Synchronization of Markov Chains in Multivariate Regime-Switching Models
Title | Synchronization of Markov Chains in Multivariate Regime-Switching Models PDF eBook |
Author | Raphael Vial |
Publisher | |
Pages | 0 |
Release | 2015 |
Genre | |
ISBN |
Multivariate regime-switching presents an efficient way of jointly modeling the cyclical behavior of financial time series. Standard regime-switching models thereby a priori determine the relationship between the regime-switches of individual assets. These switches are usually assumed to be either perfectly synchronized or fully independent. However, neither assumption seems realistic in practice. This thesis develops a multivariate Markov regime-switching model to infer the actual degree of synchronization from the underlying data. This flexible model allows subgroups of assets to be driven by individual Markov chains. At the same time, these Markov chains underlie a dynamically changing degree of synchronization. In comparison to most existing solutions, this model is not restricted to bivariate analysis. To keep the model traceable, a novel factorization algorithm for the regime-dependent correlation matrix is formulated. This algorithm scales down the increase in parameters and presents an efficient way of ensuring positive semi-definite correlation matrices. The structure of the flexible regime-switching model is motivated by the initial synchronization analysis conducted in this thesis. The analysis of univariate regime-switching results shows that neither perfectly synchronized nor fully independent regime cycles are empirically observable. The synchronization of regime cycles tends to dynamically change over time. Some assets, however, might show more contemporaneous switching dynamics and can therefore be governed by a joint regime process. The empirical results for a sample of six international equity markets confirm the assumptions underlying this thesis. The flexible model reveals a stable synchronization factor, marked by one particular change in synchronization. The estimated parameters of this model closely cover the individual dynamics of their underlying assets and confirm the model's validity. Moreover, in some.
How Well Do Markov Switching Models Describe Actual Business Cycles?
Title | How Well Do Markov Switching Models Describe Actual Business Cycles? PDF eBook |
Author | Penelope A. Smith |
Publisher | |
Pages | 40 |
Release | 2004 |
Genre | Business forecasting |
ISBN | 9780734031518 |
Modeling and Estimation of Synchronization in Multistate Markov-switching Models
Title | Modeling and Estimation of Synchronization in Multistate Markov-switching Models PDF eBook |
Author | Cem Cakmakli |
Publisher | |
Pages | 40 |
Release | 2011 |
Genre | |
ISBN |
Discrete-Time Markov Chains
Title | Discrete-Time Markov Chains PDF eBook |
Author | G. George Yin |
Publisher | Springer Science & Business Media |
Pages | 354 |
Release | 2005-10-04 |
Genre | Mathematics |
ISBN | 0387268715 |
This book focuses on two-time-scale Markov chains in discrete time. Our motivation stems from existing and emerging applications in optimization and control of complex systems in manufacturing, wireless communication, and ?nancial engineering. Much of our e?ort in this book is devoted to designing system models arising from various applications, analyzing them via analytic and probabilistic techniques, and developing feasible compu- tionalschemes. Ourmainconcernistoreducetheinherentsystemcompl- ity. Although each of the applications has its own distinct characteristics, all of them are closely related through the modeling of uncertainty due to jump or switching random processes. Oneofthesalientfeaturesofthisbookistheuseofmulti-timescalesin Markovprocessesandtheirapplications. Intuitively,notallpartsorcom- nents of a large-scale system evolve at the same rate. Some of them change rapidly and others vary slowly. The di?erent rates of variations allow us to reduce complexity via decomposition and aggregation. It would be ideal if we could divide a large system into its smallest irreducible subsystems completely separable from one another and treat each subsystem indep- dently. However, this is often infeasible in reality due to various physical constraints and other considerations. Thus, we have to deal with situations in which the systems are only nearly decomposable in the sense that there are weak links among the irreducible subsystems, which dictate the oc- sional regime changes of the system. An e?ective way to treat such near decomposability is time-scale separation. That is, we set up the systems as if there were two time scales, fast vs. slow. xii Preface Followingthetime-scaleseparation,weusesingularperturbationmeth- ology to treat the underlying systems.
Explicit-duration Markov Switching Models
Title | Explicit-duration Markov Switching Models PDF eBook |
Author | Silvia Chiappa |
Publisher | |
Pages | 83 |
Release | 2014 |
Genre | Markov processes |
ISBN | 9781601988317 |
Markov switching models (MSMs) are probabilistic models that employ multiple sets of parameters to describe different dynamic regimes that a time series may exhibit at different periods of time. The switching mechanism between regimes is controlled by unobserved random variables that form a first-order Markov chain. Explicit-duration MSMs contain additional variables that explicitly model the distribution of time spent in each regime. This allows to define duration distributions of any form, but also to impose complex dependence between the observations and to reset the dynamics to initial conditions. Models that focus on the first two properties are most commonly known as hidden semi-Markov models or segment models, whilst models that focus on the third property are most commonly known as changepoint models or reset models. In this monograph, we provide a description of explicit-duration modelling by categorizing the different approaches into three groups, which differ in encoding in the explicit-duration variables different information about regime change/reset boundaries. The approaches are described using the formalism of graphical models, which allows to graphically represent and assess statistical dependence and therefore to easily describe the structure of complex models and derive inference routines. The presentation is intended to be pedagogical, focusing on providing a characterization of the three groups in terms of model structure constraints and inference properties. The monograph is supplemented with a software package that contains most of the models and examples described. The material presented should be useful to both researchers wishing to learn about these models and researchers wishing to develop them further.
Analysis of Markov Chain Models of Adaptive Processes
Title | Analysis of Markov Chain Models of Adaptive Processes PDF eBook |
Author | K. R. Kaplan |
Publisher | |
Pages | 116 |
Release | 1965 |
Genre | Adaptation (Physiology) |
ISBN |
Learning and adaptation are considered to be stochastic in nature by most modern psychologists and by many engineers. Markov chains are among the simplest and best understood models of stochastic processes and, in recent years, have frequently found application as models of adaptive processes. A number of new techniques are developed for the analysis of synchronous and asynchronous Markov chains, with emphasis on the problems encountered in the use of these chains as models of adaptive processes. Signal flow analysis yields simplified computations of asymptotic success probabilities, delay times, and other indices of performance. The techniques are illustrated by several examples of adaptive processes. These examples yield further insight into the relations between adaptation and feedback. (Author).
Advances in Markov-Switching Models
Title | Advances in Markov-Switching Models PDF eBook |
Author | James D. Hamilton |
Publisher | Springer Science & Business Media |
Pages | 267 |
Release | 2013-06-29 |
Genre | Business & Economics |
ISBN | 3642511821 |
This book is a collection of state-of-the-art papers on the properties of business cycles and financial analysis. The individual contributions cover new advances in Markov-switching models with applications to business cycle research and finance. The introduction surveys the existing methods and new results of the last decade. Individual chapters study features of the U. S. and European business cycles with particular focus on the role of monetary policy, oil shocks and co movements among key variables. The short-run versus long-run consequences of an economic recession are also discussed. Another area that is featured is an extensive analysis of currency crises and the possibility of bubbles or fads in stock prices. A concluding chapter offers useful new results on testing for this kind of regime-switching behaviour. Overall, the book provides a state-of-the-art over view of new directions in methods and results for estimation and inference based on the use of Markov-switching time-series analysis. A special feature of the book is that it includes an illustration of a wide range of applications based on a common methodology. It is expected that the theme of the book will be of particular interest to the macroeconomics readers as well as econometrics professionals, scholars and graduate students. We wish to express our gratitude to the authors for their strong contributions and the reviewers for their assistance and careful attention to detail in their reports.