Markov Regime Switching Research Articles

The Effects of Taxes on Income Distribution Justice

One of the most important economic problems that countries have faced from the past to the present is income inequality. Income distribution is an important indicator that allows us to understand the social and political structures of societies by revealing how the income gap between the rich and the poor in a country changes. Thus, income redistribution or income justice is one of the important goals of fiscal policy. Taxes are also important intervention tools that economic management has in achieving this goal. In this context, this study aims to examine the relationships between different types of taxes and income distribution in the Turkish economy during the period from 2002 to 2022 by using markov regime switching analysis method. The analysis results indicate that, during the period covered by the study, indirect taxes have a positive effect on income distribution, while taxes based on declaration have a detrimental effect on income distribution.

A dynamic model of repositioning with a Markov-switching regime

AbstractRepositioning products and services to entice customers is a key management strategy that requires careful planning, timing, and budget allocation. This study is the first to investigate repositioning strategies by considering a Hotelling-type location model where consumer preferences change over time with short- and long-term uncertainty. This requires the firm to adjust its product positioning to continue to appeal to consumers. A key assumption is that the parameters describing the dynamics of consumer preferences are modulated by a Markov-switching regime representing long-term uncertainty. Further, this study investigates the effects of regime shift intensity on repositioning strategies. We numerically solve a simultaneous ordinary differential equation system to derive a firm’s optimal strategy, represented by repositioning thresholds that depend on the regimes. We find that in the two-regime case, one threshold is monotonic with respect to the regime shift intensity, whereas the other can be non-monotonic. This suggests that a firm should simultaneously consider the value of the current regime and option value of waiting for a regime shift, thus effectively demonstrating the significance of the uncertainty associated with regime-switching.

Beyond the glitter: An empirical assessment of the true risk and hedging role of precious metals

Precious metals are an essential component of asset class within the global financial market offering diverse benefits. Being significant store of value, they are immune to default risk, provide hedge against inflation and are deemed to be safe haven. This highlights the importance of precious metals in the broader context of financial and economic stability. This paper focuses on the S&P Precious Metal Index (PMI) to explore its resilience towards financial market risks and examines if it could offer effective hedge during crises. Using Markov Regime Switching model, we find significant volatility persistence in PMI returns undermining its suitability for dynamic hedging. However, absence of speculative trends makes the PMI a safe asset. Further, financial market stress, as revealed by estimated Component GARCH and Discrete Threshold Models, does not affect the unique and market risk of investing in PMI. Although we could not establish PMI as a strong safe haven or a zero-beta asset, its potential to be included in a risk-avert investor's portfolio cannot be negated. Its consistently low and stress-invariant time-varying beta safeguards wealth against losses. The role of PMI becomes particularly salient while considering portfolio optimization during crises. In recent years, particularly during and after the pandemic the hedging effectiveness of PMI has increased. There is, however, a point of concern. The market risk of PMI is explained mostly by its lagged values rather than exogenous factors like market stress. Given the endogenous nature of market risks, chaotic dynamics might be in operation making forecasting and regulating PMI prices difficult.

ANALYSIS OF EXPORT-LED GROWTH MODEL FOR TÜRKİYE ECONOMY WITH MARKOV REGIME SWITCHING MODEL

Research on the export-led growth model is significant due to the contribution of exports to the development of domestic sectors and its positive impact on national values, especially employment and growth. Therefore, countries prefer the export-led growth model by increasing their export volumes with an outward-oriented policy. When Turkiye's growth figures are analyzed, it is feasible to say that there is a strong connection between exports and economic growth. In this study, within the framework of export-led growth hypotheses, the additive of exports to Turkiye's growth dynamics between 1980 and 2022 is investigated. The aim of the study is to econometrically test the intercourse between exports and GDP in the Turkiye economy. In the analysis, first linear unit root analyses were implemented. Then, the dynamic correlation relationship between exports and GDP was analyzed with DCC-GARCH, and the dynamic regression relationship was performed with the Markov regime switching model. As a consequence of the examines, it was seen that the dynamic correlation indicators and Markov model results were consistent with each other. While it was found that the correlation decreased during the high volatility period, a decrease in the number of regressions was observed according to the Markov model results.

