Download or read book A Component Markov Regime Switching Autoregressive Conditional Range Model written by Richard D. F. Harris and published by . This book was released on 2016 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we develop a component Markov switching conditional volatility model based on the intraday range and evaluate its performance in forecasting the weekly volatility of the S&P 500 index. We compare the performance of the range-based Markov switching model with that of a number of well established return-based and range-based volatility models, namely EWMA, GARCH and FIGARCH models, the Markov Regime-Switching GARCH model of Klaassen (2002), the hybrid EWMA model of Harris and Yilmaz (2009), and the CARR model of Chou (2005). We show that the range-based Markov switching conditional volatility models produce more accurate out-of-sample forecasts, contain more information about true volatility, and exhibit similar or better performance when used for the estimation of value at risk.
Download or read book Advances in Markov Switching Models written by James D. Hamilton and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Markov Switching Vector Autoregressions written by Hans-Martin Krolzig and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contributes to re cent developments on the statistical analysis of multiple time series in the presence of regime shifts. Markov-switching models have become popular for modelling non-linearities and regime shifts, mainly, in univariate eco nomic time series. This study is intended to provide a systematic and operational ap proach to the econometric modelling of dynamic systems subject to shifts in regime, based on the Markov-switching vector autoregressive model. The study presents a comprehensive analysis of the theoretical properties of Markov-switching vector autoregressive processes and the related statistical methods. The statistical concepts are illustrated with applications to empirical business cyde research. This monograph is a revised version of my dissertation which has been accepted by the Economics Department of the Humboldt-University of Berlin in 1996. It con sists mainly of unpublished material which has been presented during the last years at conferences and in seminars. The major parts of this study were written while I was supported by the Deutsche Forschungsgemeinschajt (DFG), Berliner Graduier tenkolleg Angewandte Mikroökonomik and Sondeiforschungsbereich 373 at the Free University and Humboldt-University of Berlin. Work was finally completed in the project The Econometrics of Macroeconomic Forecasting founded by the Economic and Social Research Council (ESRC) at the Institute of Economies and Statistics, University of Oxford. It is a pleasure to record my thanks to these institutions for their support of my research embodied in this study.
Download or read book Covariance Estimation with Markov Switching Generalized Autoregressive Conditional Heteroskedasticity Models Applications to Portfolio Management written by Tristan Gardner Wisner and published by . This book was released on 2017 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this paper is to implement and test the multivariate regime-switching GARCH model as a potential improvement on traditional methods for estimating the covariance matrix for multiple time series. I describe the characteristics and estimation of the primary model of interest, MS-GARCH, and some competitor models. I implement and backtest a portfolio management strategy based on risk minimization using MS-GARCH forecasts and evaluate performance relative to competitors. I find MS-GARCH to be an useful tool in portfolio construction, and to offer some significant advantages over more traditional models in terms of accuracy and interpretability when describing a process.
Download or read book Low Frequency Autoregressive Model with Markov Switching written by Feng Chang and published by . This book was released on 2009 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: Existing hidden Markov regime-switching models based on Hamilton (1989) characterize a time series process as a sequence of stochastic, segmented fix trends with some short-term dynamics in mean or conditional variance. To capture the time-varying trend and conditional volatility of economic time series in low-frequency, this paper extends the Hamilton model to the regime duration domain by assuming that the current process depends on the current regime duration (number of periods that the process has been in the current state) and few previous regime durations. Deviation from the long-term trend occurred over the previous durations is assumed to have an impact on the trend of the current process as well as the entire current regime duration. So this model generates interactions not only between the high-frequency current trend and low-frequency variables which aggregate the information over each of the past regime durations, but also between the current regime duration and the previous regime durations. Similarly, I have also incorporated a low-frequency auto-regressive conditional variance process in the model. By transforming the state-space representation to duration-space representation in describing the Markov process and truncating the path-dependency by a maximum allowable total duration, I am able to alleviate the path-dependency problem involved and keep estimation of the model tractable. The maximum likelihood estimation, the basic filtering presented in this paper produce reasonable results when I apply the model to the quarterly data of foreign exchange rates for British Pound, French Franc, German Mark, and Japanese Yen over 1973-1994. Significant low-frequency mean and conditional variance auto-regressive processes in the data have been detected by the model.
Download or read book Complex Systems in Finance and Econometrics written by Robert A. Meyers and published by Springer Science & Business Media. This book was released on 2010-11-03 with total page 919 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finance, Econometrics and System Dynamics presents an overview of the concepts and tools for analyzing complex systems in a wide range of fields. The text integrates complexity with deterministic equations and concepts from real world examples, and appeals to a broad audience.
