Markov Regime-switching Approach Research Articles

Volatility and Dynamic Herding in Energy Sector of Developed Markets During COVID-19: A Markov Regime-Switching Approach

Volatility and Dynamic Herding in Energy Sector of Developed Markets During COVID-19: A Markov Regime-Switching Approach

The Role of Gold as a Hedge or Safe Haven in African Stock Markets during the COVID-19 Pandemic

This study seeks to investigate the role of gold as a hedge or safe haven against African stock movements during the COVID-19 pandemic by using a two-state Markov Regime-Switching approach. The findings suggest that gold serves as a weak hedge in Malawi, Morocco, Nigeria, Tunisia, and Zambia, a weak safe haven in Ghana, Kenya, Mauritius, Rwanda, Uganda, and Zimbabwe, and a strong safe haven in Botswana and Tanzania. Furthermore, the findings reveal that gold co-moves with the South African stock market regardless of the market regime. These findings imply that, on average, gold offers limited protection for African equity investors.

CORRELATION BETWEEN BITCOIN, STOCKS AND GOLD: A MARKOV REGIME-SWITCHING APPROACH

ABSTRACT The following article explores the correlation between bitcoin and both stocks and gold. A Markov regime-switching approach was used to identify and date two regimes in each of these financial assets. Stock returns are characterized by short-lived episodes of elevated volatility and negative returns whereas bitcoin returns are characterized by a persistent high volatility state with positive returns. Gold stayed in the low volatility period most of the time and only a few short-lived episodes of high volatility were identified during the first year of the pandemic. A concordance measure was computed to assess the synchronicity and correlation between the regimes. The regimes of bitcoin and gold are uncorrelated suggesting that bitcoin is not yet perceived as a safe haven like gold. The regimes of bitcoin and stocks were also uncorrelated suggesting that bitcoin may be used as a hedge against stocks. Keywords Bitcoin, correlation between bitcoin and stock market, correlation between bitcoin and gold, Markov regime-switching.

Three-Point Rule, Average Goals Per Game and Predictability in Football: A Long-Term Analysis with The Markov-Regime Switching Approach

In this study, the effects of the three-point rule in football on the average goals per game and the predictability of the matches were investigated by using the Markov-Regime Switching Model. The results obtained in the analysis covering 61 seasons and 3 teams in the example of the Turkish First Division Football League showed that the three-point rule increased the average goals per game, as expected. However, the effects of the three-point rule on the predictability of matches are different for each team.

Impact of Public and Private Investment on Economic Growth in Nigeria

The contention in the literature is the relative contribution of private and public investment on economic growth and whether the relationship is linear or non-linear. In addition, there is the issue of whether the impact of investment on economic growth changes depending on public and private investment Purpose. The study examines the relationship between investment (public and private) and economic growth in Nigeria over the period 1970-2016. Design/Methodology/Approach. The study employs Markov regime-switching approach developed by Hamilton (1989, 1990). Specifically, a multivariate dynamic Markov-switching model is estimated using maximum likelihood estimation techniques. The study employs annual time-series sourced from Central Bank of Nigeria, Statistical Bulletin and World Bank, World Development Indicator. Findings/Implications. The results show that the relationship between investment and economic growth is non-linear. Also, both public and private investments have a significant positive impact on economic growth. However, private investment contributes more to economic growth than public investment during the period of expansion. The reverse is the case during the period of contraction. The results support the basic neoclassical framework, with emphasis on savings and investment for analyzing long-term growth performance. Also, it is crucial to make a distinction between the impact of investment (public and private) on growth in two stages of growth. Originality. Government needs to be innovative by spending more during period of slump as more public investment will be required to pump prime the economy for increased private investment.

