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This paper aims to investigate the pass-through (PT) effect in Turkey by using quarterly data for the period 1998: Q1-2019: Q2 to understand the dynamic potential effects of exchange rates on domestic prices.

The paper launches several nonlinear models in which the basic determinants of domestic prices in Turkey are determined through Markov regime-switching models (MSMs). Hence, this research follows the variables of the consumer price index (CPI), USD exchange rate, gross domestic product (GDP; demand side of the economy), industrial production index (production side of the economy), economic uncertainty and geopolitical risk index for Turkey.

This work explores that the exchange rate and demand side of the economy (GDP) follow a positive nonlinear relationship with CPI at both regimes. The production side of the economy (IP) affects negatively the CPI during regime 0. Economic uncertainty influences the CPI positively at Regime 1, while geopolitical risk has a negative association with CPI at Regime 0. Eventually, the paper provides some policy proposals associated with the impacts of GDP, IP, economic uncertainty and geopolitical risk on CPI in Turkey.

One may claim that any PT model, which does not observe the possible structural or regime shifts in estimated parameters, might fail to estimate the coefficients unbiasedly and efficiently. Hence, this work differs from available relevant works in the literature since this paper considers linearity or nonlinearity important and reveals that the relevant PT model follows a nonlinear path rather than a linear path, this nonlinear path is converged strongly by MSMs and estimates the significant regime shifts in the constant term and, in parameters of independent variables of PT by MSMs.

This study aims to examine the transmission mechanism of factors on the characteristic fluctuation of Vietnamese coffee bean export price (PVN).

Applying Markov switching–vector autoregressive model.

Significantly, the empirical results showed that the transmission of independent variables on PVN is non-linear, and the fluctuation of PVN is affected by many factors, especially PVN in the previous period. In addition, the effect of Robusta coffee price was the greatest with coefficient is 0.28785, and the correlation between PVN and it was also the highest in both regimes with coefficients are 0.5317 and 0.3959, respectively.

These obtained results are in accordance with reality, as Vietnam is the largest exporter of Robusta coffee in the world.

In the context of strategic marketing and market commitment models, modeling of brand switching behaviour of consumers is of extreme use in determining the performance potential of the existing players. This paper revisits the static models of Ehrenberg and Hendry and introduces a dynamic model of brand switching based on market shares. In terms of a χ² measure of closeness, these three models have been compared to ascertain their modeling performances. A case study has been carried out for the field of sanitary napkin to give some further insight into this problem. Studies have also been included for a few FMCG items. In all the cases, the proposed model gives a better description of the actual situation than the existing static models.

The objective of this paper is twofold. First, to study the safe-haven characteristic of the Islamic stock indexes and Ṣukūk during the crises time. Second, to evaluate this property in the last pandemic. This study employs the daily dataset from June 15, 2015, to June 15, 2020, for the most affected countries by the earlier disease.

This study uses the Markov-switching Capital Asset Pricing Model (CAPM) approach and the basic CAPM for the main analysis and the safe haven index (SHI) recently developed by Baur and Dimpfl (2021) for the robustness test.

Based on Baur and Lucey's (2010) definition, empirical findings indicate that Islamic stock indexes cannot be a refuge throughout the crisis regime for all selected conventional markets. However, Ṣukūk are a strong refuge in Brazilian, Russian and Malaysian markets. For the remainder countries, except Italy, the USA and Spain, the Ṣukūk index offers weak protection against serious conventional market downturns. Similar conclusions are obtained during the COVID-19 global crisis period. Finally, results are confirmed by using the SHI.

To the best of the authors’ knowledge, this paper is the first study that evaluates the safe haven effectiveness of the Islamic index and Ṣukūk using the SHI in the most impacted countries by the COVID-19 outbreak.

The purpose of this paper is to explore the direct and exclusive effects of this rather unconventional monetary policy on financial markets, economic activity and labor markets across the Eurozone.

Using a range of variables, the analysis employed the Markov-switching dynamic regression methodological approach.

The findings provided evidence in favor of the reduction of short- and log-term credit spreads, increased stock prices, improved market expectations, recovered labor market conditions and economic productivity, while the primary transmission channel of the quantitative easing policy is the expectations channel.

The novelties of this paper are twofold: it makes use of a wide data set to investigate the effect of economic and financial variables on productivity, labor markets, bond markets and equity markets in the Eurozone; and the analysis focuses on the direct effects of monetary base increases on the Eurozone economy, as well as on Eurozone financial markets.

The study aims to understand the role of different streams of oil shocks (demand, supply and risk shocks) on the oil-importing and exporting countries' stock returns. The study also examines the impact of crude oil shocks across the economic regimes and market states. Besides, the role of the Global Financial Crisis (GFC) of 2008 in shaping the oil–stock relationship is also investigated.

