Markov Regime-switching Model Research Articles

Geopolitical threats, equity returns, and optimal hedging

In this paper, we demonstrate that the U.S. equity market and a few specific sectors produce significantly positive returns during high geopolitical threats, even with the presence of standard controls, whereas other major markets around the world fail to exhibit such results. We use the geopolitical threats (GPT) index of Caldara and Iacoviello (2022). We extend our study by examining the equity returns during extremely high geopolitical threats and find the results significantly positive for the U.S. equity market and two specific sectors- information technology and financials. The results of our investigation are likewise supported by the lead-lag regression and the Markov regime-switching model. Our results are robust in the presence of various alternative measures of market uncertainty indices, for instance, economic policy uncertainty, economic uncertainty, macroeconomic uncertainty etc., on a daily basis. However, the return on equity was not robust when conditional volatility and monthly frequency were considered. We also investigate and find the optimal hedging implications for investors during the presence of geopolitical threats. We find a considerable hedge alternative between the US market and gold and further explore how Geopolitical threats affect Gold and different US sectoral Exchange-traded funds (ETFs).

Determinants of the European Sovereign Debt Crisis: Application of Logit, Panel Markov Regime Switching Model and Self Organizing Maps.

The study aims to empirically identify the determinants of the debt crisis that occurred within the framework of 15 core EU member countries (EU-15). Contrary to previous empirical studies that tend to use event-based crisis indicators, our study develops a continuous fiscal stress index to identify the debt crises in the EU-15 and employs three different estimation techniques, namely self-organizing map, multivariate logit and panel Markov regime switching models. Our estimation results show first that the study correctly identifies the time and the length of the debt crisis in each EU-15-member country. Empirical results then indicate, via three different models, that the debt crisis in the EU-15 is the consequence of deterioration of both financial and macroeconomic variables such as nonperforming loans over total loans, GDP growth, unemployment rates, primary balance over GDP, and cyclically adjusted balance over GDP. Furthermore, variables measuring governance quality, such as voice and accountability, regulatory quality, and government effectiveness, also play a significant role in the emergence and the duration of the debt crisis in the EU-15.

Political stability and credibility of currency board

Do currency boards offer protection against self-fulling speculative attacks? This paper examines the credibility of currency boards of Argentina, Bulgaria, Estonia, Hong Kong, Latvia, and Lithuania. We employ a Bayesian Markov regime-switching model to analyze the role of economic fundamentals and self-fulfilling expectations in accounting for the credibility of the currency board. We find that the credibility of our sample currency boards is all subject to self-fulfilling runs. We also find that the political stability of adopting economies relates to the credibility of their currency boards and explains a few self-fulfilling runs of their currency boards.

Analyzing and forecasting electricity price using regime‐switching models: The case of New Zealand market

AbstractThis paper aims to study the forecasting capabilities of several models under the Markov regime‐switching (MRS) and the extreme value theory (EVT) frameworks applied to daily electricity prices in the New Zealand electricity market. The MRS models in this study include up to five regimes, with time‐varying transition probabilities and incorporation of external market variables. We apply Hamilton's filter with maximum likelihood estimation for parameter estimation. The EVT peaks‐over‐threshold (EVT‐PoT) framework is also considered, and its relationship to the MRS class of models is discussed. We generate out‐of‐sample forecasts under various market scenarios. The MRS models are able to replicate real price densities under stable market conditions. The EVT‐PoT model performs well despite its lack of complexity compared to the MRS framework. We attribute this to the usage of the generalized Pareto distribution to model price extremities.

A Study of Trade Strategies Based on the Markov Regime Switching Model

Market traders often buy and sell stocks to maximize their total return. For each purchase and sale, there is often a return commission. Having trading technology plays an important role in quantitative trading. In this paper, we first use the XGBoost model to learn the historical price fluctuation data of gold and bitcoin, and the prediction accuracy R2 is between 0.998 and 0.999, which is a good fit. Then LT algorithm and PS algorithm are used to identify the rules, the Markov regime switching model is used to determine the bear and bull market, and finally, the DQN model is used to plan the trading strategy and add a machine learning algorithm to it to optimize the strategy to get the initial $1000 in the future investment strategy.