The Impact of Uncertainty on Trade: The Case for a Small Port

In the present paper, we show how uncertainty emanating from fluctuations in economic uncertainty, news-based uncertainty, and geopolitical risks affect the number of containers exported from Thailand via Penang Port, Malaysia. Our sample extends from January 2009 to May 2020 from three main entry points in the Northern Peninsular Malaysia–Thailand Border: Padang Besar, Surat Thani, and Bukit Kayu Hitam. Two modes of transportation of containers are mainly used for export purposes, namely, road and rai. This study examines the nonlinear effect of uncertainty on trade by employing a two-regime Markov regime-switching approach. The empirical results show that, overall, uncertainty significantly affects the movement of containers in the high-uncertainty regime. Therefore, small ports must continue to diversify their client base to cushion the impact of fluctuations in global trade due to uncertainty.

An examination of herding behaviour of the Chinese mutual funds: A time-varying perspective

This paper investigates the existence of herding behaviour in the Chinese mutual fund market from a time-varying perspective. We examine the relationship between the dispersion of fund returns and the fund market returns using a Markov regime switching approach, and explore the driving factors of herding under different regimes. Our results suggest that herding behaviour is time-varying and heterogeneous across different fund types, investment styles, fund sizes, and industrial groups. In addition, the observed herding behaviours are mainly driven by non-fundamentals. We also find that herding behaviour is more pronounced during the up market and becomes insignificant during the down market.

Asset allocation of Australian superannuation funds: a markov regime switching approach

We extend an observable Markov Regime Switching framework to assess the switching behaviour of asset classes of Australian superannuation funds across different fund sizes. We identify the most prominent asset class which contributes to the performance of the investment options and what factors trigger funds’ decisions on rebalancing their portfolio. We find that smaller funds tend to be more active in switching to aggressive options and the larger funds are more conservative. However, in periods of volatility, the large funds are the risk seekers and tend to switch their asset classes and hence their investment strategies. The asset classes whose values add to the performance of the investment options are equity markets and bond markets with the domestic equity market having better performance than international equity market. The switch for the larger funds is driven by volatility of the equity market.

Re-examining oil price-stock market returns nexus in Nigeria using a two-stage Markov regime switching approach

PurposeIt has been said that oil price shocks affect stock market returns. However, empirical studies remain inconclusive regarding the nexus between oil price shocks and stock market returns. Consequently, the purpose of this study is to investigate the asymmetric impact of oil price shocks on stock returns in Nigeria.Design/methodology/approachA two-stage Markov regime-switching approach is used to examine the asymmetric effects of three different structural oil shocks on stock returns. The oil shocks, which include oil supply shock, aggregate demand shock and oil-specific demand shock, are derived using structural vector autoregressive. Monthly data that spans the period between January 1990 and December 2018 are deployed for estimation.FindingsThe linear estimation results show that only oil demand shock negatively and significantly affects the stock market returns. The Markov-switching regime results reveal that oil supply shock has a significant positive impact on the stock returns in a low-volatility state, whereas oil-specific demand shock negatively impacts the stock returns in a high-volatility state.Practical implicationsThere is a need for policymakers and investors to take cognizance of not only the positive outcomes of a relatively stable state of oil price but also the negative consequences of a high-volatility state when formulating policy and making investment decisions, respectively.Originality/valueThis study differs from other similar studies in Nigeria that have examined the asymmetric relationship between oil price shocks and stock market return by using a two-stage Markov regime-switching approach. To the best of the authors’ knowledge, this is the first attempt at using this methodology.

Revisiting the macroeconomic variables and economic growth nexus: A Markov regime-switching approach

Purpose ―Current paper assesses the impact of macroeconomic variables on Pakistan's economic growth. Method ― This study analyzed the data using the Markov Regime switching (MS) model using monthly data for 1981-2020. Firstly, BDS and CUSUM square tests were applied to detect the non-linearity of the model. Results ―The model is non-linear, so the Markov regime-switching model is used for analysis. Each regime's mean and variance are highly significant and show a high growth regime with high volatility and a low growth regime with low volatility. Furthermore, the results show that inflation, interest rate, and trade openness negatively impact while real effective exchange rates positively affect development in both regimes. The negative effect of interest rate, exchange rate, inflation, and trade openness become more pronounced in low growth regimes. Implication ― This study suggests that policymakers should consider the non-linear behaviour of macroeconomics. This will help to formulate better policies for the economy's economic growth. Originality ―The current research adds to the existing literature by identifying the non-linear effect of growth indicators on economic growth, which was previously neglected in the case of Pakistan.

