Regime Switching Behavior Research Articles

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.

Fossil fuel prices and economic policy uncertainty– A regime-switching approach

The study investigates the impact of fossil fuel prices on the regime-switching dynamics of economic policy uncertainty for the global economy. The period of investigation spans 25 years; comprising monthly data for the period of 1998:01 to 2023:03 and, due to its propensity to accommodate shocks, swings and shifts in the data, the technique of analysis employed is the Markov Switching Dynamic Regression. The principal component analysis (PCA) method was used in obtaining a composite index for the fossil fuel prices. The results obtained show evidence of regime-switching behaviour with five (5) times persistence of low to high global economic policy uncertainty. In addition, the study finds significant counter-cyclical and pro-cyclical effects of fossil fuel prices on global economic policy uncertainty; especially under the regime of high uncertainty. These results are consistent with the results for the composite index of fossil fuel prices but with alternate persistence effects. These suggest that policymakers should be concerned in stabilizing fluctuating fossil fuel prices in order to contain its spiralling and uncertain effects on the global economic policy. More so, governments should devise series of low carbon-emission means for home and industrial uses to ultimately reduce the excessive demand for fossil fuel so as to crash its prices in the international market.

Oil price shocks and macroeconomic dynamics in resource-rich emerging economies under regime shifts

How do global oil price fluctuations affect macroeconomic activity and monetary policy in resource-rich emerging economies? This paper tackles this question by developing a unified emerging market/rest-of-the-world DSGE framework featuring Ricardian and Non-Ricardian households, an oil-producing sector, a fuel subsidy programme, and a regime-switching process. The model is estimated using Bayesian methods, allowing us to be agnostic regarding regime shifts in the monetary policy rule and oil price volatility. Using data for Nigeria, we find substantial empirical support for the regime-switching behaviour and discuss how macroeconomic implications of oil price shocks may vary depending on the nature of the shock, monetary policy responses, and the fuel subsidy policy in place. Apart from providing important insights into the monetary policy transmission mechanism, the paper offers a novel, flexible, and functional model for future policy analysis, especially in resource-rich emerging countries.

Regional regime-switching behavior of China’s carbon price during regional and nationwide emissions trading scheme pilots

Regional regime-switching behavior of China’s carbon price during regional and nationwide emissions trading scheme pilots

Multi-regime foreign exchange rate model: Calibration and pricing

To price exotic foreign exchange (FX) options, a model needs to be selected for FX spot rate dynamics. The classic approach of modelling spot rates with Black–Scholes framework makes inappropriate assumptions of constant drift and volatility, resulting in mispricing. In this article, we investigate multi-regime Black–Scholes (MRBS) model for FX rate, with regime-switching behaviour of drift and volatility governed by a Markov chain. We derive an analytic formula for European FX call options via Fourier transform and present a Monte Carlo simulation algorithm for FX barrier option pricing. Further, we propose a calibration strategy with penalty. Finally, the empirical study shows that volatility smile can be recovered by MRBS model; calibrating MRBS with penalty gives stable calibrated parameters and small out-of-sample mean squared errors; combined use of our calibration methods and pricing algorithms provides reasonable prices for FX barrier options. Thus, MRBS model, together with proposed calibration-with-penalty strategy and pricing algorithm, provides a promising approach for FX option pricing.

Analysing the response of CO2 emissions to business cycle in a developing economy: evidence for South Africa post-apartheid era

