Two-state Markov Switching Model Research Articles

Inference and forecasting phase shift regime of COVID-19 sub-lineages with a Markov-switching model.

The occurrences and domination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are still crucial factors for determining the coronavirus disease 19 (COVID-19) policies. We collected weekly Phylogenetic Assignment of Named Global Outbreak sub-lineages, naming genetically distinct lineages of SARS-CoV-2, including variants of concern, in the United Kingdom, South Africa, South Korea, Denmark, Germany, the United States, and worldwide. This study included 12,296,756 samples of the max share of the sub-lineages from the 33rd week of 2020 to the 40th week of 2022. This study conducted a two-state Markov-switching model to estimate the probability of the phase shift state and predicted the probability of each regime with the Hamilton filter and Kim's smoothing algorithm. We discovered different weekly patterns based on dominant SARS-CoV-2 variants in target area. Due to differences in containment policies and outbreak waves, we observed a time lag in dominant variants in these countries. Using the inferred probability of the phase shift regime for forecasting, it showed significant probabilities that the stable phase will still be stable in the next week. It also showed significant probabilities that the unstable phase will still be unstable in the next week. Our findings present the probability of observing the phase shift regime each week. Until a new SARS-CoV-2 variant occurs, the regime tended to stay with a low probability of phase shift regime. When a new SARS-CoV-2 variant would occur, the regime would immediately react and change the probability. We propose the Markov-switching model to determine COVID-19 policies and predict SARS-CoV-2 variants. IMPORTANCE Using regime-switching models, we attempted to determine whether there is a link between changes in severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) variants and infection waves, as well as forecasting new SARS-Cov-2 variants. We believe that our study makes a significant contribution to the field because it proposes a new approach for forecasting the ongoing pandemic, and the spread of other infectious diseases, using a statistical model which incorporates unpredictable factors such as human behavior, political factors, and cultural beliefs.

Is energy transition possible for oil-producing nations? Probing the case of a developing economy

An expected decline in oil revenue and a complicated global energy market present significant concerns about oil-producing countries’ intentions to move away from fossil fuels. Understanding the dynamics of these concerns, particularly in light of potential energy transitions, is crucial for future energy supply and sustainable energy discussion. Given the scarcity of research on the issue, this study examined whether an implied shift from fossil fuels to a cleaner energy state is possible for Ghana, a small oil-producing economy in sub-Saharan Africa. A two-state Markov Switching Model (MSM) was applied to a dataset covering 1980–2019. Results based on a multivariate state-dependent regression technique were documented as follows: First, the probability of transitioning from a nonrenewable energy state to a renewable energy state is 76.5%. Second, there is 80.2% chance of remaining in a renewable energy state for five years. Third, fossil fuel production undermines cleaner energy development as 1% growth in nonrenewable energy causes a 0.20% decline in renewable energy generation. Finally, trade openness and foreign direct investment promote cleaner energy growth. The study concluded that Ghana’s economy has a greater chance of transitioning from fossil fuels and an equally higher chance of realizing a cleaner energy state, despite a declining policy support for renewable energy development.

Economic policy uncertainty and commodity market volatility: implications for economic recovery.

As a consequence of the COVID-19 pandemic outbreak, most commodities experienced significant price drops, which were expected to continue well into 2020. As a result, the Markov switching model is used to study the influence of policy uncertainty and the COVID-19 pandemic on commodity prices in the USA. Commodity markets are stimulated by economic policy uncertainty, according to results from a two-state Markov switching model. In both high and low regimes, economic policy uncertainty (EPU) influences the commodity market, according to the study’s findings. However, in the high regime, EPU has a greater influence on the energy and metal sectors. EPU has different influences on commodity markets in high- and low-volatility regimes, according to this study. There is a wide range of correlations between COVID-19 outcomes and EPU and how the prices of natural gas, oil, corn, silver, soybean, copper, gold, and steel respond to these tremors, in both high- and low-volatility tenure. Oil and natural gas, on the other hand, are unaffected by shifts in COVID-19 death rates under either regime. Results show that in both high- and low-volatility regimes, the demand and supply for most commodities are responsive to historical prices.

