Growth enterprise market in Hong Kong: Efficiency evolution and long memory in return and volatility

Trang Nguyen, Taha Chaiechi, Lynne Eagle, David Low

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose
Growth enterprise market (GEM) in Hong Kong is acknowledged as one of the world’s most successful examples of small and medium enterprise (SME) stock market. The purpose of this paper is to examine the evolving efficiency and dual long memory in the GEM. This paper also explores the joint impacts of thin trading, structural breaks and inflation on the dual long memory.

Design/methodology/approach
State-space GARCH-M model, Kalman filter estimation, factor-adjustment techniques and fractionally integrated models: ARFIMA–FIGARCH, ARFIMA–FIAPARCH and ARFIMA–HYGARCH are adopted for the empirical analysis.

Findings
The results indicate that the GEM is still weak-form inefficient but shows a tendency towards efficiency over time except during the global financial crisis. There also exists a stationary long-memory property in the market return and volatility; however, these long-memory properties weaken in magnitude and/or statistical significance when the joint impacts of the three aforementioned factors were taken into account.

Research limitations/implications
A forecasts of the hedging model that capture dual long memory could provide investors further insights into risk management of investments in the GEM.

Practical implications
The findings of this study are relevant to market authorities in improving the GEM market efficiency and investors in modelling hedging strategies for the GEM.

Originality/value
This study is the first to investigate the evolving efficiency and dual long memory in an SME stock market, and the joint impacts of thin trading, structural breaks and inflation on the dual long memory.





Original languageEnglish
Pages (from-to)19-34
Number of pages16
JournalJournal of Asian Business and Economic Studies
Volume27
Issue number1
DOIs
Publication statusPublished - 11 Feb 2020

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