The authors revisit the impact of oil shocks on emerging equity markets by using the novel shock decomposition algorithm proposed by Ready (2018). The authors consider 24 emerging equity markets for the period spanning over July 15, 2002, to June 18, 2018, and bifurcate them based on oil dependence. The authors use rolling and dynamic conditional correlation analysis to understand the time-varying co-movements between oil prices and stock returns. The regime and state-specific dependence of stock returns on the structural oil shocks are captured by the Markov regime switching and quantile regression models.

The authors find that the demand shocks are positively associated with stock markets, whereas the supply shocks are negatively related, except in some of the oil-exporting countries. The risk-based shocks also appear to have a negative association with stocks. The authors do not find evidence of strong regime dependence and the direction of relationship across the high and low regimes is somewhat stable. Further, the authors observe an intense oil–stock relationship in the bearish market conditions. Besides, the authors also report evidences of changes in oil–stock relationship onset the GFC.

This is among the first studies to use the oil shock decomposition algorithm of Ready (2018) in the context of emerging equity markets. Additionally, oil shocks' role on the stock market movements across the regimes and market states is studied comprehensively. Thus, the nature of oil shock and the extent to which the emerging markets are exposed is observed in this study.

One of the most important sources of energy in the world, due to its great impact on the global economy, is the crude oil. Due to the instability of oil prices which exhibit extreme fluctuations during periods of different times of market uncertainty, it became hard to the governments to predict accurately the prices of crude oil in order to build their financial budgets. Therefore, this study aims to analyse and model crude oil price using the hidden Markov process (HMM).

Traditional mathematical approaches of time series may be not give accurate results to measure and analyse the crude oil price, since the latter has an unstable and fluctuating nature, hence, its prediction forms a challenge task. A novel methodology that is so-called the HMM is proposed that takes into account the heterogeneity in prices as well as their hidden state-based behaviour.

Using the Bayesian approach, several estimated models with different ranks are fitted to a non-homogeneous data of Iraqi crude oil prices from January 2010 into December 2021. The model selection criteria and measures of the prediction performance of each model are applied to choose the best model. Movements of crude oil prices exhibit extreme fluctuations during periods of different times of market uncertainty. The processes of model estimation and the model selection were conducted in Python V.3.10, and it is available from the first author on request.

Using the Bayesian approach, several estimated models with different ranks are fitted to a non-homogeneous data of Iraqi crude oil prices from January 2010 to December 2021.

The paper aims to prove that between 1992 and 2016, people in poverty as a proportion of the total population has not been reduced. In particular, food poverty (FP) represented an average of 22%, despite the fact that gross domestic product (GDP) per capita and GDP, social development expenditure and food programme expenditure (both as GDP proportion) grew by 0.96%, 1.9%, 2.7% and −17.4% on an annual average, respectively.

There are non-linear relationships between economic growth and food poverty expenditure to reduce poverty. Three econometric models were estimated as follows: a linear model [ordinary least squares (OLS)] that addresses the capability of the economic growth to reduce FP (which detects a structural change in 2007) and three models of regime change (Markov–Switching Regression) that prove the existence of two different regimes.

The author proved that economic growth has lost the capability to reduce poverty and that there are decreasing effects of expenditure in addressing poverty since 2007. These results point out that Mexico is in a poverty trap and suggests that for the economy as for life and even more in the case of social (public) policies, more is not necessarily better than less. Therefore, the author suggests that the resources allocated in response to poverty may well have generated perverse incentives that yielded the opposite results.

There is no official measure of the public expenditure for poverty. Therefore, an accurate series was built to estimate the government effort and do the econometrics that proves the main hypothesis. This is another contribution.

Previous studies have shown that the effectiveness of monetary policy depends, to a large extent, on the market expectations of its future actions. This paper proposes an econometric framework to address the effect of the current state of the economy on monetary policy expectations. Specifically, we study the effect of contractionary (or expansionary) demand (or supply) shocks hitting the euro area countries on the expectations of the ECB's monetary policy in two stages. In the first stage, we construct indexes of real activity and inflation dynamics for each country, based on soft and hard indicators. In the second stage, we use those indexes to provide assessments on the type of aggregate shock hitting each country and assess its effect on monetary policy expectations at different horizons. Our results indicate that expectations are responsive to aggregate contractionary shocks, but not to expansionary shocks. Particularly, contractionary demand shocks have a negative effect on short-term monetary policy expectations, while contractionary supply shocks have negative effect on medium- and long-term expectations. Moreover, shocks to different economies do not have significantly different effects on expectations, although some differences across countries arise.

This paper shows that volatility persistence in GARCH models and spurious long memory in autoregressive models may arise if the possibility of structural changes is not incorporated in the time series model. It also describes a tractable hidden Markov model (HMM) in which the regression parameters and error variances may undergo abrupt changes at unknown time points, while staying constant between adjacent change-points. Applications to real and simulated financial time series are given to illustrate the issues and methods.