Heterogeneous impact of economic and political uncertainty on green bond volatility: Evidence from the MRS-GARCH-MIDAS-Skewed T model

Green bonds attract increasing attention as a new eco-friendly investment product. We explore the heterogeneous impact of low-frequency economic and political uncertainty across high or low uncertainty states on green bond volatility in order to accurately analyze the green bond market risk. To this end, we propose a new Markov regime switching GARCH-MIDAS-Skewed T model, in which the regime switching behavior occurs on the low-frequency long-term volatility. An effective filtering estimation method is put forth by introducing the likelihood of the low-frequency sub-sample set. The evidence supports that there are significant time-varying and state-dependent impacts from uncertainty shocks on the volatility of green bonds, including monetary policy, inflation, and crude oil prices as well as global economic policy and political environment. In addition, we find the counter-cyclical behavior of green bond volatility, which increases in the period of economic recession or financial turbulence with expanding uncertainty. Improving the hedging ability of green bonds against uncertainty risks effectively contributes to low-carbon economic development.

Investor sentiments and performance of selected ESG indices in BRICS markets during bull and bear conditions

ABSTRACT Sustainability concerns are increasingly shaping global investment decisions, prioritising sustainable impacts over mere profit-making. This study examines the performance of Environmental, Social, and Governance (ESG) funds in emerging markets, focusing on bull and bear market scenarios. A Markov Regime Switching model was employed to analyse daily price data from BRICS (Brazil, Russia, India, China, and South Africa) ESG market indices spanning from 31 January 2011 to 2 January 2023. The analysis highlights the variability of ESG returns performance across different market conditions, emphasising the need for tailored investment strategies that account for market dynamics. Additionally, patterns in ESG returns and volatility are identified, along with pairwise relationships among ESG markets within the emerging markets bloc. The study also examines the influence of investor sentiments on ESG performance and controlled for the influence of macroeconomic factors on ESG performance amid changing market conditions. Finally, insights into transition probabilities between bullish and bearish market conditions offer guidance for more resilient investment portfolio management. This research contributes to advancing empirical knowledge on ESG investing in emerging markets, emphasising the significance of investor sentiments and market conditions for sustainable investment practices and long-term financial success.

Does Regime-Dependent Volatility Drive Dynamism in Investor Herding?

The existing literature on herding often uses the static model to test herd behaviour in the Indian market context. Hence, the objective of this paper is to investigate the dynamic herd behaviour for S&P BSE 500 from 2009-2023 using the Markov Regime Switching model. Results exhibit the occurrence of three regimes, namely, high, low, and extremely volatile regimes. Findings suggest that the Indian market moves into the order of low, high, and extreme volatility (LHC), similar to other developed countries. This has implications for investors to either exit from the market or reframe their portfolio through hedging techniques before the market enters into extreme volatility. Moreover, the results exhibit anti-herding in high and low-volatile regimes. Our study discloses the presence of herding in crashes or extremely volatile regimes, showing that Indian investors start following each other during crash-like situations. This research is significant for individual investors, portfolio managers, and stock market regulators.

Optimal trading with regime switching: Numerical and analytic techniques applied to valuing storage in an electricity balancing market

Accurately valuing storage in the electricity market recognizes its role in enhancing grid flexibility, integrating renewable energy, managing peak loads, providing ancillary services and improving market efficiency. In this paper we outline an optimal trading problem for an Energy Storage Device trading on the electricity balancing (or regulating) market. To capture the features of the balancing (or regulating) market price we combine stochastic differential equations with Markov regime switching to create a novel model, and outline how this can be calibrated to real market data available from NordPool. By modelling a battery that can be filled or emptied instantaneously, this simplifying assumption allows us to generate numerical and quasi analytic solutions.We implement a case study to investigate the behaviour of the optimal strategy, how it is affected by price and underlying model parameters. Using numerical (finite-difference) techniques to solve the dynamic programming problem, we can estimate the value of operating an Energy Storage Device in the market given fixed costs to charge or discharge. Finally we use properties of the numerical solution to propose a simple quasi-analytic approximation to the problem. We find that analytic techniques can be used to give a benchmark value for the storage price when price variations during the day are relatively small.

