9th Economics & Finance Conference, London

A STUDY IN HK STOCK MARKET SENTIMENT

DAVID CHUI

Abstract:

The first part of this paper is to study the mean reversion behaviour of Hong Kong stock market volatility and model such behaviour to forecast volatilities. This modelling is easy to implement and achieves reasonable accuracy. In Hong Kong market, when volatility deviates from the long-term level (i.e. the mean reversion level), we estimate that the deviation would be reverted to half in around sixteen trading days, or within three weeks. The accuracy of the mean reversion model in modeling Hong Kong stock market volatility also translates into a superior forecasting model. Such mean reversion information has application in portfolio insurance for long term investors. The second part of the paper is to investigate how much of the market information conveying a prediction on market sentiment. The market information is to use the implied volatilities of the Hang Seng Index Options and test if it reflects on investor’s sentiment on positive (bullish) or negative (bearish) market climate. The reflection is based upon the relative sizes of tails of the implicit Hong Kong market volatility distribution (i.e. investigating skewness and variance of the ex-ante volatility distribution of the Hong Kong market). Interestingly, it is found that investor’ expectations did not portend to rising and falling markets, but rather appear to have been random. Over the period of study 2005 – 2015, there was no evidence in the predictability of market crash such as 2008 and neither were they related to trendy forecasts such as bull market. Nevertheless, it was revealed that investors did have some forecasting ability, as the implied volatilities from Hang Seng Index Options were better forecasts of subsequent volatility than forecasts based solely upon historic price movements.

Keywords: Sentiment, Hong Kong, Implied Volatility, Options

PDF: Download



Copyright © 2024 The International Institute of Social and Economic Sciences, www.iises.net