Introduction: This research addresses the response of CO2 emissions to economic fluctuations in South Africa post-Apartheid, covering the period 1990–2018. While previous studies focused on developed countries, limited attention has been given to sub-Saharan developing nations. The study challenges the assumption of constant emissions elasticity in current forecasts.Methods: The study employs a two-step strategy. Firstly, the rolling window regression with Hodrick-Prescott filtering was used to investigate whether the CO2 emissions elasticity varies over time. Secondly, a Markov-switching approach was used to examine the regime-switching behavior in GDP.Results and Discussion: Results suggest that CO2 emisssions elasticity varies over time. This was confirmed through alternative filtering techniques (Christiano-Fitzgerald, Baxter King, and the Butterworth filter). Markov-switching analysis revealed a regime-switching behavior in GDP, indicating negative CO2 emissions elasticity during recessions and positive elasticity during expansions. These findings persist even when accounting for monetary policy shocks and productivity shocks in the Environmental Dynamic Stochastic General Equilibrium (E-DSGE) model. Noteworthy is that South Africa is among the top 20 greenhouse gas emitters globally.Conclusion and recommendations: The study recommends tailored carbon-pricing policies that are conscious to the countercyclical nature of business cycles. Pricing emissions higher during economic upswings aligns with periods of growth. Additionally, the government is advised to invest in research and development for energy conservation, efficiency, and renewable technologies to counterbalance emissions growth. Implementing emission caps and tax incentives can further enforce pollution abatement measures. Policymakers should consider these asymmetrical responses when addressing global warming challenges in South Africa.

The occurrence mechanisms of extreme events in a class of nonlinear Duffing-type systems under random excitations.

The occurrence mechanisms of extreme events under random disturbances are relatively complex and not yet clear. In this paper, we take a class of generalized Duffing-type systems as an example to reveal three mechanisms for the occurrence of extreme events. First, it is intuitive that a very large excitation can generate extreme events, such as the Lévy noise. In such a case, extreme excitation works, while it does not require much about the systems. Second, when a system has a bifurcation structure, if the difference of the branches at the bifurcation point is large, a randomly varying bifurcation parameter can lead to extreme events. Finally, when a system has rare attractors, a random impulse excitation, such as Poisson white noise, is able to cause the system to escape from one general attractor into rare attractors. Such a kind of special regime switching behavior can lead to extreme events. These results reveal the possible mechanisms of extreme events in a class of nonlinear Duffing-type systems and provide guidance for further prediction and avoidance of extreme events.

Understanding the effects of COVID-19 on Regime Switching Behaviour of Asian Stock Markets

Understanding the effects of COVID-19 on Regime Switching Behaviour of Asian Stock Markets

Stock Market Performance of the US Hospitality And Tourism During the Covid-19 Pandemic

In this study, the stock market performance of the travel and leisure industry during the COVID-19 pandemic is investigated by use of the three-regime Markov switching model. The analysis employs daily data for six subsectors (airlines, gambling, hotels, leisure services, restaurants and bars, as well as travel and tourism) for the US from January 2018 to November 2021. Estimation results provide strong evidence of regime switching behavior with wide differences across subsectors during the course of the COVID-19 pandemic. A longer duration of high volatility characterizes the airline and leisure services indices. These sectors exhibit the most pronounced downturn that was not fully recovered in November 2021. In contrast, the period of high volatility in the restaurant, gaming, and hotel industries is relatively short, and stock market performance recovers almost to the general trend. Of all subsectors, restaurants and bars experience the shortest duration of high volatility, limited to the second quarter of 2020. The stock market indices for the travel and tourism industry (mainly car rentals) are also highly volatile, but this pattern was observed already before the pandemic.

Predicting interest rate distributions using PCA & quantile regression

Principal component analysis (PCA) is well established as a powerful statistical technique in the realm of yield curve modeling. PCA based term structure models typically provide accurate fit to observed yields and explain most of the cross-sectional variation of yields. Although principal components are building blocks of modern term structure models, the approach has been less explored for the purpose of risk modelling—such as Value-at-Risk and Expected Shortfall. Interest rate risk models are generally challenging to specify and estimate, due to the regime switching behavior of yields and yield volatilities. In this paper, we contribute to the literature by combining estimates of conditional principal component volatilities in a quantile regression (QREG) framework to infer distributional yield estimates. The proposed PCA-QREG model offers predictions that are of high accuracy for most maturities while retaining simplicity in application and interpretability.

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.

