Econometric analysis of stock returns and idiosyncratic volatility
2017-02-06T02:07:25Z (GMT) by
This thesis focuses on the Malaysian stock market, investigating return predictability and the time series and cross-sectional behaviour of idiosyncratic volatility. Emerging markets are classified as Global Growth Generators (3G countries) by Citigroup. 3G countries are considered to be a group with potential growth and profitable investment opportunities. Even though Malaysia is not on the list of 3G countries, it is classified as a high growth country in the Citigroup study of February, 2011. The Malaysian stock market is similar to other emerging markets in terms of its political and economic force. This study is intended to provide useful inspirations for investors who are searching for investment opportunities in emerging countries. Chapter 1 outlines the empirical investigations carried out in each chapter and emphasizes the relevant research questions and contributions of the study to the existing literature. Chapter 2 to Chapter 5 investigate the return predictability and idiosyncratic volatility, and form the main body of the thesis. Chapter 2 studies return predictability in the Malaysian stock market by synthesizing the conventional return predictability methods, such as constant variance over time and the absence of autocorrelation. A comprehensive study is undertaken of returns at the market, industry and firm levels. Both macroeconomic and firm attributes which may explain the stock return predictability are also investigated in this chapter. Although return predictability is observed at the market level, it is not common at the security level. While market returns are unpredictable during crisis periods, the number of individual securities with predictable returns increased. Money growth and changes in interest rates explain the return predictability at the macro level. Size is one factor that affects the return predictability at the micro level. In the third chapter, the time series behaviour of idiosyncratic volatility is examined. This chapter provides an in-depth analysis of the characteristics of idiosyncratic volatility. Both economic conditions and firm variables are investigated as potential explanatory variables of the dynamics of idiosyncratic volatility. In addition, versatile models for estimating the idiosyncratic volatility are also discussed. Using a robust trend test, trending behaviours of the idiosyncratic volatility are examined. The aim here is to provide additional insights, from an emerging market, into either the increasing trend in idiosyncratic volatility found by Campbell et al. (2001) or the no-trend behaviour found by Brandt et al. (2010) in the U.S. market. The evidence shows a declining trend in idiosyncratic volatility after the Asian financial crisis. An upward trend in small, low-priced firms and a downward trend in large firms contributed somewhat to the declining trend. We further show, both analytically and empirically, that the dynamics of the idiosyncratic volatility are caused by stock return synchronicity, market volatility and systematic risk. The empirical literature provides various contentious results on the pricing ability of idiosyncratic volatility. The most common arguments for these controversial results are sample specificity, data frequency, and the weighting schemes used in the construction of idiosyncratic volatility. In the fourth chapter, the contradictory results of Fu (2009) and Ang et al. (AHXZ, 2006, 2009) on the relationship between idiosyncratic volatility and stock returns in the case of Malaysia are examined. In this chapter, we forecast the idiosyncratic volatility by taking into account the regime-switching behaviour of returns and the volatility clustering in the variance. Several control variables (such as an omitted variables bias, return reversal, and liquidity bias) are used to rule out the possibility of a spurious relationship between idiosyncratic volatility and stock returns. An analysis at the portfolio level explores the arbitrage or investment opportunities. We find a significant contemporaneous negative relationship between idiosyncratic volatility and stock returns. The results are robust to liquidity, return reversal, idiosyncratic skewness and momentum. The fifth chapter investigates the relationship between the idiosyncratic volatility and stock returns, conditional on trading patterns, by adopting the signal decomposition methodology of wavelet analysis. Return series are decomposed using wavelet analysis, and three mutually exclusive and exhaustive components of the idiosyncratic volatility are constructed, after which the relationship between idiosyncratic volatility and returns is examined at different timescales. Different trading patterns are found at different timescales. Previous studies have identified an inverse relationship, using time series data without decomposition, and may therefore have failed to uncover the true effects of idiosyncratic volatility on stock returns. We uncover the fact that the negative relationship is generated from the short run dynamics, while there is no association between this relationship and the long run idiosyncratic volatility. Chapter 6 summarises the key findings of this thesis, and also highlights potential areas for future research.