Value at Risk: GARCH vs.Stochastic Volatility Models: Empirical Study
|Author:||Mgr. Tesárová Viktoria|
|Year:||2013 - winter|
|Leaders:|| PhDr. Petr Gapko Ph.D.
|Work type:|| Doctoral
|Awards and prizes:|
|Abstract:||The thesis compares GARCH volatility models and its extensions EGARCH,
TGARCH, APARCH and Stochastic Volatility (SV) models with Student's t
distributed errors and its empirical forecasting performance of Value at Risk
on ve stock price indices: S&P, NASDAQ Composite, CAC, DAX and FTSE.
It introduces in details the problem of SV models Maximum Likelihood examinations
and suggests the newly developed approach of Ecient Importance
Sampling (EIS). EIS is a procedure that provides an accurate Monte Carlo evaluation
of likelihood function which depends upon high-dimensional numerical
Comparison analysis is divided into in-sample and out-of-sample forecasting
performance and evaluated using standard statistical probability backtestig
methods as conditional and unconditional coverage.
Based on empirical analysis thesis shows that SV models can perform at
least as good as GARCH models if not superior in forecasting volatility and
|Downloadable:|| Rigorous Thesis of Tesárová