Nonlinearities between bank stability and income diversification: A dynamic network data envelopment analysis approach

This study contributes to the ongoing debate about the cost-benefit tradeoff of income diversification in banking. Its main objective is to shed light on the non-linear effects of income diversification on the financial stability of a set of European commercial banks during the post-financial crisis period (2010–2019). From an efficiency perspective, we use a three-stage dynamic network slacks-based measure model to assess the financial stability of banks, including non-performing loans as a measure of risk that is carried over between two periods. Then, by performing a panel smooth transition regression model, we investigate the regime-switching behavior of the relationship between income diversification and bank stability by showing how these variables heterogeneously interact with each other. Our findings show that high levels of income diversification negatively and significantly impact bank financial stability regardless of the diversification index used. Important policy implications arise from our findings pertaining to the optimality of income diversification and financial stability of European banks.

The dynamics of money velocity

ABSTRACT The dynamic of money velocity has changed gradually, influencing how macroeconomic shocks affect money velocity. This paper examines the response of money velocity to the external shocks in a system of equations, where money velocity, real output growth, money growth volatility, expected inflation, and risk premium are jointly determined. The regime-switching behaviour of money velocity is then examined through a Bayesian threshold approach. The main finding suggests that money velocity drops significantly after negative output and expected inflation shocks, followed by a sudden increase. In addition, the regime-switching process distinguishes the deterministic and stochastic behaviour of money velocity.

Continuous‐time threshold autoregressions with jumps: Properties, estimation, and application to electricity markets

AbstractContinuous‐time autoregressive processes have been applied successfully in many fields and are particularly advantageous in the modeling of irregularly spaced or high‐frequency time series data. A convenient nonlinear extension of this model are continuous‐time threshold autoregressions (CTAR). CTAR allow for greater flexibility in model parameters and can represent a regime switching behavior. However, so far only Gaussian CTAR processes have been defined, so that this model class could not be used for data with jumps, as frequently observed in financial applications. Hence, as a novelty, we construct CTAR processes with jumps in this paper. Existence of a unique weak solution and weak consistency of an Euler approximation scheme is proven. As a closed form expression of the likelihood is not available, we use kernel‐based particle filtering for estimation. We fit our model to the Physical Electricity Index and show that it describes the data better than other comparable approaches.

Regime-switching empirical similarity model: a comparison with baseline models

In this paper, I extend the standard specification of the empirical similarity (ES) model of Gilboa et al. (Rev Econ Stat 88:433–444, 2006) to account for changes in parameters. I implement this by allowing for a combination of component ES models in the spirit of Gaussian mixture models. The predictive power of the modified model, along with that of the standard specification, will be assessed and compared to the baseline models consisting of autoregressions and Markov-switching autoregressions within a simulation exercise. Finally, we also compare the predictive ability of models using data on quarterly US real GDP growth. The results indicate that in situations of a more complex regime-switching behavior and a moderate to high autocorrelation in series, modified ES model demonstrates a better empirical fit. In addition, results of the empirical example show that modified ES models can better predict more extreme observations.

Regime switching in Sub-Saharan frontier equity markets - a case study of the Ghana Stock Exchange Index

We investigated regime switching behaviour of the broad aggregate returns of the Ghana Stock Exchange within the context of frontier markets. The data covered the daily period from January 04, 2011 to March 31, 2017. We made the following findings: (a) there are clear market regimes corresponding to periods of tranquillity and turbulence as demonstrated by the vastly different unconditional volatilities associated with the low and high regimes in the data (b) contrary to investor beliefs, frontier markets are less risky than reported in the popular press and (c) the returns from frontier markets are positively skewed. The regime switching model employed was compared to the workhorse GARCH(1,1) model used for volatility estimation in finance using the Deviance Information Criteria. In our findings, the MS-GARCH performed better than the GARCH(1,1), confirming recent studies in volatility modeling. The implications of these findings for trading strategies, investment and portfolio choices and risk management have been highlighted. For policymakers this study will provide a counter argument for granting tax exemptions to global investors on the basis of perceived elevated risk in frontier markets.

Regime Shifts in the Behaviour of International Currency and Equity Markets: A Markov-Switching Analysis.

