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The purpose of this paper is to consider whether or not the introduction of inflation targeting (IT) impacts on the mean-reversion properties of inflation and output growth.

Focusing on eight Asian countries of which four are inflation-targeters, the authors employ a two-state Markov-switching model which characterizes the behavior of inflation and output growth as regime-dependent based on periods of stationarity or non-stationarity.

In contrast to a literature that offers mixed findings, the authors find the presence of stationary inflation and output growth in one regime for all IT countries, except for South Korea which is characterized by stationary output growth in both regimes. In the cases of South Korea and Thailand, IT reduces the probability of inflation remaining in a non-stationary regime. IT increases the probability of South Korea remaining in a regime of low persistence output growth. While IT is important in understanding behavior, so are other considerations such as exchange rate volatility, as well as the Asian and global financial crises.

In contrast to other unit root tests of inflation and output growth, a novelty of the approach is that the authors obtain new insights in terms of two concepts of stationarity that allow for inflation and output growth to switch between stationary and non-stationary regimes (partial stationarity), or between stationary regimes of differing degrees of persistence (varied stationarity).

This paper aims at examining the co-movement dependent regime and causality relationships between conventional and Islamic returns for emerging, frontier and developed markets from November 2008 to August 2020.

First, the authors used the Markov-switching autoregression (MS–AR) model to capture the regime-switching behavior in the stock market returns. Second, the authors applied the Markov-switching regression and vector autoregression (MS-VAR) models in order to study, respectively, the co-movement and causality relationship between returns of conventional and Islamic indexes across market states.

Results show the presence of two different regimes for the three studied markets, namely, stability and crisis periods. Also, the authors found evidence of a co-movement relationship between the conventional and Islamic indexes for the three studied markets whatever the regime. For the Granger causality, it is proved only for emerging and developed markets and only during the stability regime. Finally, the authors conclude that Islamic indexes can act as diversifiers, or safe-haven assets are not strongly supported.

This paper is the first study that examines the co-movement and the causal relationship between conventional and Islamic indexes not only across different financial markets' regimes but also during the COVID-19 period. The findings may help investors in making educated decisions about whether or not to add Islamic indexes to their portfolios especially during the recent outbreak.

This study examines the extent to which gold and silver bubbles are correlated and which metal’s bubble spills over to the other. In addition, the overlap in bubble-like episodes for the two metals is demonstrated and the influence of crises (global financial crises, European debt crisis and the COVID-19 pandemic) on the development of these episodes is compared.

This study proposes a two-step approach. In the first step, price bubbles are identified based on the backward sup augmented Dickey–Fuller of Phillips et al. (2015a, 2015b) and modified by Phillips and Shi (2018). In the second step, the correlation in the contagion effect of the bubbles between the two precious metal prices is measured using a nonparametric regression with a time-varying coefficient approach developed by Greenaway-McGrevy and Phillips (2016).

The findings suggest that the safe-haven property of gold and silver during financial market turbulence induces excessive price increases beyond their fundamental values. Furthermore, the results indicate that bubbles are contagious among precious metal markets and flow mainly from gold to silver; these findings are associated with the period after 2005, particularly during the global financial crisis. A contagious bubble effect is not found between gold and silver during the coronavirus disease 2020 pandemic.

The results suggest that financial market participants should consider portfolio weights in precious markets in light of the bubble correlation between gold and silver, especially during crises.

To the best of the authors’ knowledge, this is the first study that explores the correlation of bubble-like episodes between gold and silver.

The purpose of this study is to create a value variation measurement model to define the relationship among various roles in resource management within a service system; and divide value creation into two states (i.e. cocreation and codestruction) and use them as crucial indicators for value variation by adopting the service-dominant logic and using the Markov switching model.

This study proposed that variations in value are similar to changes in economy because both are abstract, indefinable and not easy to identify. Therefore, this study used the Markov switching model to define the state of value through value cocreation and codestruction; analyze value variations in a service system; and provide a numerical evaluation method by using the concept of probability to depict state transitions. In addition, open data from the Kaohsiung City Government’s 1999 call center were collected to address the aforementioned research objectives. The 1999 call center (service provider) offers citizens (customers) efficient consultant services to help them solve problems regarding the city government’s affairs or policies. Thus, this call center can be considered a complex service system.

This study revealed that the call center can utilize the analysis results of the Markov switching model on answer rates to predict service quality patterns. In addition, most first call resolution rates occurred under State 1 (value cocreation). To address problems caused by accidental or rare events, the call center should formulate policies to increase people and technical resources and improve service system effectiveness.

