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Econometric analysis of stock returns and idiosyncratic volatility
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
posted on 06.02.2017by Tan, Pei Pei
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.