| Summary: | Financial market volatility is central to the theory and practice of asset pricing, option pricing,
asset allocation, portfolio selection, portfolio rebalancing and hedging strategies as well as
various risk management applications. Most textbooks assume volatility to be constant;
however in practice this is a very dangerous assumption to make and has lead to a research
program regarding the distributional and dynamic properties of financial markets. Given that
financial markets display high speeds of adjustment, studies based upon daily observations
may fail to capture information contained in intraday or high frequency market movements
and until relatively recently the use of daily or equally spaced data was considered the
highest meaningful sampling frequency for financial market data.
Recently, the volatility modelling literature took a significant step forward. Andersen et al.
(2001) proposed a new approach called 'realized' volatility that exploits the information in
high frequency returns. Basically, the approach is to estimate daily volatility by taking the
square root of the sum of the squared intraday returns which are sampled at very short
intervals. We discuss several theoretical measures for volatility of which quadratic variation
(QV), integrated variance (IV) and conditional variance (CV) are the most popular. Realized
variance is a consistent estimator for QV and can approximate IV and CV under various
conditions. GARCH models are only concerned with estimating CV. === Thesis (M.Sc. (Risk Analysis))--North-West University, Potchefstroom Campus, 2006.
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