This paper examines regime switching behaviour and dynamic linkages among currency and equity markets of Eurozone, India, Japan and U.S. using a Markov-switching framework. First, we seek to characterize the market specific and common regime shifts in international stock and currency markets. Second, we aim to study regime-dependent conditional correlations across these markets. We estimate state-dependent models for the financial markets in a univariate Markov-switching Autoregression (MS-AR) as well as a multivariate Markov-switching Vector Autoregression (MS-VAR) framework. The paper utilizes weekly data from July, 1999 to October, 2020 to model the interactions among the markets. Our univariate results identify two-states viz. bull state (bear state) characterized by high returns (low returns) and low volatility (high volatility) for the stock market indices and Euro/USD and INR/USD returns. For the Yen/USD market the bull state corresponds to depreciation accompanied by low volatility. Further, we employ a multivariate formulation to study the regimes across asset classes which provides additional insights into the common states across the markets. Using the MS-VAR model encompassing stocks and currencies, we find a tranquil regime characterized by lower volatility and higher returns and a turbulent regime depicted by higher volatility and lower returns. Contemporaneous correlations among asset market pairs are sharper during the crises. Some of the turbulent periods highlighted in the analysis include the dot-com bubble burst, South American crisis, 9/11, Iraq war, housing bubble burst, global financial crisis, Eurozone debt crisis, Taper Tantrum, Brexit, U.S. Federal Government Shutdown, U.S.-China Trade War and the recent COVID-19 pandemic.

On the time-varying dynamics of stock and commodity momentum returns

Using a state-of-the-art Markov switching framework augmented by popular proxies of arbitrage activity and investor sentiment, we reexamine the dynamics of stock momentum returns and provide a first structured time-series analysis of commodity momentum portfolios. Our study arrives at the important finding that, in contrast to previous studies relying on restrictive static models, we cannot detect persuasive links between momentum returns and such variables in recent data. Consequently, the evolution of momentum returns remains puzzling. Furthermore, putting the behavior of extremes aside, stock and commodity momentum returns exhibit quite similar regime-switching behavior. This supports the frequent statement that the financialization of commodity futures markets has non-trivially linked stock and commodity returns.

Forecasting crude oil volatility with geopolitical risk: Do time-varying switching probabilities play a role?

This study examines whether geopolitical risk (GPR) exhibits an ability to forecast crude oil volatility from a time-varying transitional dynamics perspective. Unlike previous studies that assume an oversimplification of the fixed transition probabilities for crude oil volatility, we develop an asymmetric time-varying transition probability Markov regime switching (AS-TVTP-MS) GARCH model. In-sample estimated results show that GPR yields strong evidence of regime switching behavior on crude oil volatility and that the negative shocks of GPR result in greater effects on switching probabilities than positive shocks. Out-of-sample results indicate that the AS-TVTP-MS GARCH model containing the GPR index outperforms other models, suggesting that the consideration of GPR information and time-varying regime switching together results in superior predictive performance. Moreover, the predictability of oil volatility is further verified to be economically significant in the framework of portfolio allocation. In addition, our results are robust to various settings.

Markov Regime-Switching Autoregressive Model of Stock Market Returns in Nigeria

This study is designed to model and forecast Nigeria’s stock market using the All-Share Index (ASI) as a proxy. By employing the Markov regime-switching autoregressive (MS-AR) model with data from April 2005 to September 2019, the study analyzes the stock market volatility in three distinct regimes (accumulation or distribution – regime 1; big-move – regime 2; and excess or panic phases – regime 3) of the bull and bear periods. Six MS-AR candidate models are estimated and based on the minimum AIC value, MS(3)-AR(2) is returned as the optimal model among the six candidate models. The MS(3)-AR(2) analysis provides evidence of regime-switching behaviour in the stock market. The study also shows that only extreme events can switch the ASI returns from regime 1 to regime 2 and to regime 3, or vice versa. It further specifies an average duration period of 9, 3 and 4 weeks for the accumulation/distribution, big-move and excess/panic regimes respectively which is an evidence of favorable market for investors to trade. Based on Root Mean Square Error and Mean Absolute Error, the fitted MS-AR model is adjudged the most appropriate ASI returns forecasting model. The study recommends investments in stock across the regimes that are switching between accumulation/distribution and big-move phases for promising returns.