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Application of Hidden Markov Models and Filters to Financial Time Series Data

23 - 24 April 2012, London

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Scope and Purpose
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.
Benefits of Attending
This in-depth two-day workshop gives you a comprehensive overview of the most important concepts in this field, and
  • introduces and explores the theoretical aspects;
  • presents the applications of state space modelling, Kalman filtering and nonlinear filtering;
  • explains the practical applications of linear/non-linear filtering in financial risk management
  • provides an overview on the future development of the application of filtering in finance
Target Audience

Practitioners at banks, risk professionals, traders, consultants and academics.


Industry Rate: 2 days £1025 + VAT

Thanks to our sponsors, there are a limited number of generous bursaries available for academics and research students so they can attend at reduced rates.

For more information regarding bursaries, please contact us on + 44 (0) 1895 819 488 or email

Discounted rates for group bookings can be also arranged on request.

Brochure Download | Register Now