Markov Switching Regression Model Research Articles

The Nonlinear Nexus of Climate Policy Uncertainty and Renewable Energy Consumption in the United States of America: A Markov-Switching Approach

We employ a Markov-Switching regression model using monthly US data from April 1987 to August 2022 to investigate the effect of climate policy uncertainty (CPU) on renewable energy consumption (REC). The main findings suggest the presence of a nonlinear relationship between CPU and REC. The baseline analysis using the CPU index reveals an adverse effect in regimes characterized by high levels of uncertainty. However, the effect is not statistically significant in a low uncertainty regime. To account for potential variations in the results, we perform a robustness analysis that considers the effect of CPU on REC, which may fluctuate based on the authorities' contextual perspectives (i.e., being in favor or against climate policy) and also the effect of CPU on REC by household. In addition, we incorporate a robustness check by utilizing the environmental policy uncertainty index developed by Noailly et al. (2022). The robustness test results confirm the results obtained from the baseline estimation.

Market Reactions to U.S. Financial Indices: A Comparison of the GFC versus the COVID-19 Pandemic Crisis

This study delves into the impacts of the 2008 global financial crisis (GFC) and the COVID-19 health crisis on U.S. financial indices, exploring the intricate relationship between economic shocks and these indices during downturns. Using Markov switching regression models and control variables, including GDP, consumer sentiment, industrial production, and the ratio of inventories-to-sale, it quantifies the effects of these crises on the CBOE Volatility Index (VIX), Standard & Poor’s 500 (S&P 500), and the Dow Jones Industrial Average (DJIA) from Q1 2000 to Q2 2023, covering crucial moments of both crises and stable periods (dichotomous variables). Results reveal that the 2008 crisis significantly heightened financial volatility and depreciated the valuation of S&P 500 and DJIA indicators, while the COVID-19 crisis had a diverse impact on market dynamics, particularly negatively affecting specific sectors. This study underscores the importance of consumer confidence and inventory management in mitigating financial volatility and emphasises the need for robust policy measures to address economic shocks, enhance financial stability, and alleviate future crises, especially during endogenous crises such as financial downturns. This research sheds light on the nuanced impact of crises on financial markets and the broader economy, revealing the intricate dynamics shaping market behaviour during turbulent times.

Foreign investor portfolio flow and monetary policy response in the Indonesian stock market considering the COVID-19 pandemic

Foreign portfolio investment in developing countries, including Indonesia, plays a crucial role in the economy, where this fund flow can influence exchange rates and stimulate price increases in the stock market. During the COVID-19 pandemic, the volatility of foreign portfolio flows by investors has significantly increased. To anticipate these conditions, the monetary authorities in Indonesia have implemented various monetary policies to address the possibility of more adverse situations. This study examines the impact of the inflow or outflow of foreign portfolio investments and the monetary policies reflected in the 7-day repo rate of Bank Indonesia on the Indonesian stock market. The data were collected from April 4, 2016, to March 18, 2022. The research methodology involves the Non-Linear Autoregressive Distributed Lag and the Markov Switching Regression (MSR) model. The findings indicate that foreign investor portfolio flows influence the Jakarta Composite Index. There is a tendency for domestic investors to analyze the habits of foreign investors. The study also found that monetary policy is not proven to affect the Jakarta Composite Index, while the USD/IDR exchange rate has an impact on the Indonesian stock market. This indicates many companies listed on the Indonesia Stock Exchange have debt in dollars or are paid in US dollars, making them vulnerable to exchange rate fluctuations.