Constructing and Characterising the Aggregate South African Financial Cycle: A Markov Regime-Switching Approach

Constructing and Characterising the Aggregate South African Financial Cycle: A Markov Regime-Switching Approach

A Markov regime-switching (MS) approach to modeling the effects of fiscal policies and COVID-19 pandemic on tourism destination competitiveness in Kenya

PurposeThe COVID-19 pandemic has had a catastrophic impact on the tourist activity in Kenya. Global lockdown has limited travel resulting to losses in the tourism sector. This paper discusses the specific role that fiscal policy plays to improve tourism competitiveness in Kenya. Specifically, the study examines how Kenyan government can revive the tourism economy to improve its competitiveness.Design/methodology/approachA tourism demand model to explore relationship between fiscal policies and inbound tourism in Kenya is developed. This study uses a Markov regime-switching (MS) regression model to establish the relationships that exist between COVID-19 pandemic, fiscal policies and tourism revenue in Kenya.FindingsThe estimation results of the Markov-switching dynamic regression showed that the coefficients of international tourists arrivals, domestic bed occupancy and international bed occupancy are positive and significant with p-values of 0.000 during the pandemic period. The findings show that the transitioning periods during the fiscal policy shifts had an effect on the international arrivals. Therefore, fiscal incentives were key in influencing tourism arrivals and bednights occupancies.Research limitations/implicationsThe theoretical implications show that to promote the state of high international and domestic tourist arrivals, the government should encourage more fiscal spending initiatives that encourage the increase in tourist arrivals and occupancies such as vaccinations against COVID-19 and promoting safe spaces for visitors within the destination is key towards reviving the sector. In order to curb the hysteresis effects of COVID-19 related depression and resultant impacts on GDP, there is a need to review the national fiscal policies and target fiscal policies on the cyclical effects of the COVID-19 impacts on international tourism market.Originality/valueThis research develops an economic model that builds accurate relationships between fiscal policies, pandemics and tourism destination competitiveness as a means of informing competitive tourism management strategies and governance.

Foreign exchange intervention and monetary policy rules under a managed floating regime: evidence from China

ABSTRACT This paper investigates the consequences of foreign exchange (FX) intervention in monetary policy under a managed floating regime. Focusing on China’s FX intervention, we identify periods of strong and weak FX intervention using a Markov regime switching approach. We then evaluate quantity-based monetary policy rules using both regime switching reduced-form and structural estimations. In particular, monetary policy regimes obtained from the structural estimation match well with the previously identified intervention regimes. We find that the People’s Bank of China has significant exchange rate stabilization incentives during periods of strong FX intervention, and that the monetary policy rule depends on the state of FX intervention. Furthermore, our estimations point to a trade-off between the central bank’s internal and external policy targets in that strong FX intervention leads to weak responses to domestic GDP and inflation fluctuations.

Detection of structural regimes and analyzing the impact of crude oil market on Canadian stock market: Markov regime-switching approach

