Stylised facts of nancial time series and hidden Markov models in continuous time
نویسندگان
چکیده
منابع مشابه
Modelling financial time series with switching state space models
The deeciencies of stationary models applied to nancial time series are well documented. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for nancial markets. We use a dynamic switching (modelled by a hidden Markov model) combined with a linear dynamical system in a hybrid switching state space...
متن کاملSegmentation of switching dynamics with a Hidden Markov Model of neural prediction experts
We discuss a framework for modeling the switching dynamics of a time series based on hidden Markov models (HMM) of prediction experts, here neural networks. Learning is treated as a maximum likelihood problem. In particular, we present an Expectation-Maximization (EM) algorithm for adjusting the expert parameters as well as the HMM transition probabilities. Based on this algorithm, we develop a...
متن کاملDiscrete or Continuous-Time Hidden Markov Models for Count Time Series
In Hidden Markov Models (HMM) the probability distribution of response Yt (∀t = 1, 2, . . . , T ) at each observation time is conditionally specified on the current hidden or latent state Xt. The sequence of hidden states follows a first order time-homogeneous Markov chain. Discrete time or continuous time HMM are respectively specified by T ⊆ N or T ⊆ R (from now on DHMM and CHMM). In this wor...
متن کاملOn the Stationarity of Markov-Switching GARCH Processes
GARCH models with Markov-switching regimes are often used for volatility analysis of nancial time series. Such models imply less persistence in the conditional variance than the standard GARCH model, and potentially provide a signi cant improvement in volatility forecast. Nevertheless, conditions for asymptotic wide-sense stationarity have been derived only for some degenerated models. In this...
متن کاملContinuous-time hidden Markov models for network performance evaluation
Abstract In this paper, we study the use of continuous-time hidden Markov models for network protocol and application performance evaluation. We develop an algorithm to infer the continuous-time hidden Markov model from a series of end-to-end delay and loss observations of probe packets. This model can then be used to simulate network environments for network performance evaluation. We validate...
متن کامل