AN N-STATE ENDOGENOUS MARKOV-SWITCHING MODEL WITH APPLICATIONS IN MACROECONOMICS AND FINANCE

2019 ◽  
pp. 1-29
Author(s):  
Shih-Tang Hwu ◽  
Chang-Jin Kim ◽  
Jeremy Piger
Keyword(s):  
Regime Switching ◽  
Likelihood Function ◽  
Markov Switching ◽  
Ratio Test ◽  
Markov Switching Model ◽  
Switching Model ◽  
Finite Samples ◽  
Endogenous Switching ◽  
Volatility Model ◽  
Three State Model

We develop an N-regime Markov-switching model in which the latent state variable driving the regime switching is endogenously determined with the model disturbance term. The model’s structure captures a wide variety of patterns of endogeneity and yields a simple test of the null hypothesis of exogenous switching. We derive an iterative filter that generates objects of interest, including the model likelihood function and estimated regime probabilities. Using simulation experiments, we demonstrate that the maximum likelihood estimator performs well in finite samples and that a likelihood ratio test of exogenous switching has good size and power properties. We provide results from two applications of the endogenous switching model: a three-state model of US business cycle dynamics and a three-state volatility model of US equity returns. In both cases, we find statistically significant evidence in favor of endogenous switching.

scholarly journals Markov Switching Model Analysis of Implied Volatility for Market Indexes with Applications to S&P 500 and DAX

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Luca Di Persio ◽  
Samuele Vettori
Keyword(s):  
Regime Switching ◽  
Implied Volatility ◽  
Financial Time Series ◽  
Markov Switching ◽  
Sampling Procedure ◽  
Model Parameters ◽  
Classical Approach ◽  
Markov Switching Model ◽  
Discrete State ◽  
Switching Model

We adopt aregime switchingapproach to study concrete financial time series with particular emphasis on their volatility characteristics considered in a space-time setting. In particular the volatility parameter is treated as an unobserved state variable whose value in time is given as the outcome of an unobserved, discrete-time and discrete-state, stochastic process represented by a suitable Markov chain. We will take into account two different approaches for inference on Markov switching models, namely, the classical approach based on the maximum likelihood techniques and the Bayesian inference method realized through a Gibbs sampling procedure. Then the classical approach shall be tested on data taken from theStandard & Poor’s 500and theDeutsche Aktien Indexseries of returns in different time periods. Computations are given for a four-state switching model and obtained numerical results are put beside by explanatory graphs which report the outcomes obtained exploiting both smoothing and filtering algorithms used in the estimation/calibration procedures we proposed to infer on the switching model parameters.

scholarly journals TESTING FOR REGIME SWITCHING IN SINGAPOREAN BUSINESS CYCLES

2005 ◽  
Vol 50 (01) ◽  
pp. 25-34 ◽  
Author(s):  
ROBERT BREUNIG ◽  
ALISON STEGMAN
Keyword(s):  
Model Selection ◽  
Business Cycles ◽  
Nonlinear Model ◽  
Regime Switching ◽  
Markov Switching ◽  
Markov Switching Model ◽  
Switching Model ◽  
Intended Use ◽  
Better Than ◽  
Model Fits

We examine a Markov-Switching model of Singaporean GDP using a combination of formal moment-based tests and informal graphical tests. The tests confirm that the Markov-Switching model fits the data better than a linear, autoregressive alternative. The methods are extended to allow us to identify precisely which features of the data are better captured by the nonlinear model. The methods described here allow model selection to be related to the intended use of the model.

scholarly journals Unemployment rate and wage growth in Brazil: evidence from a Markov-switching model

2020 ◽  
Vol 24 (2) ◽  
pp. 171-194
Author(s):  
Wellington Charles Lacerda Nobrega ◽  
Cássio da Nóbrega Besarria ◽  
Felipe Araújo De Oliveira
Keyword(s):  
Unemployment Rate ◽  
Regime Switching ◽  
Phillips Curve ◽  
Markov Switching ◽  
Wage Growth ◽  
New Keynesian ◽  
Switching Regression ◽  
Markov Switching Model ◽  
Switching Model ◽  
Key Factor

This paper has the purpose to investigate the relationship between unemployment rate and wage growth for the Brazilian economy from 2000to 2016, by means of a Markov-switching regression model. The empirical approach is based on the New-Keynesian Phillips Curve developed by Galí (2011). The estimation results suggest the existence of two well definedregimes, one characterized by the non-validation of the Phillips Curve, while in the other the trade-off between unemployment and wageinflation is validated, with the economic cycle being a key factor in regime switching.

scholarly journals Risk Analysis Of Hedge Funds: A Markov Switching Model Analysis

2013 ◽  
Vol 30 (1) ◽  
pp. 243 ◽  
Author(s):  
Frederic Teulon ◽  
Khaled Guesmi ◽  
Saoussen Jebri
Keyword(s):  
Risk Analysis ◽  
Hedge Funds ◽  
Regime Switching ◽  
Emerging Market ◽  
Markov Switching ◽  
Hedge Fund ◽  
Markov Switching Model ◽  
Market Strategy ◽  
Switching Model ◽  
High Volatility

The paper applies Markov Regime Switching GARCH Model (SW-GARCH) to investigate the volatility behavior of strategies hedge fund monthly returns for the period 1997-2011. The results highlight two different regimes: The first regime is characterized by a high volatility for all strategies hedge fund monthly returns. The second is characterised by lower volatility and positive average returns (except Emerging Market strategy). Our results helped to capture even the short-lived crises along with the material crises of 2001 and 2008.

A Markov-Switching model for building occupant activity estimation

2019 ◽  
Vol 183 ◽  
pp. 672-683 ◽  
Author(s):  
Sebastian Wolf ◽  
Jan Kloppenborg Møller ◽  
Magnus Alexander Bitsch ◽  
John Krogstie ◽  
Henrik Madsen
Keyword(s):  
Markov Switching ◽  
Markov Switching Model ◽  
Switching Model ◽  
Activity Estimation
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