PurposeThis study aims to analyze the impact of the crude oil market on the Toronto Stock Exchange Index (TSX).Design/methodology/approachThe focus is on detecting nonlinear relationship based on monthly data from 1970 to 2021 using Markov-switching vector auto regression (VAR) model.FindingsThe results indicate that TSX return contains two regimes: positive return (Regime 1), when growth rate of stock index is positive; and negative return (Regime 2), when growth rate of stock index is negative. Moreover, Regime 1 is more volatile than Regime 2. The findings also show the crude oil market has a negative effect on the stock market in Regime 1, while it has a positive effect on the stock market in Regime 2. In addition, the authors can see this effect in Regime 1 more significantly in comparison to Regime 2. Furthermore, two-period lag of oil price decreases stock return in Regime 1, while it increases stock return in Regime 2.Originality/valueThis study aims to address the effect of oil market fluctuation on TSX index using Markov-switching approach and capture the nonlinearities between them. To the best of the author’s knowledge, this is the first study to assess the effect of the oil market on TSX in different regimes using Markov-switching VAR model. Because Canada is the sixth-largest producer and exporter of oil in the world as well as the TSX as the Canada’s main stock exchange is the tenth-largest stock exchange in the world by market capitalization, this paper’s framework to analyze a nonlinear relationship between oil market and the stock market of Canada helps stock market players like policymakers, institutional investors and private investors to get a better understanding of the real world.

A Comparative Performance Analysis of Sustainability Themed Indices in India: Markov Regime Switching Approach

Sustainability Indices serve as a benchmark for the companies screened for their superior performance on environmental, social and governance (ESG) parameters. This article intends to compare the overall and regime-specific financial performance of socially responsible indices of the National Stock Exchange, Nifty100 ESG and Nifty100 ESG Enhanced with Nifty50 (representing the market) from 1 April 2012 to 31 March 2020. Overall comparative performance analysis of these indices is conducted using risk-adjusted return measures and volatility has been captured through the TGARCH model. Further, time duration has been decomposed into regimes using Markov Regime Switching Model and the comparison of indices has been undertaken in both regimes. Our findings suggest that there is no significant difference between the return performance of sustainability indices and market benchmark index in single time duration and sustainability indices performing marginally better in both the regimes identified. This implies that socially responsible investments in India are providing reasonable returns to investors without comprising non-financial objectives. For corporates, it is a win–win situation to focus on ESG parameters to attract capital from investors and deliver better corporate financial performance and hence increasing the potential of growth of socially responsible investing in India.

Modeling the exchange rate pass-through in Turkey with uncertainty and geopolitical risk: a Markov regime-switching approach

PurposeThis paper aims to investigate the pass-through (PT) effect in Turkey by using quarterly data for the period 1998: Q1-2019: Q2 to understand the dynamic potential effects of exchange rates on domestic prices.Design/methodology/approachThe paper launches several nonlinear models in which the basic determinants of domestic prices in Turkey are determined through Markov regime-switching models (MSMs). Hence, this research follows the variables of the consumer price index (CPI), USD exchange rate, gross domestic product (GDP; demand side of the economy), industrial production index (production side of the economy), economic uncertainty and geopolitical risk index for Turkey.FindingsThis work explores that the exchange rate and demand side of the economy (GDP) follow a positive nonlinear relationship with CPI at both regimes. The production side of the economy (IP) affects negatively the CPI during regime 0. Economic uncertainty influences the CPI positively at Regime 1, while geopolitical risk has a negative association with CPI at Regime 0. Eventually, the paper provides some policy proposals associated with the impacts of GDP, IP, economic uncertainty and geopolitical risk on CPI in Turkey.Originality/valueOne may claim that any PT model, which does not observe the possible structural or regime shifts in estimated parameters, might fail to estimate the coefficients unbiasedly and efficiently. Hence, this work differs from available relevant works in the literature since this paper considers linearity or nonlinearity important and reveals that the relevant PT model follows a nonlinear path rather than a linear path, this nonlinear path is converged strongly by MSMs and estimates the significant regime shifts in the constant term and, in parameters of independent variables of PT by MSMs.

