Regime switching GARCH models

We develop univariate regime-switching GARCH (RS-GARCH) models wherein the conditional variance switches in time from one GARCH process to another. The switching is governed by a time-varying probability, specified as a function of past information. We provide sufficient conditions for stationarity and existence of moments. Because of path dependence, maximum likehood estimation is infeasible. By enlarging the parameter space to include the state variables, Bayesian estimation using a Gibbs sampling algorithm is feasible. We apply this model using the NASDAQ daily returns series.GARCH; regime switching; Bayesian inference

Theory and inference for a Markov switching GARCH model

We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity and existence of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter space to include the state variables, Bayesian estimation using a Gibbs sampling algorithm is feasible. We illustrate the model on SP500 daily returns.GARCH, Markov-switching, Bayesian inference

Theory and inference for a Markov switching Garch model.

We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity and existence of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter space to include the state variables, Bayesian estimation using a Gibbs sampling algorithm is feasible. We illustrate the model on SP500 daily returns.GARCH, Markov-switching, Bayesian inference.

Empirical Modeling of Radiative versus Magnetic Flux for the Sun-as-a-Star

We study the relationship between full-disk solar radiative flux at different wavelengths and average solar photospheric magnetic-flux density, using daily measurements from the Kitt Peak magnetograph and other instruments extending over one or more solar cycles. We use two different statistical methods to determine the underlying nature of these flux-flux relationships. First, we use statistical correlation and regression analysis and show that the relationships are not monotonic for total solar irradiance and for continuum radiation from the photosphere, but are approximately linear for chromospheric and coronal radiation. Second, we use signal theory to examine the flux-flux relationships for a temporal component. We find that a well-defined temporal component exists and accounts for some of the variance in the data. This temporal component arises because active regions with high magnetic field strength evolve, breaking up into small-scale magnetic elements with low field strength, and radiative and magnetic fluxes are sensitive to different active-region components. We generate empirical models that relate radiative flux to magnetic flux, allowing us to predict spectral-irradiance variations from observations of disk-averaged magnetic-flux density. In most cases, the model reconstructions can account for 85-90% of the variability of the radiative flux from the chromosphere and corona. Our results are important for understanding the relationship between magnetic and radiative measures of solar and stellar variability

Theory and inference for a Markov switching GARCH model

We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity and existene of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter space to include the state variables, Bayesian estimation using a Gibbs sampling algorithm is feasible. We illustrate the model on SP500 daily returns.GARCH, Markov-switching, Bayesian inference

OAUC's participation in the CLEF2015 SBS Search Suggestion Track

In this article we describe the OAUC's participation in the CLEF 2015 SBS Search Suggestion track. We are trying to represent appeal elements, used in readers' advisory theory and practice, to see if they can be used in an automatic retrieval and recommendation context. We are starting out with the pace appeal element, used on fiction to representing how quickly a buildup of the story is. The results so far indicate that much tuning is needed when building models that can represent pace

OUC's Participation in the 2011 INEX Book Track

In this article we describe the Oslo University College’s participation in the INEX 2011 Book track. In 2010, the OUC submitted retrieval results for the “Prove It” task with traditional relevance detection combined with some rudimental detection of confirmation. In line with our belief that proving or refuting facts are different semantic aware actions of speech, we have this year attempted to incorporate some rudimentary semantic support based on the WordNet database

Theory and Inference for a Markov-Switching GARCH Model

We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity and existence of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter space to include the state variables, Bayesian estimation using a Gibbs sampling algorithm is feasible. We illustrate the model on SP500 daily returns.GARCH, Markov-switching, Bayesian inference

Evaluating (linked) metadata transformations across cultural heritage domains

This paper describes an approach to the evaluation of different aspects in the transformation of existing metadata into Linked data-compliant knowledge bases. At Oslo and Akershus University College of Applied Sciences, in the TORCH project, we are working on three different experimental case studies on extraction and mapping of broadcasting data and the interlinking of these with transformed library data. The case studies are investigating problems of heterogeneity and ambiguity in and between the domains, as well as problems arising in the interlinking process. The proposed approach makes it possible to collaborate on evaluation across different experiments, and to rationalize and streamline the process