Our main research is on Regime switching models with applications in financial and economic time series. We consider theoretical structure of the models with Markov chains. We are also interested in empirical analysis, especially volatility forecasting performance and its application in risk management. Currently, we carry our research on how to facilitate high frequency data to volatility estimation and forecasting.
Novel applications from existing statistical techniques can be applied such as using latent class analysis, meta-analysis, survival and prognostic modelling which seek to answer specific clinical questions such as determining appropriate risk factors for certain diseases. These are often very applied in nature but can present challenges in how to model missing data.
- Menli Tirkishova
- Regime switching models
- Volatility forecasting using high frequency data
- Stochastic diffusion Model and its application
- Spectral analysis of nonlinear time series
- Margin setting and risk management.