Stress testing for IInd pillar life-cycle pension funds using hidden Markov model

AbstractThis paper presents a stress testing technique based on a hidden Markov regime switching model and scenario generations. Firstly, we assume that investor’s risk preferences and beliefs are primarily observed in main world indices and then transmitted to other markets such as IInd pillar life-cycle pension funds. This enables both regime (market state) identification and regime-switching detection. Second, we apply stress to the transition matrix by increasing probabilities of moving to a worse market state, i.e., we increase the transition probabilities on the upper-right side of the main diagonal and decrease the diagonal probabilities correspondingly. Third, future scenarios (evolution) of returns of pension funds are generated using stressed transition matrices and basic risk and performance measures of the final wealth are presented. Finally, three different strategies for pension managers are considered and compared to each other. The results show how the best strategy depends on the stress level.

A MS SHARV-MIDAS model: a new regime-switching model for volatility forecasting

ABSTRACT The recent SHARV and SHARV-MIDAS models incorporate current returns information for volatility forecasting. However, these models fail to capture the intricate transition process of volatility states due to structural breaks caused by extreme events in financial markets. Therefore, we introduce a Markov regime-switching into the SHARV-MIDAS model and develop the MS SHARV-MIDAS model that comprehensively accounts for structural breaks, long memory, and current return information in volatility forecasting. Furthermore, we examine the effectiveness of this model through empirical studies and robustness tests, which demonstrate its superiority over benchmark models regarding in-sample fitting and out-of-sample volatility forecasting.

Cement production and CO2 emission cycles in the USA: evidence from MS-ARDL and MS-VARDL causality methods with century-long data

The cement industry is among the top three polluters among all industries and the examination of the nonlinear and cointegration dynamics between cement production and CO2 emissions has not been explored. Focusing on this research gap, the study employs a novel Markov-switching autoregressive distributed lag (MS-ARDL) model and its generalization to vector error correction, the MS-VARDL model, for regime-dependent causality testing. The new method allows the determination of nonlinear long-run and short-run relations, regime duration, and cement-induced-CO2 emission cycles in the USA for a historically long dataset covering 1900–2021. Empirical findings point to nonlinearity in all series and nonlinear cointegration between cement production and cement-induced CO2 emissions. The phases of regimes coincide closely with NBER’s official economic cycles for the USA. The second regime, characterized by expansions, lasts twice as long relative to the first, the contractionary regime, which contains severe economic recessions, as well as economic crises, the 1929 Great Depression, the 1973 Oil Crisis, the 2009 Great Recession, and the COVID-19 Shutdown and Wars, including WWI and II. In both regimes, the adverse effects of cement production on CO2 emissions cannot be rejected with varying degrees both in the long and the short run. Markov regime-switching vector autoregressive distributed lag (MS-VARDL) causality tests confirm unidirectional causality from cement production to CO2 emissions in both regimes. The traditional Granger causality test produces an over-acceptance of causality in a discussed set of cases. Industry-level policy recommendations include investments to help with the shift to green kiln technologies and energy efficiency. National-level policies on renewable energy and carbon capture are also vital considering the energy consumption of cement production.Graphical

Exploring Inflation Dynamics with the Phillips Curve in Türkiye: Evidence from the Markov Regime Switching Model

Unlike in many advanced and emerging market economies, inflation was already a problem for the Turkish economy before the COVID-19 pandemic. During the 1990s, Türkiye experienced severe inflation, which reached three digits in some months. Although inflation was reduced to single digits in the 2000s through a successful disinflation program under an implicit inflation targeting framework with a flexible exchange rate regime, price stability became a problem again after the global financial crisis. Inflation gradually increased during the 2010s and became out of control after late 2021. This study aims to explore inflation dynamics in Türkiye using the Phillips curve framework for different inflation environments. To this end, the Phillips curve equation, augmented with the exchange rate and oil prices, is estimated using the Markov regime switching model between January 2006 and September 2023. Two regimes are identified in the sample, namely low- and high-inflation regimes. According to the estimation results, the Phillips curve is invalid in both regimes. The unemployment gap is statistically insignificant despite its negative impact on current inflation. The increasing coefficient of backward inflation in the high regime reflects the resurgent indexation behavior, which was dominant before inflation targeting was introduced in Türkiye. The positive impact of oil prices is only statistically significant in the low regime. While an increase in the exchange rate significantly raises inflation in both regimes, the effect is greater in the high-inflation regime. These empirical findings indicate that the primary issues for controlling inflation in Türkiye are inflation inertia and exchange rate stability.