Download or read book High Frequency Volatility Modelling written by Yifan Li and published by . This book was released on 2019 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop a Markov-Switching Autoregressive Conditional Intensity (MS-ACI) model with time-varying transitional parameters, and show that it can be reliably estimated via the Stochastic Approximation Expectation-Maximization algorithm. Applying our model to high-frequency transaction data, we detect two distinct regimes in the intraday volatility process: a dominant volatility regime that is observable throughout the trading day representing the risk-transferring trading activity of investors, and a minor volatility regime that concentrates around market liquidity shocks which mainly capture impacts of firm-specific news arrivals. We propose a novel daily volatility decomposition based on the two detected volatility regimes.
Download or read book Time Varying Transition Probabilities for Markov Regime Switching Models written by Marco Bazzi and published by . This book was released on 2014 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a new Markov switching model with time varying probabilities for the transitions. The novelty of our model is that the transition probabilities evolve over time by means of an observation driven model. The innovation of the time varying probability is generated by the score of the predictive likelihood function. We show how the model dynamics can be readily interpreted. We investigate the performance of the model in a Monte Carlo study and show that the model is successful in estimating a range of different dynamic patterns for unobserved regime switching probabilities. We also illustrate the new methodology in an empirical setting by studying the dynamic mean and variance behavior of U.S. Industrial Production growth. We find empirical evidence of changes in the regime switching probabilities, with more persistence for high volatility regimes in the earlier part of the sample, and more persistence for low volatility regimes in the later part of the sample.
Download or read book Financial Risk Management with Bayesian Estimation of GARCH Models written by David Ardia and published by Springer Science & Business Media. This book was released on 2008-05-08 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents in detail methodologies for the Bayesian estimation of sing- regime and regime-switching GARCH models. These models are widespread and essential tools in n ancial econometrics and have, until recently, mainly been estimated using the classical Maximum Likelihood technique. As this study aims to demonstrate, the Bayesian approach o ers an attractive alternative which enables small sample results, robust estimation, model discrimination and probabilistic statements on nonlinear functions of the model parameters. The author is indebted to numerous individuals for help in the preparation of this study. Primarily, I owe a great debt to Prof. Dr. Philippe J. Deschamps who inspired me to study Bayesian econometrics, suggested the subject, guided me under his supervision and encouraged my research. I would also like to thank Prof. Dr. Martin Wallmeier and my colleagues of the Department of Quantitative Economics, in particular Michael Beer, Roberto Cerratti and Gilles Kaltenrieder, for their useful comments and discussions. I am very indebted to my friends Carlos Ord as Criado, Julien A. Straubhaar, J er ^ ome Ph. A. Taillard and Mathieu Vuilleumier, for their support in the elds of economics, mathematics and statistics. Thanks also to my friend Kevin Barnes who helped with my English in this work. Finally, I am greatly indebted to my parents and grandparents for their support and encouragement while I was struggling with the writing of this thesis.
Download or read book Identification robust Moment based Tests for Markov switching in Autoregressive Models written by Richard Luger and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book State space Models with Regime Switching written by Chang-Jin Kim and published by Mit Press. This book was released on 1999 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Both state-space models and Markov switching models have been highly productive paths for empirical research in macroeconomics and finance. This book presents recent advances in econometric methods that make feasible the estimation of models that have both features. One approach, in the classical framework, approximates the likelihood function; the other, in the Bayesian framework, uses Gibbs-sampling to simulate posterior distributions from data.The authors present numerous applications of these approaches in detail: decomposition of time series into trend and cycle, a new index of coincident economic indicators, approaches to modeling monetary policy uncertainty, Friedman's "plucking" model of recessions, the detection of turning points in the business cycle and the question of whether booms and recessions are duration-dependent, state-space models with heteroskedastic disturbances, fads and crashes in financial markets, long-run real exchange rates, and mean reversion in asset returns.
Download or read book Regime Switching Models written by Simon van Norden and published by . This book was released on 2000 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper is a user's guide to a set of Gauss procedures developed at the Bank of Canada for estimating regime-switching models. The procedures can estimate relatively quickly a wide variety of switching models and so should prove useful to the applied researcher. Sample program listings are included. FRENCH VERSION La presente etude constitue un guide d'utilisation d'un ensemble de procedures de Gauss mises au point a la Banque du Canada en vue de l'estimation des modeles a changement de regime. Ces procedures permettent d'estimer de facon assez rapide une vaste gamme de modeles a changement de regime et devraient s'averer utiles pour la recherche appliquee. Des echantillons de programmes sont inclus dans l'etude.