Asymmetric and Nonlinear Foreign Debt–Inflation Nexus in Brazil: Evidence from NARDL and Markov Regime Switching Approaches

This paper augments the sparse literature on the inflationary impact of foreign debt in Brazil while addressing methodological caveats in previous studies. We depart from the linearity assumption and employ two nonlinear techniques: the nonlinear autoregressive distributed lag (NARDL) model and a Markov Switching Regression (MSR) to investigate the connection between foreign debt and inflation within a multivariate framework. The analyses consider the presence of structural breaks via assessing variable stationarity using the Zivot and Andrew unit root test and incorporating a residual-based cointegration test proposed by Gregory and Hansen. Additionally, we apply a multiple structural breakpoints test by Bai and Perron to determine the presence of structural breaks in the impact of foreign debt on inflation. Our findings robustly indicate that the domestic money supply has a statistically significant positive effect, while the nominal effective exchange rate has a negative effect on inflation in both the short and long run. The NARDL model reveals that only positive changes in foreign debt have a statistically significant negative effect on inflation in the short run, whereas both positive and negative foreign debt changes significantly affect inflation in the long run. The results from the MSR model are generally consistent with those of the NARDL model.

Conditional macroeconomic and stock market volatility under regime switching: Empirical evidence from Africa

<abstract> <p>We used the Markov switching regression model to establish a relationship between the conditional stock market returns and macroeconomic volatilities. Monthly data from thirteen (13) African stock markets and macroeconomic variables (exchange rate, inflation, interest rate, money supply, and crude oil price) from 2003 to 2022 were employed. We confirmed the existence of two distinct regimes: An economic expansion or a "tranquil" state with less volatility and an economic decline or a "crisis" state with high volatility. Our findings indicated that macroeconomic variables significantly affect both expansion and crisis periods. However, the estimated coefficients were more significant in a tranquil than in a crisis state. The findings of the study were consistent with macroeconomic theory and pointed out policy implications.</p> </abstract>

The effect of gas and oil prices on economic confidence: An empirical analysis for euro area

Energy prices are determinants of economic confidence by directly affecting production, consumption, and transportation costs. Nevertheless, countries’ energy policies and macroeconomic and geopolitical developments may moderate the impact of energy prices on economic confidence. This study analyzes the effects of oil and gas prices on business and confidence in the Euro Area over the period 1994–2023. We employ non-linear correlation and regression models to investigate the spillover relationship between the variables. For this purpose, the Markov Switching Regression (MSR) model is employed to analyze the spillover effect of oil and gas prices on both consumer and business confidence. The empirical results show that oil and gas prices harm consumer confidence in all regimes. Oil prices have a positive effect on business confidence in a volatility regime. However, oil prices negatively affect business confidence in low volatility, but this effect is insignificant. The effect of gas prices on the business confidence index is negative and statistically significant during periods of high volatility. Besides, gas prices positively affect business confidence for low volatility.

Effects of Interest Rate Policy on Inflation

This paper analyzes the episodes of high inflation and the efficacy of raising interest rates in reducing inflationary pressures in the U.S. economy. Estimating a Markov-switching regression model, the results show that high inflation frequently occurred from 1975 to 1983 with the most prolonged duration. In the 2000s, there were merely two inflationary months in 2008, but this is too short to call it a period. Then, after a period of low inflation in 2020, inflationary pressure started to build up. From March 2021 to the present, a period of high inflation has been prevailing. The estimation results suggest that the period from 1976 to 1983 is one episode of the high inflation period, and the period from 2016 to 2022 is the second period, which is about equal in length to both periods. A simple sub-period analysis shows that an increase in the federal funds rate lowers future inflation in both periods. A vector autoregression model is then estimated to identify the monetary policy channel via the short-term interest rate together with other variables such as inflation expectations and real economic activity. A one standard deviation increase in the federal funds rate can reduce annual inflation by up to 1.6 percent in the first period and 0.74 percent in the second. The effects on expected inflation are also comparable. The results suggest that interest rate policy has been effective in controlling inflation.

Gold and CoVid-19: Uncovering the safe haven hypothesis with dynamic MSR modeling

During the catastrophic Covid-19 era, numerous assets experienced a decline in their original values, leaving the financial community grappling with the implications of the pandemic. A significant concern arising from this context is whether gold emerged as the ultimate safe haven during the pandemic and whether there were any shifts in investor behavior between the two waves of the pandemic. To address these concerns, we employed relevant analytical approaches, utilizing a dynamic Markov-Switching Regression (MSR) model. Our findings indicate that during both waves of Covid-19, gold exhibited characteristics of a safe haven asset against bonds, providing a hedge against economic turmoil. However, when it comes to stocks, gold's role slightly changed. It acted as a diversifier, offering a different pattern of returns compared to equities. This highlights gold's ability to adapt to evolving market characteristics.These results emphasize the importance of conducting further research on this topic to gain deeper insights into the factors influencing gold's behavior during periods of crisis. By expanding our understanding, we can refine risk management strategies and enhance portfolio performance in turbulent market conditions.