Macroeconomic variables for predicting bear stock markets of Taiwan and China

PurposeThis research examines how macroeconomic variables can precisely predict bull/bear stock markets in China and Taiwan.Design/methodology/approachThis 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.FindingsThis 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.Practical implicationsIn 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.Originality/valueIt 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.

Pricing effects of the electricity market reform in Brazil

We examine electricity market reform in Brazil. From 1998 until 2004, the largely hydro-powered market featured a forward market in which bilateral contracts were negotiated. In March 2004, this was partially replaced by a centralised clearing of forward positions, with the objective of stabilising electricity prices. We develop a theoretical model for the Brazilian forward electricity market, showing how generator entry would be affected by the two regimes. We then model monthly prices using a two-state Markov Switching model, allowing offtakes to affect prices, volatility and transition probabilities. Consistent with our theory, we show that while the reforms decreased volatility in some regions and states, the market became both more likely to enter a high price state, and more likely to remain in that state for a protracted period.

Static and regime-dependent herding behavior: An emerging market case study

We contribute to behavioral finance literature by demonstrating the relative superiority of a dynamic regime-sensitive approach in unraveling herding phenomenon. Employing daily data in Bursa Malaysia from 1995 to 2016, we first apply two orthodox techniques: cross-sectional standard deviation of returns (CSSD) model of Christie and Huang (2005) and the cross-sectional absolute dispersion (CSAD) model of Chang et al. (2000). The ensuing results appear inconsistent, with only one model capturing herding. In contrast, the dynamic approach with a two-state Markov Switching model reveals that herding is a heavily regime-dependent and non-linear phenomenon. A deeper dive via sectoral decompositions shows that the financial sector and large- and mid-capitalization segments are more herding-prone.

Regime-specific impact of financial reforms on economic growth in Pakistan

This study uses and proposes a new methodological approach to construct a financial liberalization index on the basis of the dynamic factor model technique. The resulting index is used to investigate the impact of the financial sector reforms in Pakistan on economic growth. Using the Markov regime-switching model over the period 1972–2015, the empirical results showed that the examined relationship is nonlinear, nonmonotonic, state-dependent, and better described by the two-state Markov switching model characterized by the high growth regime and low growth regime. Despite the positive impact of financial liberalization on economic growth in both the high and low growth regimes, financial liberalization relatively strongly affects real GDP growth in the high growth regime. The results further demonstrate that transition probabilities establish an inordinate episode of the low growth regime. Furthermore, the high growth regime is relatively short-lived than the low growth regime. Among the other variables, trade openness and physical capital stock have a positive impact on economic growth, while labor force and government expenditure exert a negative effect on economic growth. Several economic policies are proposed and discussed for better functioning of financial sector development in Pakistan.

Static and Regime-Dependent Herding Behavior: An Emerging Market Case Study

We contribute to behavioral finance literature by demonstrating the relative superiority of a dynamic regime-sensitive approach in unravelling herding phenomenon. Employing daily data in Bursa Malaysia from 1995 to 2016, we first apply two orthodox techniques: cross-sectional standard deviation of returns (CSSD) model of Christie and Huang (2005) and the cross-sectional absolute dispersion (CSAD) model of Chang et al. (2000). The ensuing results appear inconsistent, with only one model capturing herding. In contrast, the dynamic approach with a two-state Markov Switching model reveals that herding is a heavily regime dependent and non-linear phenomenon. A deeper dive via sectoral decompositions shows that the financial sector and large- and mid-capitalization segments are more herding-prone.

Uncertainty and energy extraction

ABSTRACT We examine the effects of uncertainty by investigating the effects of oil price uncertainty on employment in domestic oil and gas extraction. This industry is interesting to examine for two reasons. First, employment in this industry should be closely linked to oil prices, facilitating identification of more subtle economic relations related to uncertainty. For example, recent research finds that uncertainty about oil prices tends to diminish drilling activity in a small sample of drilling rigs in Texas. Second, the response of this industry to negative shocks to the supply of oil, for example, should be opposite that of aggregate employment. We characterize the univariate properties of the time series with a two-state Markov switching model, and we then estimate a vector autoregression with conditionally heteroskedastic errors, in which the conditional variance is permitted to interact with the conditional mean. We find that employment in oil and gas extraction cycles through episodes of boom and bust that are tightly linked to oil prices, in contrast to aggregate employment. We also find that uncertainty about oil prices has a negative and quantitatively large effect on employment in this sector. Our analysis is robust to different measures of uncertainty, including stochastic volatility and implied volatility.