Assessment on fast simulation of wind-driven pollutant dispersion around a street canyon with regime-switching Markov chain

High-density cities shorten commutes but also create proximity to air pollution sources. Sensors coupled with simulations can achieve real-time monitoring and risk assessment, and the recently emerging data-driven models provide opportunities that allow minimizing the computation burden of physical models. This study investigates the use of a time inhomogeneous Markov chain (CLF-MC) to fast simulation of wind-driven pollutant dispersion around a street canyon. The CLF-MC model is trained and tested using synthetic datasets obtained from large eddy simulations (LES). The LES model is validated using a benchmark wind tunnel test, which yields correlation coefficients above 0.95. The results show that the CLF-MC can faithfully reproduce the cumulative distribution of air pollution concentrations at the receptor site using the optimal model configuration identified in this study. For the hold-out test dataset, the release condition is a time-varying release strength, and the CLF-MC shows improved prediction results compared to the baseline models, i.e. the weighted average deviation is 15 % and 51 % smaller, respectively. In comparison to LES, the CLF-MC model is 4.8 × 106 times faster. These findings provide scientific references for fast and accurate wind-driven pollutant dispersion simulation using the probabilistic modelling approach.

Forecasting the Chinese crude oil futures volatility using jump intensity and Markov-regime switching model

This study examines the predictive ability of nine high-frequency jumps on the Chinese crude oil futures volatility using a series of the Heterogeneous Autoregressive (HAR) models. Out-of-sample empirical results indicate that among the nine high-frequency jump tests, the JO jump component is powerful because the prediction model including this component demonstrates superior predictive performance. Compared to other competing models, the model incorporating JO jump component, jump intensity, and Markov-regime achieves higher predictive accuracy. During the outbreak of the COVID-19 pandemic and periods of high volatility, this new model continues to exhibit strong predictive capability for volatility in the Chinese oil futures market. This study provides novel insights into forecasting volatility in the Chinese oil market under the presence of extreme shocks.

Bayesian Markov switching model for BRICS currencies' exchange rates

AbstractExchange rate modeling has always fascinated researchers because of its complex macroeconomic dynamics. This study documents the exchange rate dynamics of major emerging economies after accounting for their macroeconomic cycles and explores the Bayesian Vector Error Correction Model (VECM) Markov Regime switching model, which uses time‐varying transition probabilities. The main objective is to study the exchange rate dynamics of Brazil, Russia, India, China, and South Africa (BRICS) vis‐à‐vis the US dollar. The Bayesian setup uses two hierarchal shrinkage priors, the normal‐gamma (NG) prior and the Litterman prior, for parameters' estimation. These shrinkage priors allow for a more comprehensive assessment of the regime‐specific coefficients. The model performed well in differentiating between the two regimes for all currencies. The Russian ruble was identified to be the most depreciated currency, whereas the African Rand was the most appreciated. The evaluation of model features revealed that many regime‐specific coefficients differed significantly from their common mean. A forecasting exercise was then performed for the out‐of‐sample period to assess the model's performance. A significant improvement was observed over the basic random walk (RW) model and the linear Bayesian vector autoregression (BVAR) model.

Gerber-Shiu theory for discrete risk processes in a regime switching environment

In this paper we develop the Gerber-Shiu theory for the classic and dual discrete risk processes in a Markovian (regime switching) environment. In particular, by expressing the Gerber-Shiu function in terms of potential measures of an upward (downward) skip-free discrete-time and discrete-space Markov Additive Process (MAP), we derive closed form expressions for the Gerber-Shiu function in terms of the so-called (discrete) Wv and Zv scale matrices, which were introduced in [27]. We show that the discrete scale matrices allow for a unified approach for identifying the Gerber-Shiu function as well as the value function of the associated constant dividend barrier problems.