Enterprises currently focus on catering to customers’ needs and offering services through comprehensive service procedures to sustainably generate multiple values for customers, helping them to create values. Previous studies have mostly focused on analyzing the values of a service system and have failed to extensively explore actual value variations. Thus, the value variation measurement model proposed in the present study was able to analyze value variations of a set of call center data and illustrate value variations by using state transitions.

The purpose of this paper is to provide an extensive review of the monetary model of exchange rate determination which is the main theoretical framework on analyzing exchange rate behavior over the last 40 years. Furthermore, we test the flexible price monetarist variant and the sticky price Keynesian variant of the monetary model. We conduct our analysis employing a sample of 14 advanced economies using annual data spanning the period 1880–2012.

The theoretical background of the paper relies on the monetary model to the exchange rate determination. We provide a thorough econometric analysis using a battery of unit root and cointegration testing techniques. We test the price-flexible monetarist version and the sticky-price version of the model using annual data from 1880 to 2012 for a group of industrialized countries.

We provide strong evidence of the existence of a nonlinear relationship between exchange rates and fundamentals. Therefore, we model the time-varying nature of this relationship by allowing for Markov regime switches for the exchange rate regimes. Modeling exchange rates within this context can be motivated by the fact that the change in regime should be considered as a random event and not predictable. These results show that linearity is rejected in favor of an MS-VECM specification which forms statistically an adequate representation of the data. Two regimes are implied by the model; the one of the estimated regimes describes the monetary model whereas the other matches in most cases the constant coefficient model with wrong signs. Furthermore it is shown that depending on the nominal exchange rate regime in operation, the adjustment to the long run implied by the monetary model of the exchange rate determination came either from the exchange rate or from the monetary fundamentals. Moreover, based on a Regime Classification Measure, we showed that our chosen Markov-switching specification performed well in distinguishing between the two regimes for all cases. Finally, it is shown that fundamentals are not only significant within each regime but are also significant for the switches between the two regimes.

The results are of interest to practitioners and policy makers since understanding the evolution and determination of exchange rates is of crucial importance. Furthermore, our results are linked to forecasting performance of exchange rate models.

The present analysis extends previous analyses on exchange rate determination and it provides further support in favor of the monetary model as a long-run framework to understand the evolution of exchange rates.

This research examines how macroeconomic variables can precisely predict bull/bear stock markets in China and Taiwan.

This paper adopts a two-state Markov switching model to characterize the bull and bear markets spanning from 1994 to 2019 and then conduct a bear stock market predictability test by running regressions between the filtered probabilities of bear markets and a series of macroeconomic variables in turn at different horizons of 1, 3, 6, 12 and 24 months.

This paper shows that inflation rates, changes in real exchange rates, and foreign currency reserve growth are key predictors of bear markets in China, while term spreads, unemployment rates and foreign reserve growth are major factors that can predict bear markets in Taiwan. Remarkably, industrial production growth does not have predictive power for bear markets, which may suggest emerging markets are driven by fund flows rather than real economic activities. Besides, the impact directions of foreign currency reserve growth are opposite, which may be due to different proportions of the financial accounts in their balance of payments.

In practical respect, this paper provides market participants the usefulness, impact direction and implications of bear market predictors when building their market-timing strategies in China and Taiwan stock markets. The government institutions may also thereby make appropriate policies to prevent huge stock market downturns and serious drawbacks.

It highlights the “fund-driven market hypothesis” and “foreign currency reserve effects” that commonly dominate Taiwan and China stock markets since both are highly affected by international funds.

The aim of this study was to determine if there are asymmetries in the pass-through of West Texas Intermediate (WTI) crude oil prices to its derivatives (diesel and gasoline) in the Brazilian market.

Initially, the future WTI oil price series was analyzed using the self-exciting threshold autoregressive (SETAR) and logistic smooth transition autoregressive (LSTAR) non-linear models. Subsequently, the threshold autoregressive error-correction model (TAR-ECM) and Markov-switching model were used.

The findings indicated high prices throughout 2008 due to the subprime crisis. The findings indicated high prices throughout 2008 due to the subprime crisis. The results indicated that there is long-term pass-through of oil prices in both methods, suggesting an equilibrium adjustment in the prices of diesel and gasoline in the analyzed period. Regarding the short term, the variations in contemporary crude oil prices have positive effects on the variations in fuel prices. Lastly, this behavior can partly be explained by the internal price management structure adopted during almost all of the analyzed period.