Does Islamic banking reduce the risks of COVID-19 for SMEs? Novel evidence for SME financing in the pandemic period for an emerging market

This paper aims to examine the effect of COVID-19 on SME bank lending in the Islamic banking sector to small and medium-sized enterprises (SMEs) in an emerging market: Turkey. Understanding whether SME bank lending in the Islamic banking sector is procyclical or not is very important to reduce the impact of COVID-19 on SMEs. Interrupted time series analysis (ITSA), the Markov switching regression model, and vector autoregressive (VAR) methodologies are employed using novel weekly data for the pandemic period. The present study finds that the Islamic banking sector for SME financing has behaved countercyclically during COVID-19 in the Turkish economy. The paper thus sheds new light on the relationship between Islamic bank lending for SME financing and the COVID-19 shock in an important emerging market. The findings can provide insights into how Islamic banks mitigate the effect of COVID-19 on SMEs in an emerging market context. The present paper clearly shows differences between Islamic bank lending toward SMEs and deposit bank lending toward SMEs in the pandemic era. The willingness of Islamic banks to supply loans to SMEs during the pandemic plays a vital role in reducing SME firm failure in the Turkish economy.

Carbon emissions and sustainability in Covid-19's waves: evidence from a two-state dynamic Markov-switching regression (MSR) model.

Throughout the world, carbon emissions have decreased in an unprecedented way as a result of the Covid-19 pandemic. The purpose of this paper is to investigate whether a rebound effect in carbon emissions is anticipated following the extraction of information related to the beliefs of investors. A suitable Markov switching model is used in this paper to adapt the safe haven financial methodology to an environmental sustainability perspective. Analytically, the aforementioned situation is modeled by estimating a two-state dynamic Markov-Switching Regression (MSR), with a state-dependent intercept term to capture the dynamics of the series, across unobserved regimes. In light of the results of the research and the robustness checks, investors are anticipating a rebound effect on the total quantity of carbon emissions.

The Impact of ECB’s Unconventional Monetary Policy on the German Stock Market Volatility

Abstract This study investigated the reaction of German stock market volatility (Dax index) to the European Central Bank (ECB)’s unconventional monetary policy (UMP) announcements. The financial crisis of 2008 proved that the traditional monetary policy’s tool (the short -term interest rate) has lost its effectiveness to meet the new challenges. So, the key central banks, ECB included, had to implement the new, untested and nonstandard monetary policy which so called unconventional monetary policy. In this study, we used the ECB’s shadow policy rate approach to extract unconventional monetary policy. Also, We employed GJR GARCH (p,o,q) model to estimate the volatility in the German stock market. Then we calibrated both OLS (linear regression) and Markov-switching (probability-matrix of regime changes) models to examine the reaction of German stock returns volatility to UMP announcement by ECB for a period from January 2006 to December 2019. The results delivered by both models showed that the ECB’s UMP had a strong and negative effect on the volatility of the German stock market. Also, both models showed that the past German stock volatility has a significant and negative effect on the dependent variable, while the volatility of the German stock returns is a function of the global volatility estimated by the VIX index. Moreover, the results showed that the Markov-switching regression model provides a better illustration of the stock market volatility impact of UMP than the OLS model because it can represent the changes into the two different regimes named ordinary regime and quantitative easing (crisis) regime. Furthermore, under the Markov-switching regression model, we can see how the output gap and the inflation gap influence the volatility of the Dax index, while the results of the OLS regression model showed that there is no significant relationship between the output gap and the inflation gap with the German stock market volatility.