How competitive is SRI in developing financial markets: The case of Central and Eastern Europe

This study investigates the competitiveness of sustainable and responsible investment (SRI) in Central and Eastern European (CEE) financial markets. Specifically, we examined whether a statistically significant measurable difference in the return and volatility between an SRI index and two conventional benchmark indices in the CEE region exists. To test whether the market volatility may affect the results, we applied a Markov regime-switching model to examine the performance in high and low volatility environments. We also used the Fama-French three-factor model to analyse the potential sources of outperformance and verify the initial analysis results. The analysis covered an eleven-year period (January 2011–December 2021) and was based on monthly returns of indices available on the Vienna Stock Exchange: CECE SRI, CECE Composite and CECE MID. Our findings showed that the SRI phenomenon in developing financial markets of the CEE countries followed performance patterns similar to ones in developed financial markets. Sustainable and responsible investment is competitive with conventional investment in the CEE region. However, the differences in returns between the SRI index and conventional benchmarks were statistically insignificant. Although a statistically significant difference in volatility between the SRI index and the large-cap CECE Composite index was reported, we did not find any evidence of exposure to the SRI factor regarding the analysed returns of the CECE SRI index. Our analysis of SRI returns pointed to the statistical significance of the common risk factors, such as the market and the size, which is similar to the analysed conventional benchmarks, with alpha not being statistically significant.

The behavior of the real effective exchange rate of Ethiopia: does misalignment exist?

ABSTRACT This study examined the main drivers of real effective exchange rate (REER) misalignment for Ethiopia over the period 1970–2020. Based on the behavioural equilibrium exchange rate concept, the study used the dynamic OLS and Markov regime switching model (MSM). The cointegration results revealed that the REER appreciates for an increase in relative productivity, government spending and net remittances, but it depreciates for more trade openness and real investment in the long-run. The MSM results also suggested a distinct misalignment episodes of under- and overvaluation with significant variability for the Ethiopia’s Birr over the study period.

The volatility of natural resources implications for sustainable development: Crude oil volatility prediction based on the multivariate structural regime switching

Applying the multivariate heterogeneous autoregressive (MHAR-RV-DCC) model, this article considers the structural mutation characteristics of oil futures and the U.S. stock markets’ information fluctuations, and embeds Markov regime switching (MS) to investigate the prediction performance of the crude oil market volatility prediction model under the transition of high and low states, and explores whether the Markov mechanism conversion multivariate model constructed by nonparametric measurement variables will help the government, scholars and practitioners to judge the future market. We test the in-sample fitting and out-of-sample forecasting. In comparison, our newly proposed multivariate MHAR-RV-DCC model and MS-MHAR-RV-DCC model have stronger predictive capabilities than other high-frequency volatility prediction models, the MS-MHAR-RV-SJV-DCC has the best predictive ability. The above empirical results not only affirm the advantages of the newly constructed multivariate and Markov regime switching models in the application of crude oil futures market forecasting but also broaden the research ideas and specific methods for the characterization and prediction of crude oil market volatility in the future.

The Regime Examination of Nigeria Exchange Rate Volatility: Evidence from Markov Regime Switching Autoregressive Approach

Inarguably, the escalation in dollar rates and the price instability in the Nigerian economy underwent significant structural and institutional changes. In assessing the importance of understanding exchange rates, it becomes imperative to build reliable models for predicting the volatility of exchange rates of home currency. Hence, this study aims to model the Nigerian exchange rate volatility using the Markov regime-switching model. The study analyses the Nigerian exchange rate returns in two and three distinct regimes by employing the Markov regime-switching autoregressive (MS-AR) model with data from 2nd January 2018 to 7th September 2020. Four MS-AR candidate models were estimated for the exchange rate series. Based on the least AIC value, MS(3)-AR(2) was returned as the most parsimonious model among the four candidate models. The MS(3)-AR(2) analysis established a high probability that the returns system remains in the liquidation and awareness states. It implied that only unconventional or severe events could switch the series from regime 2 (liquidation phase) and regime 3 (awareness). While there is a low probability that the system will stay in an imbalanced regime implies high switching of regime 1. Furthermore, an average duration period of 2 days, six days and five days were estimated for the imbalance, liquidation and awareness regimes, respectively. Thus, the findings, i.e. imbalance and liquidation regimes’ identification and their average durations, show that the Naira in the foreign exchange market is not favourable for investors to trade. The study recommends that the Nigerian government should direct more efforts towards improving the performance of the Naira in the foreign exchange market to make the market more favourable for investors. Specifically, the CBN should develop new strategies towards tackling the behaviour of the Nigerian exchange rate when in a liquidation state.

