scholarly journals An Advanced Markov Switching Approach for the Modelling of Consultation Rate Data

2021 ◽  
Vol 5 (1) ◽  
pp. 2
Author(s):  
Emmanouil-Nektarios Kalligeris ◽  
Alex Karagrigoriou ◽  
Christina Parpoula
Keyword(s):  
Regime Switching ◽  
Markov Switching ◽  
Penalized Likelihood ◽  
Rate Data ◽  
Consultation Rate ◽  
Real Application

Regime switching in conjunction with penalized likelihood techniques could be a robust tool concerning the modelling of dynamic behaviours of consultation rate data. To that end, in this work we propose a methodology that combines the aforementioned techniques, and its performance and capabilities are tested through a real application.

Dynamics of a stochastic susceptible-infective-recovered (SIRS) epidemic model with vaccination and nonlinear incidence under regime switching and Lévy jumps

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Junna Hu ◽  
Buyu Wen ◽  
Ting Zeng ◽  
Zhidong Teng
Keyword(s):  
Epidemic Model ◽  
Regime Switching ◽  
Markov Switching ◽  
Threshold Value ◽  
Open Problems ◽  
Nonlinear Incidence ◽  
Sirs Epidemic Model ◽  
Stochastic Epidemic ◽  
Lévy Jumps ◽  
White Noises

Abstract In this paper, a stochastic susceptible-infective-recovered (SIRS) epidemic model with vaccination, nonlinear incidence and white noises under regime switching and Lévy jumps is investigated. A new threshold value is determined. Some basic assumptions with regard to nonlinear incidence, white noises, Markov switching and Lévy jumps are introduced. The threshold conditions to guarantee the extinction and permanence in the mean of the disease with probability one and the existence of unique ergodic stationary distribution for the model are established. Some new techniques to deal with the Markov switching, Lévy jumps, nonlinear incidence and vaccination for the stochastic epidemic models are proposed. Lastly, the numerical simulations not only illustrate the main results given in this paper, but also suggest some interesting open problems.

Dependence structure between money and economic activity: A Markov-switching copula VEC approach

2021 ◽  
pp. 1-17
Author(s):  
Apostolos Serletis ◽  
Libo Xu
Keyword(s):  
Error Correction ◽  
Economic Activity ◽  
Regime Switching ◽  
Markov Switching ◽  
Tail Dependence ◽  
Error Correction Model ◽  
Dependence Structure ◽  
Copula Models ◽  
Correction Model ◽  
Dependence Structures

Abstract This paper examines correlation and dependence structures between money and the level of economic activity in the USA in the context of a Markov-switching copula vector error correction model. We use the error correction model to focus on the short-run dynamics between money and output while accounting for their long-run equilibrium relationship. We use the Markov regime-switching model to account for instabilities in the relationship between money and output, and also consider different copula models with different dependence structures to investigate (upper and lower) tail dependence.

scholarly journals The Regime-Switching Behaviour of Exchange Rates and Frontier Stock Market Prices in Sub-Saharan Africa

2021 ◽  
Vol 14 (3) ◽  
pp. 122
Author(s):  
Maud Korley ◽  
Evangelos Giouvris
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Regime Switching ◽  
Markov Switching ◽  
Sub Saharan Africa ◽  
Var Model ◽  
High Volatility ◽  
Sub Saharan ◽  
The Relationship ◽  
Fx Market

Frontier markets have become increasingly investible, providing diversification opportunities; however, there is very little research (with conflicting results) on the relationship between Foreign Exchange (FX) and frontier stock markets. Understanding this relationship is important for both international investor and policymakers. The Markov-switching Vector Auto Regressive (VAR) model is used to examine the relationship between FX and frontier stock markets. There are two distinct regimes in both the frontier stock market and the FX market: a low-volatility and a high-volatility regime. In contrast with emerging markets characterised by “high volatility/low return”, frontier stock markets provide high (positive) returns in the high-volatility regime. The high-volatility regime is less persistent than the low-volatility regime, contrary to conventional wisdom. The Markov Switching VAR model indicates that the relationship between the FX market and the stock market is regime-dependent. Changes in the stock market have a significant impact on the FX market during both normal (calm) and crisis (turbulent) periods. However, the reverse effect is weak or nonexistent. The stock-oriented model is the prevalent model for Sub-Saharan African (SSA) countries. Irrespective of the regime, there is no relationship between the stock market and the FX market in Cote d’Ivoire. Our results are robust in model selection and degree of comovement.