The Impact of Renewable Energy on Carbon Neutrality for the Sustainable Environment: Role of Green Finance and Technology Innovations

Over time environmental degradation has become a severe concern globally, especially in China. Therefore, to solve this serious issue, environmental economists have tried their best to explain the crux of environmental degradation. Besides such efforts, they have not reached a single opinion. Nowadays, economic progress has been considered a primary target to compete with other nations at any cost of environmental degradation. But, there may be some alternative solutions to reduce such costs, and the existing literature has not considered such core indicators. Thus, the present study assesses the eco-friendly variables for a sustainable environment in which green finance, green energy, and research and development expenditures. Moreover, this study also focuses on socio-economic factors like economic growth, urbanization, and openness. Time series estimators such as fully modified ordinary least square and Markov switching regression model are employed to investigate selected variables’ long-run impact on carbon emissions. The estimated outcomes show the significant contribution of green finance, green energy, openness, and R&D expenditures to environmental quality. Likewise, China’s urbanization and economic progress are harmful to environmental quality. Moreover, this study investigates the causal association between the selected variables and shows the two-way causal association between openness and emissions, RE and openness, and green finance and R&D expenditures. The results of the uni-directional association are also interesting. The current study develops some interesting policy implications for a sustainable environment on behalf of empirical outcomes.

Co-movement and causal relationships between conventional and Islamic stock market returns under regime-switching framework

PurposeThis 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.Design/methodology/approachFirst, 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.FindingsResults 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.Originality/valueThis 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.

Investor's herding behavior in Asian equity markets during COVID-19 period

In this paper we investigate herding behavior triggered by the COVID-19 outbreak in 2020 by considering six typical Asian stock markets. Cross-sectional Standard Deviation (CSSD) and Cross-sectional Absolute Deviation (CSAD) have been employed as the key indicators, which are aligned with the Markov-switching regression and HS model to identify the presence and magnitude of herding. We then elaborate our study by examining herding in specific time slots and markets with different idiosyncratic volatility. Our empirical results show a clear presence of herding in the “Feb 2020-Jan 2021” time window and we have captured a sharp rise of the magnitude of herding during the market crash in March 2020, and found herding emerged in these markets with shocks and fierce fluctuations.

Renewable Energy and Energy Innovations: Examining Relationships Using Markov Switching Regression Model

Accelerating the development of new energy infrastructure in the EU based on renewable energy sources is necessary for the targeted reduction of greenhouse gas emissions and increase in energy production from renewable sources. This article reviews current renewable energy development issues and research on energy innovations within the European energy policy. The effectiveness of energy generation from renewable resources and adaptation of energy innovations may be limited to the challenges of ensuring the flexibility of the pan-European energy system, as the development of energy storage systems and technologies to respond to demand is much slower than the development of renewable energy. Therefore, the study's primary purpose was to explain by mathematical modelling the determinism of variation in electricity production in the EU27 due to predictors of net electricity generation from certain types of renewable resources for 2017-2020. To identify the effects of the deployment of renewable energy in the EU27, a regression model of Markov switching for three regimes was chosen, consisting of selected predictors of clean energy generation from renewable sources (hydro, geothermal, wind, and solar). The statsmodels v0.13.2 toolkit in Python 3.10.5 was used to conduct this study. The variation between total electricity production and net electricity generation in the EU27 is not constant and depends on the mode of electricity production. That is, there is an asymmetry in the relationship between these parameters. The results also show that when the electricity generation rate in the EU27 is moderate, the net wind energy generation rate is not significant. Furthermore, the negative link between clean solar energy generation and electricity production in the EU is significant for all three regimes.

Asymmetries in the monetary policy reaction function: evidence from India

Abstract This paper analyzes the reaction function of monetary authority in India from 1997Q 1 to 2019Q 4 using nonlinear Taylor rule. It has been found that monetary policy reaction function (MPRF) in India is asymmetric and is influenced by the state of the economy, determined by the lagged interest rate. To capture such asymmetry, we have used a set of nonlinear models including smooth transition regression (STR) model, threshold regression (TR) model and Markov-switching regression (MSR) model along with the instrumental variable estimation technique. The analysis discloses that the behaviour of the Reserve Bank of India (RBI) is asymmetric, reacts aggressively to output gap in general and particularly during periods of high interest rate. Furthermore, the RBI reacts more to inflation and output gap during low volatile regimes in MSR models compared to high volatile regimes. We also found that there is a high degree of inertia in the policy rates of the RBI. The study concludes that nonlinear models may not only help in understanding the behaviour of the RBI but also prevent from making incorrect and misleading conclusions in Indian context.