Modeling the relationship between the Russian ruble exchange rate and oil prices: Markov regime switching approach

This paper examines the relationship between the Russian ruble/US dollar exchange rate and global oil prices using autoregressive model with Markovian regime shifts. Empirical analysis on daily data for 2009–2019 shows that exchange rate dynamics is best described by three regimes, characterized as follows: 1) weak exchange rate reaction to oil price shocks — low conditional volatility of exchange rate changes; 2) strong reaction — moderate volatility; 3) strong reaction — high volatility. Regime 3 covers crisis periods, when ruble depreciated substantially. Regime 1 prevailed during the period of managed exchange rate arrangement lasted until November 2014. After adoption of a floating exchange rate and inflation targeting policy, regime 1 became regularly identified since mid-2017. This result can be attributed to the introduction in 2017 of a new budget rule, aimed to reduce dependence of exchange rate on oil price fluctuations. Switches between regimes could also be due to fluctuations in the uncertainty measured by the indices of geopolitical risk and economic policy uncertainty for Russia. It is also shown that the model with three regimes outperforms the random walk and linear models of the ruble exchange rate in an out-of-sample fit exercise. The proposed model can be used for identifying the current exchange rate regime in real time, scenario analysis of the consequences for the ruble exchange rate under alternative oil price trajectories, as well as in developing strategies for hedging currency risks by the private sector.

A Markov Regime Switching Approach of Estimating Volatility Using Nigerian Stock Market

Understanding and forecasting the behavior of volatility in stock market has received significant attention among researchers and analysts in the last few decades due to its crucial roles in financial markets. Portfolios managers, option traders, and market makers are all interested in the possibility of forecasting, with a reasonable level of accuracy. This study examined the volatility on the Nigeria stock market by comparing two Markov regime switching Autoregressive (MS-AR) Models estimated at different lagged values using the Nigeria stock exchange monthly All Share Index data from 1988 to 2018 in the Central Bank of Nigeria (CBN) Statistical Bulletin. It was found that factors like financial crisis, information flow, trading volume, economical aspects and investor’s behavior are the causes of volatility in the stock market. The results and forecasts obtained from the statistical analysis in this research showed that the stock market will experience a steady growth in 2020 and beyond. Also, the stock market is experiencing fluctuations in the price indices which show that over the years, investors have been exposed to some certain risks in the time past. We therefore recommended that researchers should focus more attention in developing robust statistical model that will reflect and continue to monitor future trends and realities.

A Markov regime-switching regression approach to modelling NDVI from surface temperature and soil moisture

ABSTRACTBuilding accurate relationships between vegetation amount and climatic variables is helpful in understanding and informing sustainable environmental management. The common approach in this regard is to develop a generic, linear relationship or season-dependent relationships. Such approaches, however, fail to hold if data characteristics deviate from expected patterns. This study applied a regime-switching regression model, namely the Markov-switching (MS) approach, to predict time-series Normalized Difference Vegetation Index (NDVI). This was done using surface temperature, soil moisture and the interaction of surface temperature and soil moisture as regressors at monthly temporal resolution. Modelling was executed at the biome spatial (broad vegetation categories) scale. The results showed that the MS approach captured the non-linear dynamics in the data for each of the eight biomes considered in the study. The accuracy of the MS approach compared to non-switching modelling approach was evident in model comparison criteria including significance of parameter estimates, coefficient of determination (R2), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and log-likelihood as well as post-modelling diagnostics such as residual plots, autocorrelation function (ACF) and partial autocorrelation function (PACF) of residuals, and squared residuals. Overall, the study clearly demonstrates the superiority of MS modelling that captures non-linear relationships that may not be modelled using conventional non-switching modelling. Further studies are encouraged to test the approach at larger spatial scales.

A Markov regime switching approach to estimating the volatility of Johannesburg Stock Exchange (JSE) returns

The study used the Markov regime switching model to investigate the presence of regimes in the volatility dynamics of the returns of JSE All-Share Index (ALSI). Volatility regimes are as a result of sudden changes in the underlying economy generating the market returns. In all, twelve candidate models were fitted to the data. Estimates from the regime switching model were compared to the industry standard non-switching GARCH (1,1) using the Deviance Information Criteria (DIC). The results show that the two-regime switching EGARCH model with skewed Student t innovations describes better the return of the JSE Index. Additionally, we backtest the model results in order to confirm our findings that the two-regime switching EGARCH is the best of the models for the sample period.