This paper contributes to the literature at some points. The first contribution is the modeling of the oil price series through non-linear models, further enriching the literature on the recent behavior of this time series. The second is the simultaneous use of the TAR-ECM and Markov-switching model to capture possible short- and long-term asymmetries in the pass-through of prices, as few studies have applied these methods to the future price of oil. The third and main contribution is the investigation of whether there are asymmetries in the transfer of oil prices to the price of derivatives in Brazil. So far, no work has investigated this issue, which is very relevant to the country.

Introduction – Increases in prices of commodity markets may be associated with increased volatility in financial markets. That is why analysing time-varying co-movements of commodity prices can be of great importance for investors who take into consideration optimal asset allocation.

Purpose – The aim of this study is to investigate the volatility spillover from oil to precious metals under high-volatility and low-volatility regimes.

Methodology – The data covered daily closing prices of assets such as oil, palladium, and platinum for the period January 2010–December 2018. GARCH models were analysed in order to determine the most appropriate volatility structure, and it was determined that GARCH (1,1) model was the most suitable model for all commodities. Markov Switching model was used to analyse the volatility spillover from oil to precious metals.

Findings – According to the analyses, the results showed that there were volatility spillovers from oil to palladium and platinum in low-volatility regimes and from oil to platinum in high-volatility regimes. On the other hand, there was no volatility spillover from oil to palladium in high-volatility regimes. Investing into oil and palladium in the same portfolio can provide diversification benefits for investors in high-volatility regimes. On the other hand, investing into oil and palladium in the same portfolio may not provide diversification benefits for investors in low-volatility regimes. The findings of the analyses can be beneficial for investors, market participants, and portfolio managers to make an accurate portfolio management.

This study aims to analyze the impact of the crude oil market on the Toronto Stock Exchange Index (TSX).

The focus is on detecting nonlinear relationship based on monthly data from 1970 to 2021 using Markov-switching vector auto regression (VAR) model.

The results indicate that TSX return contains two regimes: positive return (Regime 1), when growth rate of stock index is positive; and negative return (Regime 2), when growth rate of stock index is negative. Moreover, Regime 1 is more volatile than Regime 2. The findings also show the crude oil market has a negative effect on the stock market in Regime 1, while it has a positive effect on the stock market in Regime 2. In addition, the authors can see this effect in Regime 1 more significantly in comparison to Regime 2. Furthermore, two-period lag of oil price decreases stock return in Regime 1, while it increases stock return in Regime 2.

This study aims to address the effect of oil market fluctuation on TSX index using Markov-switching approach and capture the nonlinearities between them. To the best of the author’s knowledge, this is the first study to assess the effect of the oil market on TSX in different regimes using Markov-switching VAR model. Because Canada is the sixth-largest producer and exporter of oil in the world as well as the TSX as the Canada’s main stock exchange is the tenth-largest stock exchange in the world by market capitalization, this paper’s framework to analyze a nonlinear relationship between oil market and the stock market of Canada helps stock market players like policymakers, institutional investors and private investors to get a better understanding of the real world.

The study aims to examine the relationship among economic policy uncertainty (EPU), geopolitical-risks (GPR), the interaction (EPGR) of EPU and GPR and the returns of gold, silver, platinum, palladium and rhodium using monthly data from January (1997) to May (2021).

The paper employs the Markov-switching and the novel Shi et al. (2020) bootstrap time-varying Granger-causality approach.

Though the Markov-switching shows variation in the responses of precious metals to EPU, GPR and EPGR across low and high states, the paper observes the safe-haven potential of the precious metals in the high regime while the hedging potency is also evident in the results. To further substantiate the safe-haven and hedging properties, the time-varying Granger-causality shows the causal effect of EPU on all the selected precious metal returns coinciding with global events. While the authors show that GPR Granger causes platinum, palladium and rhodium consistently under the rolling/recursive-evolving tests, the authors cannot find the causal effect of GPR on gold and silver returns across the algorithms. The paper also observes persistence in the causal effect of EPGR on palladium and platinum across all the algorithms, while gold and rhodium only show consistency in the responses under the rolling- and recursive-evolving algorithms given the conditions of homoscedasticity and heteroscedasticity.

The authors' results are essential to investors and policymakers since both typically leverage the hedging and safe-haven characteristics of precious metals to obviate downside risks during highly uncertain periods.

The authors' techniques allow examining the hedging and safe-haven properties of precious metals across regimes and date-stamp critical periods of causation inherent in the relationship.