Download or read book Autoregressive Moving Average Infinite Hidden Markov Switching Models written by Luc Bauwens and published by . This book was released on 2017 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov-switching models are usually specified under the assumption that all the parameters change when a regime switch occurs. Relaxing this hypothesis and being able to detect which parameters evolve over time is relevant for interpreting the changes in the dynamics of the series, for specifying models parsimoniously, and may be helpful in forecasting. We propose the class of sticky infinite hidden Markov-switching autoregressive moving average models, in which we disentangle the break dynamics of the mean and the variance parameters. In this class, the number of regimes is possibly infinite and is determined when estimating the model, thus avoiding the need to set this number by a model choice criterion. We develop a new Markov chain Monte Carlo estimation method that solves the path dependence issue due to the moving average component. Empirical results on macroeconomic series illustrate that the proposed class of models dominates the model with fixed parameters in terms of point and density forecasts.Appendix available at: 'https://ssrn.com/abstract=2965668' https://ssrn.com/abstract=2965668.
Download or read book Time Varying Transition Probabilities for Markov Regime Switching Models written by Marco Bazzi and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a new Markov switching model with time-varying transitions probabilities. The novelty of our model is that the transition probabilities evolve over time by means of an observation driven model. The innovation of the time-varying probability is generated by the score of the predictive likelihood function. We show how the model dynamics can be readily interpreted. We investigate the performance of the model in a Monte Carlo study and show that the model is successful in estimating a range of different dynamic patterns for unobserved regime switching probabilities. We also illustrate the new methodology in an empirical setting by studying the dynamic mean and variance behaviour of US industrial production growth.
Download or read book Hidden Markov Models for Time Series written by Walter Zucchini and published by CRC Press. This book was released on 2017-12-19 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hidden Markov Models for Time Series: An Introduction Using R, Second Edition illustrates the great flexibility of hidden Markov models (HMMs) as general-purpose models for time series data. The book provides a broad understanding of the models and their uses. After presenting the basic model formulation, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference for HMMs. Through examples and applications, the authors describe how to extend and generalize the basic model so that it can be applied in a rich variety of situations. The book demonstrates how HMMs can be applied to a wide range of types of time series: continuous-valued, circular, multivariate, binary, bounded and unbounded counts, and categorical observations. It also discusses how to employ the freely available computing environment R to carry out the computations. Features Presents an accessible overview of HMMs Explores a variety of applications in ecology, finance, epidemiology, climatology, and sociology Includes numerous theoretical and programming exercises Provides most of the analysed data sets online New to the second edition A total of five chapters on extensions, including HMMs for longitudinal data, hidden semi-Markov models and models with continuous-valued state process New case studies on animal movement, rainfall occurrence and capture-recapture data
Download or read book Joint Determination of the State Dimension and Autoregressive Order for Models With Markov Regime Switching written by Zacharias Psaradakis and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper is concerned with the problem of joint determination of the state dimension and autoregressive order of models with Markov-switching parameters. A model selection procedure is proposed which is based on optimization of complexity-penalized likelihood criteria. The efficacy of the procedure is evaluated by means of Monte Carlo experiments.
Download or read book Essentials of Time Series for Financial Applications written by Massimo Guidolin and published by Academic Press. This book was released on 2018-05-29 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essentials of Time Series for Financial Applications serves as an agile reference for upper level students and practitioners who desire a formal, easy-to-follow introduction to the most important time series methods applied in financial applications (pricing, asset management, quant strategies, and risk management). Real-life data and examples developed with EViews illustrate the links between the formal apparatus and the applications. The examples either directly exploit the tools that EViews makes available or use programs that by employing EViews implement specific topics or techniques. The book balances a formal framework with as few proofs as possible against many examples that support its central ideas. Boxes are used throughout to remind readers of technical aspects and definitions and to present examples in a compact fashion, with full details (workout files) available in an on-line appendix. The more advanced chapters provide discussion sections that refer to more advanced textbooks or detailed proofs. Provides practical, hands-on examples in time-series econometrics Presents a more application-oriented, less technical book on financial econometrics Offers rigorous coverage, including technical aspects and references for the proofs, despite being an introduction Features examples worked out in EViews (9 or higher)
