Estimating Risk Using Stochastic Volatility Models and Particle Stochastic Approximation Expectation Maximization

Abstract: In this thesis several stochastic volatility models are presented and used to estimate the risk of a collection of Swedish stocks, as well as of a portfolio consisting of said stocks. Model parameters are estimated using the PSAEM algorithm. It is concluded that these model are adequate at estimating the one day ahead five percent Value at Risk of the data in terms of conditional coverage.