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.

This paper is devoted to show duality in the estimation of Markov Switching (MS) processes for volatility. It is well-known that MS-GARCH models suffer of path dependence which makes the estimation step unfeasible with usual Maximum Likelihood procedure. However, by rewriting the MS-GARCH model in a suitable linear State Space representation, we are able to give a unique framework to reconcile the estimation obtained by the Kalman Filter and with some auxiliary models proposed in the literature. Reasoning in the same way, we present a linear Filter for MS-Stochastic Volatility (MS-SV) models on which different conditioning sets yield more flexibility in the estimation. Estimation on simulated data and on short-term interest rates shows the feasibility of the proposed approach.

Specially selected from The New Palgrave Dictionary of Economics 2nd edition, each article within this compendium covers the fundamental themes within the discipline and is written by a leading practitioner in the field. A handy reference tool.

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.

This book proposes new methods to value equity and model the Markowitz efficient frontier using Markov switching models and provide new evidence and solutions to capture the persistence observed in stock returns across developed and emerging markets.