despite their individual strengths, these methodologies have conventionally evolved in separate research trajectories with little interaction between them. This study endeavours to bridge this gap by establishing an equivalence relationship between models of the GARCH family and their corresponding NN counterparts. With the equivalence relationship established, we introduce an innovative approach, named GARCH-NN, for constructing NN-based volatility models. It obtains the NN counterparts of GARCH models and integrates them as components into an established NN architecture, thereby seamlessly infusing volatility stylized facts inherent in the GARCH models into the neural network.

About the Speaker

Dr. Pengfei Zhao received PhD degreee in Computer Science and Engineering at Hong Kong University of Science and Technology, and BS degree in Software Engineering (now computer science and engineering) at Beijing Institute of Technology, China. He currently serves as Assistant Professor of Financial Mathematics major, the co-supervisor of Ph.D. students in Hong Kong University of Science and Technology, and the Associate Fellow of HKBU-UIC Joint Institute Research Studies. His research interest falls in sequential models with application to finance and recommender system and quantitative trading.