scholarly journals A multivariate regime-switching GARCH model with an application to global stock market and real estate equity returns

2018 ◽  
Vol 22 (3) ◽  
Author(s):  
Markus Haas ◽  
Ji-Chun Liu
Keyword(s):  
Real Estate ◽  
Stock Market ◽  
Regime Switching ◽  
Markov Switching ◽  
Garch Model ◽  
Equity Returns ◽  
Out Of Sample ◽  
High Volatility ◽  
Simple Recursion ◽  
Global Stock

AbstractWe consider a multivariate Markov-switching GARCH model which allows for regime-specific volatility dynamics, leverage effects, and correlation structures. Conditions for stationarity and expressions for the moments of the process are derived. A Lagrange Multiplier test against misspecification of the within-regime correlation dynamics is proposed, and a simple recursion for multi-step-ahead conditional covariance matrices is deduced. We use this methodology to model the dynamics of the joint distribution of global stock market and real estate equity returns. The empirical analysis highlights the importance of the conditional distribution in Markov-switching time series models. Specifications with Student’stinnovations dominate their Gaussian counterparts both in- and out-of-sample. The dominating specification appears to be a two-regime Student’stprocess with correlations which are higher in the turbulent (high-volatility) regime.

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 THE REGIME SWITCHING OF CYCLE INSTABILITY OF ISLAMIC BANKING AND THE ECONOMY: EVIDENCE FROM INDONESIA, MALAYSIA, AND PAKISTAN

2021 ◽  
Vol 7 (2) ◽  
pp. 233-262
Author(s):  
Irfan Nurfalah ◽  
Aam Slamet Rusydiana
Keyword(s):  
Regime Switching ◽  
Markov Switching ◽  
Banking System ◽  
Research Data ◽  
Public Confidence ◽  
Islamic Banking ◽  
Z Score ◽  
Proxy Variable ◽  
The Public ◽  
Banking Stability

This study aims to examine the cyclical instability of Islamic banking in Indonesia, Malaysia, and Pakistan. A stable Islamic banking system can give the public confidence to conduct transactions and thus grow the economy. The proxy variable for stability used is the z-score, with 156 periods of research data from January 2007 to December 2019. The Markov Switching Vector Autoregression (MS-VAR) method was employed. The results show that Islamic banking stability in Indonesia based on the z-score is more stable than others. Nevertheless, in terms of the regression of all the variables, regime shifting, and the duration of the crisis, overall Malaysian Islamic banking displays the best performance. The instability of the Indonesian model is mostly affected by inflation, whereas Malaysia and Pakistan are affected by the financing to deposit ratio and the fluctuation in global oil, respectively.

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.

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.

Allocation to industry portfolios under Markov switching returns

2013 ◽  
Vol 30 (4) ◽  
pp. 317-346 ◽  
Author(s):  
Deniz Kebabci Tudor
Keyword(s):  
Portfolio Choice ◽  
Parameter Uncertainty ◽  
Regime Switching ◽  
Markov Switching ◽  
Regime Shifts ◽  
Optimal Portfolio ◽  
Switching Behavior ◽  
High Tech ◽  
Content Type ◽  
Long Run

Purpose – The purpose of this paper is to examine the effects of parameter uncertainty in the returns process with regime shifts on optimal portfolio choice over the long run for a static buy-and-hold investor who is investing in industry portfolios. Design/methodology/approach – This paper uses a Markov switching model to model returns on industry portfolios and propose a Gibbs sampling approach to take into account parameter uncertainty. This paper compares the results with a linear benchmark model and estimates without taking into account parameter uncertainty. This paper also checks the predictive power of additional predictive variables. Findings – Incorporating parameter uncertainty and taking into account the possible regime shifts in the returns process, this paper finds that such investors can allocate less in the long run to stocks. This holds true for both the NASDAQ portfolio and the individual high tech and manufacturing industry portfolios. Along with regime switching returns, this paper examines dividend yields and Treasury bill rates as potential predictor variables, and conclude that their predictive effect is minimal: the allocation to stocks in the long run is still generally smaller. Originality/value – Studying the effect of regime switching behavior in returns on the optimal portfolio choice with parameter uncertainty is our main contribution.

Sign in / Sign up

Export Citation Format

Share Document