Markov-Switching Model of Family Income Quintile Shares

This paper examines the impact of unemployment and inflation on family income shares. Unemployment and inflation are decomposed into their structural (long-term), cyclical (short-term), anticipated and unanticipated components, respectively. I propose a new framework to analyze family income quintile shares by using a two-state Markov-switching model, also known as Hamilton’s regime-switching model. The benefit of using a Markov-switching model is that it permits capturing complex dynamic behavior of non-linear time series macroeconomic variables. The switching mechanism follows a first-order Markov chain of unobservable state variables and subsequent estimated conditional transition probabilities. This paper applies a two-state regime-switching process to capture the asymmetrical effects of unemployment and inflation on family income shares. The two states of family income quintile shares are state 1 (lower income) and state 2 (upper income). This approach differs from previous studies because it examines income inequality within each quintile rather than between quintiles. It is well documented in the literature that there is income inequality between lower and higher income quintiles. Therefore, the analysis of income inequality within the quintile allows for greater insight into changes in family income share corresponding to changes in the state variables. The general findings are that unemployment has adverse effects on lower income shares while inflation acts like a progressive tax on higher income shares.

Option-like properties in the distribution of hedge fund returns

In recent years hedge funds have become more popular because of their low correlation with traditional investments and their ability to generate positive returns with a relatively low volatility. However, a closer look at those high performing hedge funds raises the question if the performance is truly superior and if the high management fees are justified. Incurring no alpha costs, so-called passive hedge fund replication strategies raise the question if they can lead to similar performance in a more efficient way at lower costs. This paper investigates two different model approaches for the equity long/short strategy, where weighted segmented linear regression models are employed in combination with two-state Markov switching models. The main finding is evidence of a short put option structure, i.e. short the equity market volatility, with the put structure present in all market states. We obtain evidence that the hedge fund managers decrease their short-volatility profile during turbulent markets.

Financial development–economic growth nexus in Pakistan: new evidence from the Markov switching model

This paper investigates the impact of financial development on economic growth in Pakistan using the Markov Switching Model over the period 1980–2017. The results based on two-state Markov switching model confirm the Schumpeter’s view that finance spurs growth. The result reveals that financial development augments economic growth in both high and low economic growth regimes in Pakistan. However, the impact of financial development on economic growth is found to be relatively higher in the high-growth regime. This implies that economic growth responds differently to financial development in low-growth and high-growth regimes. Among the control variables, trade openness and government expenditures impact economic growth positively, while labour force exerts a negative impact on economic growth.

Time-varying relationship between corporate governance and expected stock returns

This paper aims to analyze a time-varying relationship between corporate governance and expected stock returns in Thailand. The time variation of corporate governance premium is estimated by macroeconomic determinants using a two-state Markov switching model. The results indicate the presence of asymmetries in the variations of corporate governance premium over the Thai economic cycles. Investors can take advantage of the time-varying characteristics with the adaptation of switching investment strategy. Incorporation of style switching strategy with value premium in recessions and momentum premium in expansions improves expected returns of corporate governance-sorted portfolios.

Does inflation targeting matter for the behavior of inflation and output growth? Some regime-based evidence for Asian economies

PurposeThe 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.Design/methodology/approachFocusing 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.FindingsIn 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.Originality/valueIn 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).

Estimating Stock Price in Energy Market Including Oil, Gas, and Coal: The Comparison of Linear and Non-Linear Two-State Markov Regime Switching Models

A common method to study the dynamic behavior of macroeconomic variables is using linear time series models; however, they are unable to explain nonlinear behavior of the series. Given the dependency between stock market and derivatives, the behavior of the underlying asset price can be modeled using Markov switching process properties and the economic regime significance. In this paper, a two-state Markov switching model in energy market has been examined for oil, coal, and gas since 1991 to 2011. The objective price estimated by the switching model and the parameters were determined by using MATLAB program. With regard to the relationship between the total price and the variables defined in this paper, it is concluded that the non-linear model is relatively better than the linear model, since it has lower RMSE and greater R-squared, therefore it is better to use nonlinear model in Markov switching model for predicting the price of stocks.

