Application of Hidden Markov Models and Filters to Financial Time Series Data April 23, 2012

The application of Hidden Markov Models (HMM) and filters, such as Kalman Filters and Particle Filters, is finding increasing applications in mathematical finance, especially in modelling the evolution of variables, which are not directly observable (such as short rate, stock price volatility and spot prices in energy markets).

The aim of this workshop is to introduce the theory underlying HMM, Kalman Filters and Particle Filters. The use of these methods in the calibration of dynamic state space models as well as in prediction of unobservable variables is also discussed.

This course focuses in particular on a specific application, viz. calibration of stochastic volatility model using high frequency asset price data. Results of numerical experiments in calibration of models and prediction of future volatility are explained with examples.