A1361
Title: Particle Markov chain Monte Carlo for parameter estimation in volatility models
Authors: Eivind Lamo - University of Bergen (Norway) [presenting]
Abstract: With the continuous increase in computational power, sequential Monte Carlo methods have emerged as an efficient technique for estimating unknown data in a world consisting of nonlinearity and non-Gaussianity. A theoretical foundation is built with the help of Bayesian statistics that can be applied to numerous real-world problems. The interest is in solving the problem of estimating an unknown signal process given certain observations, where both processes are modelled as Markovian, nonlinear, non-Gaussian state-space models. In particular, the attempt is to estimate the unobserved volatility dynamics for the S\&P 500 index using observed returns and a slight modification of Heston's stochastic volatility model. This will be done using the sequential importance resampling filter, which is also combined with the Markov chain Monte Carlo for parameter estimation. The overall goal is to propose another alternative to Heston's model, by investigating how well the model responds to measuring volatility when including data from the financial crisis of 2007-2008.