Revisiting the Relationship between Military Expenditure and Economic Growth in Pakistan

This study aims to examine relationship of military expenditure and economic growth in different phases of military regimes in the context of Pakistan. This study uses two-state Markov switching models with Constant Transition Probability (CTP) and Time Varying Transition Probabilities (TVTP) for the time period: 1973-2014. This investigation analyses two sorts of relations between military expenditures and economic development through fixed transition probability Markov exchanging models. To begin with, there is negative connection between GDP growth and military expenditures during a high variance state (i.e. having low economic growth). Second, there is positive relation between both variables, during low variance state (i.e. having higher economic growth) which is also supported by idea of Keynesian income multiplier. Another, empirical test of time varying transition probability model was used to capture the switch through indicator variable. Results of the study suggest that chances of switching are increased from low to high economic growth. The chances of switching increase from lower to higher economic growth period (or high variance period) if non-military expenditure increases. The study concludes that military expenditure and economic growth are state dependent. If conditions of economy are stable then increase of expenditure results in positive outcomes, otherwise, it affects negatively. Empirical findings suggest that military spending should be planned in accordance to the economic performance of the country.

Estimating Stock Price in Energy Market Including Oil, Gas, and Coal: The Comparison of Linear and Non- Linear Two-State Markov Regime Switching Models

A common method to study the dynamic behavior of macroeconomic variables is using linear time series models; however, they are unable to explain nonlinear behavior of the series. Given the dependency between stock market and derivatives, the behavior of the underlying asset price can be modeled using Markov switching process properties and the economic regime significance. In this paper, a two-state Markov switching model in energy market has been examined for oil, coal, and gas since 1991 to 2011. The objective price estimated by the switching model and the parameters were determined by using MATLAB program. With regard to the relationship between the total price and the variables defined in this paper, it is concluded that the non-linear model is relatively better than the linear model, since it has lower RMSE and greater R-squared, therefore it is better to use nonlinear model in Markov switching model for predicting the price of stocks.

Identifying and measuring the contagion channels at work in the European financial crises

We investigate the phenomenon of contagion with a special focus on the recent financial crisis, distinguishing four alternative channels, namely the flight-to-quality, flight-to-liquidity, risk premium, and correlated information channels. Specifically, we employ the differences among estimates and impulse response functions across linear and nonlinear models to identify and measure cross-asset contagion. An application to weekly Eurozone data for a 2007–2014 sample reveals that a two-state Markov switching model shows economically weak, though accurately estimated, contagion effects in a crisis regime. These findings are mainly explained by a flight-to-quality channel. Furthermore, we extend our analysis to explore whether European markets may or may not have been subject to contagion when exposed to external shocks, such as those originated from the US subprime crisis.

Markov-Switching Models with Evolving Regime-Specific Parameters: Are Postwar Booms or Recessions All Alike?

Abstract In this paper, we relax the assumption of constant regime-specific mean growth rates in Hamilton's (1989) two-state Markov-switching model of the business cycle. We introduce a random walk hierarchy prior for each regime-specific mean growth rate and impose a cointegrating relationship between the mean growth rates in recessionary and expansionary periods. By applying the proposed model to postwar U.S. real GDP growth (1947:Q4–2011:Q3), we uncover the evolving nature of the regime-specific mean growth rates of real output in the U.S. business cycle. Additional features of the postwar U.S. business cycle that we uncover include a steady decline in the long-run mean growth rate of real output over the postwar sample and an asymmetric error-correction mechanism when the economy deviates from its long-run equilibrium.

State-Dependent Variations in the Expected Illiquidity Premium

Recent theories of state-dependent variations in market liquidity suggest strong variations in expected illiquidity premium across economic states. Adopting a two-state Markov switching model, we find that while illiquid stocks are more strongly affected by economic conditions than liquid ones during recessions, the differences in expected returns are relatively weak during expansions. As a result, the expected illiquidity premium displays strong state-dependent variations, and its countercyclical pattern is consistent with theoretical argument based on timevarying liquidity risk premium. Overall, our results provide a strong relation between the expected illiquidity premium and the real business cycle.