Modelling skewness in Financial data

Shum, Wai Yan (2014) Modelling skewness in Financial data. Doctoral thesis, University of East Anglia.

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The first systematic analysis of the skew-normal distribution in a scalar case
is done by Azzalini (1985). Unlike most of the skewed distributions, the
skew-normal distribution allows continuity of the passage from the normal
distribution to the skew-normal distribution and is mathematically tractable.
The skew-normal distribution and its extensions have been applied in lots of
financial applications. This thesis contributes to the recent development
of the skew-normal distribution by, firstly, analyzing the the properties of
annualization and time-scaling of the skew-normal distribution under heteroskedasticity
which, in turn allows us to model financial time series with the
skew-normal distribution at different time scales; and, secondly, extending
the Skew-Normal-GARCH(1,1) model of Arellano-Valle and Azzalini (2008)
to allow for time-varying skewness.
Chapter one analyses the performance of the time scaling rules for computing
volatility and skewness under the Skew-Normal-GARCH(1,1) model
at multiple horizons by simulation and applies the simulation results to the
Skew-Normal-Black-Scholes option pricing model introduced by Corns and
Satchell (2007). Chapter two tests the Skew-Normal Black-Scholes model
empirically. Chapter three extends the Skew-Normal-GARCH(1,1) model to
allow for time-varying skewness. The time-varying-skewness adjusted model
is then applied to test the relationship between heterogeneous beliefs, shortsale
restrictions and market declines.

Item Type: Thesis (Doctoral)
Faculty \ School: Faculty of Social Sciences > School of Economics
Depositing User: Users 2259 not found.
Date Deposited: 12 Jun 2014 13:46
Last Modified: 12 Jun 2014 13:46

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