The impacts of foreign portfolio flows and monetary policy responses on stock markets by considering COVID-19 pandemic: Evidence from Turkey

This study researches the impacts of foreign portfolio flows (proxied by foreign investors' retention share) and monetary policy responses (proxied by the repurchase interest rate) on Turkey's stock market index taking the COVID-19 pandemic into consideration. A volatility index, credit default swap spreads, and foreign exchange rates are used as control variables, with a daily dataset between January 2, 2017, and October 20, 2020. After examining the stationarity and nonlinearity characteristics of the variables, we applied a nonlinear autoregressive distributed lag (NARDL) model and then conducted a Markov switching regression (MSR) for a robustness check. The results reveal that both foreign portfolio flows and monetary responses have an important effect on the index, and foreign portfolio flows have a higher effect than monetary responses. Accordingly, the results obtained from the NARDL and MSR models are robust and consistent.

Gradient boosting in Markov-switching generalized additive models for location, scale, and shape

Markov-switching generalized additive models for location, scale, and shape constitute a novel class of flexible latent-state time series regression models. In contrast to conventional Markov-switching regression models, they can be used to model different state-dependent parameters of the response distribution — not only the mean, but also variance, skewness, and kurtosis parameters — as potentially smooth functions of a given set of explanatory variables. In addition, the set of possible distributions that can be specified for the response is not limited to the exponential family but additionally includes, for instance, a variety of Box-Cox-transformed, zero-inflated, and mixture distributions. An estimation approach based on the EM algorithm is proposed, where the gradient boosting framework is exploited to prevent overfitting while simultaneously performing variable selection. The feasibility of the suggested approach is assessed in simulation experiments and illustrated in a real-data application, where the conditional distribution of the daily average price of energy in Spain is modeled over time.

Analyzing the Influence of Transportations on Chinese Inbound Tourism: Markov Switching Penalized Regression Approaches

This study investigates the nonlinear impact of various modes of transportation (air, road, railway, and maritime) on the number of foreign visitors to China originating from major source countries. Our nonlinear tourism demand equations are determined through the Markov-switching regression (MSR) model, thereby, capturing the possible structural changes in Chinese tourism demand. Due to many variables and the limitations from the small number of observations confronted in this empirical study, we may face multicollinearity and endogeneity bias. Therefore, we introduce the two penalized maximum likelihoods, namely Ridge and Lasso, to estimate the high dimensional parameters in the MSR model. This investigation found the structural changes in all tourist arrival series with significant coefficient shifts in transportation variables. We observe that the coefficients are relatively more significant in regime 1 (low tourist arrival regime). The coefficients in regime 1 are all positive (except railway length in operation), while the estimated coefficients in regime 2 are positive in fewer numbers and weak. This study shows that, in the process of transportation, development and changing inbound tourism demand from ten countries, some variables with the originally strong positive effect will have a weak positive effect when tourist arrivals are classified in the high tourist arrival regime.

A Markov-switching dynamic regression analysis of the asymmetries related to the determinants of US crude oil production between 1982 and 2019

The structural changes brought about by shale oil revolution have inspired this paper of which the aim is to analyze the potential asymmetries related to the determinants of crude oil production in the USA. Thus, using a Markov-switching dynamic regression model in which parameters change when oil production moves from one regime to the other, it is found that for both oil production and oil relative importance, the regime that was dominant during the 1980s and the early 1990s when oil production in the USA was substantially high is the same regime that has once again become dominant in the decade corresponding to the shale oil revolution. Furthermore, the study reveals the existence of asymmetries in the relationship between US crude oil production and both manufacturing production and the consumer price index. Asymmetries are also found in the relationship between the relative importance US crude oil and manufacturing production. Finally, it is found that the intercept and the variance parameter also vary from one regime to the other, thus justifying the use of regime-dependent models.