Mohamed Saidane

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In this article we propose a new approach in event studies based on a hidden Markov chain combined with a classical event study model. The number of states inform us about the number of significant events affecting the related market and the identification of the hidden states determines exactly the delimiters of the event period. Studying each state(More)
Mixed-State conditionally heteroscedastic latent factor models attempt to describe a complex nonlinear dynamic system with a succession of linear latent factor models indexed by a switching variable. Unfortunately, despite the framework’s simplicity exact state and parameter estimation are still intractable because of the interdependency across the latent(More)
Factor models were first developed and dealt with in the case where observations are assumed to be normally distributed. Estimation is then carried out using the Expectation-Maximization (EM) algorithm based on the fact that the expectation of the completed log-likelihood conditional to the data is available in such a case. More recently, a less restrictive(More)
In this article we develop a new approach within the framework of asset pricing models that incorporates two key features of the latent volatility: co-movement among conditionally heteroscedastic financial returns and switching between different unobservable regimes. By combining latent factor models with hidden Markov chain models (HMM) we derive a(More)
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