Extreme risk spillovers between US and Chinese agricultural futures markets in crises: A dependence-switching copula-CoVaR model.

The linkages between the US and China, the world's two major agricultural powers, have brought great uncertainty to the global food markets. Inspired by these, this paper examines the extreme risk spillovers between US and Chinese agricultural futures markets during significant crises. We use a copula-conditional value at risk (CoVaR) model with Markov-switching regimes to capture the tail dependence in their pair markets. The study covers the period from January 2006 to December 2022 and identifies two distinct dependence regimes (stable and crisis periods). Moreover, we find significant and asymmetric upside/downside extreme risk spillovers between the US and Chinese markets, which are highly volatile in crises. Additionally, the impact of international capital flows (the financial channel) on risk spillovers is particularly pronounced during the global financial crisis. During the period of the COVID-19 pandemic and the Russia-Ukraine 2022 war, the impact of supply chain disruptions (the non-financial channel) is highlighted. Our findings provide a theoretical reference for monitoring the co-movements in agricultural futures markets and practical insights for managing investment portfolios and enhancing food market stability during crises.

Beyond the numbers: unveiling the environmental impacts of international tourism and the role of renewable energy transition

ABSTRACT In pursuit of Saudi Arabia's Vision 2030 objectives for a sustainable and low-carbon future, this study examines the intricate dynamics between carbon emissions and key variables crucial to the nation's decarbonisation strategy. Grounded in the Triple Bottom Line (TBL) Theory, the chosen variables – sustainable tourism, renewable energy demand, and natural resource management – take centre stage. Applying Markov switching regime regression to data spanning 1990–2022, our findings reveal a positive association between international tourism and carbon emissions, aligning with the Tourism-induced Environmental Impact theory. Incorporating the Environmental Kuznets Curve (EKC), our results illustrate a U-shaped relationship. Renewable energy consumption (REC) exhibits a negative impact, supporting EKC's premise that economic development and cleaner energy adoption mitigate environmental degradation. Identified as influential factors affecting carbon emissions, Economic growth and natural resource capital find theoretical backing. The second power of economic expansion positively influences carbon emissions, while the square of natural resources has a negative impact, aligning with resource scarcity theories. Granger causality estimates validate Tourism-led Growth, Resource Capital, and Emissions-led Growth hypotheses. Integrating insights from the Community-Based Tourism (CBT) and stakeholder theory, this study provides actionable insights for policymakers steering Saudi Arabia toward a sustainable, low-carbon future.

Predicting systemic financial risk with interpretable machine learning

Predicting systemic financial risk is essential for understanding the financial system's stability and early warning of financial crises. In this research, we use the financial stress index to measure systemic financial risk. We construct the stress index for five financial submarkets and composite stress index, employ the Markov regime switching model to identify the systemic financial risk stress state. On this basis, we use interpretable machine learning models to forecast systemic financial risk, analyze and compare the results of the intrinsic interpretable machine learning models and the post-hoc explainable methods. The results indicate that systemic financial risk can be effectively predicted using both the submarket stress index and the feature variables, with the submarket stress index as the independent variable providing relatively higher accuracy. There is a linearly positive relationship between the stress index of each submarket and systemic financial risk, with financial stress in the stock and money markets having the greatest impact on systemic financial risk. For each feature variable, stock–bond correlation coefficient, stock valuation risk, the maximum cumulative loss of the SSE Composite Index (SSE CMAX), and loan-deposit ratio have strong predictive power. Our research can provide reference for government to construct prediction model and indicator monitoring platform of systemic financial crisis.

Conditional McKean-Vlasov SDEs with jumps and Markovian regime-switching: Wellposedness, propagation of chaos, averaging principle

The conditional McKean-Vlasov SDEs with jumps and Markovian regime-switching are investigated in this work. We use the L2-Wasserstein distance to measure the regularity of the coefficients in the probability measure argument. Also, we establish the propagation of chaos for the associated mean-field interaction particle system with common noise and provide an explicit bound on the convergence rate. Furthermore, an averaging principle is established for two time-scale conditional McKean-Vlasov equations, where much attention is paid to the convergence of the conditional distribution term.