Instructor: Leopoldo Catania, University of Aarhus (DK)
Date:
Event location: Classes will be online via Teams
Type: Cycle 36 - Short courses and seminars
Online Teams event: to be created soon
- Tuesday May 11 2021 12-15
- Friday May 14 2021 12-15
- Review of discrete time hidden Markov models
- Approximating nonlinear non-Gaussian state space models with structured hidden Markov models.
- Implementation in R with examples from the stochastic volatility class of models.
- Markov switching GARCH models and Dynamic Mixture GARCH models.
- Review of score driven models with application to dynamic adaptive mixture models (DAMM)
- Implementation in R with examples in volatility modelling.
Catania, L. (2019). Dynamic Adaptive Mixture Models with an Application to Volatility and Risk. Journal of Financial Econometrics.
Creal, D., Koopman, S. J., and Lucas, A. (2013). Generalized autoregressive score models with applications. Journal of Applied Econometrics, 28(5):777-795.
Haas, M., Mittnik, S., and Paolella, M. S. (2004a). Mixed normal conditional heteroskedasticity. Journal of Financial Econometrics, 2(2):211-250.
Haas, M., Mittnik, S., and Paolella, M. S. (2004b). A new approach to Markov-switching GARCH models. Journal of Financial Econometrics, 2(4):493-530
Langrock, R. (2011). Some applications of nonlinear and non-gaussian state-space modelling by means of hidden markov models. Journal of Applied Statistics, 38(12):2955-2970.
Langrock, R., MacDonald, I. L., and Zucchini, W. (2012). Some nonstandard stochastic volatility models and their estimation using structured hidden markov models. Journal of Empirical Finance, 19(1):147-161