Abstract
Classical financial theory is based on Efficient Market Hypothesis (EMH). Several researchers like Schiller (1981) (1990), Le Roy and Porter (1980) have extensively argued for the invalidity of EMH.Volatility excess has been detected and highlighted by many researchers; however it has not been explained very well by EMH. For this reason, we conducted an empirical study to identify the variable characteristics of volatility by comparing three GARCH models (GARCH, E-GARCH and GRJ-GARCH) over five different market indexes to examine prediction of returns volatility. This comparison led us to detect several volatility characteristics like volatility clustering and leverage effect. This change in volatility regime is an irrefutable proof of the presence of volatility excess.Given the inability of classical financial theory in explaining volatility excess, researchers started to focus on behavioural finance (Barret and Saphister (1996)).
Highlights
Since the 1987 stock market crash, modeling and forecasting the financial markets volatility has received a significant attention from academics and practitioners because of its central position in many financial applications: assets valuation, allocation of wealth and hedging against risk.In addition, the financial world has witnessed the bankruptcy or pre-bankruptcy of several institutions that have incurred great losses because of their exposure to impromptu market movements for over a decade
While it is admitted that volatility is often marked by a number of stylized facts, such as persistence, volatility clustering, time-varying volatility and leptokurtic data behaviour, the introduction of the GARCH model developed by Engle (1982) and Bollerslev (1986), has created a new approach useful to recommend these time dependencies to financial econometricians
We found two types of distributions; GDAXI and MXX have tails skewed to the right and distributions skewed to the left
Summary
Since the 1987 stock market crash, modeling and forecasting the financial markets volatility has received a significant attention from academics and practitioners because of its central position in many financial applications: assets valuation, allocation of wealth and hedging against risk.In addition, the financial world has witnessed the bankruptcy or pre-bankruptcy of several institutions that have incurred great losses because of their exposure to impromptu market movements for over a decade. Hypothesis 1: Volatility of markets index returns has different regimes; there is volatility excess; Hypothesis 2: Volatility of markets index returns follows a single regime; there is no volatility excess. We will compare the predictive power of the three asymmetric volatility models of GARCH, GJRGARCH and EGARCH.
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