On testing for bubbles during hyperinflations

Abstract We consider testing for the presence of rational bubbles during hyperinflations via an analysis of the non-stationarity properties of relevant observable time series. The test procedure is based on a Markov regime-switching model with independent stochastic changes in its intercept, error variance and autoregressive coefficients. This model formulation allow us to disentangle fundamentals-driven changes in the drift, bubble-driven explosiveness, and volatility changes that may be fundamentals-driven and/or bubble-driven. The testing methodology is illustrated by applying it to data from hyperinflations in Argentina, Brazil, Germany and Poland.

Dynamics of time-varying currency beta on Indian industries: A Markov switching approach

We examine the dynamics of time-varying currency beta across Indian industries. Through the Markov regime switching model we try to check whether currency beta is also regime-dependent, similar to other financial variables. The paper finds that currency betas are different in sign and magnitude for different industries. During different crisis periods, the nature of currency beta revealed different volatility for different industries. Results also exhibit strong evidence of regime switch behaviour of currency beta. The findings are important to hedgers and portfolio managers who want to hedge and diversify currency beta during normal and turbulent market situations in an emerging economy like India.

Fiscal and monetary policy coordination and debt sustainability in the CEMAC zone: Evidence from a Markov regime-switching model

This paper investigates the nature of fiscal and monetary policy implemented in the Economic and Monetary Community of Central Africa (CEMAC) and its impact on long-run debt sustainability. The study employs a two-state Markov regime-switching model to the fiscal and monetary policy reaction functions of the six CEMAC countries using time series data from 1970 to 2020. The fiscal regime is regarded as active if the coefficient of debt is positive, while it is considered passive if the coefficient is negative and significant. Regarding monetary policy, an active regime is reported if the coefficient of inflation is positive; otherwise, the monetary policy regime is regarded as passive. In addition, the transition probability and time-varying transition probability are estimated to assess the nature of policy-mix coordination. The findings highlight unsustainable fiscal regimes in Equatorial Guinea and Chad, balanced results for Gabon and Congo, while Cameroon and the Central African Republic have sustainable fiscal regimes. Concerning monetary policy, a sustainable monetary regime is found for Cameroon and Equatorial Guinea, with balanced results for Congo, while evidence of an unsustainable monetary regime is found for the Central African Republic, Chad, and Gabon.

Markov-Regime Switches in Oil Markets: The Fear Factor Dynamics

This paper is an attempt to examine regime switches in the empirical relation between return dynamics and implied volatility in energy markets. The time-varying properties of the return-generating process are defined as a function of several risk factors, including oil market volatility and changes in stock prices and currency rates. The empirical evidence is based on Markov-regime switching models, which have the capacity to capture, in particular, the stochastic behavior of the OVX oil volatility index as a benchmark for investors’ fear. The results suggest that the dynamics of oil market returns are governed by two distinct regimes, a state driven by a negative relationship between returns and implied volatility and another state characterized by a more pronounced negative correlation. It is the latter regime with a stronger correlation that tends to prevail over the sample period from 2008 to 2021, but the frequency of regime shifts also seems to increase under more volatile oil price dynamics in association with significant events such as the COVID-19 pandemic. Thus, the evidence of a negative correlation structure is found to be robust to changes in the estimation period, which suggests that the oil volatility index remains a reliable gauge of market sentiment in the energy markets.

The risk premium in times of financial crisis: an assessment from ICAPM on the MENA region

The purpose of our research is to study the impact of financial crisis on Risk premium evolution for a set of emerging countries of MENA (Middle East and North Africa), giving special attention to the appreciation of the local risk premium in addition to the currency risk premium through the study of the dynamics of the financial integration under two assumptions: perfect integration and partial segmentation. At the methodological level, we test a conditional version of the international model of the financial assets ICAPM of De initially proposed by Adler and Dumas 1983 [Adler, M., & Dumas, B. (1983). International portfolio choice and corporation finance: A synthesis. The Journal of Finance, 38(3), 925-984.]. Our analysis is based on the conditional model of regime change of Bekaert and Harvey (1995) [Bekaert, G., & Harvey, C. R. (1995). Time‐varying world market integration. The Journal of Finance, 50(2), 403-444.], we follow the econometric modeling using the Kalman filter and the Markov regime-switching model with variable transition probabilities.

Open-loop equilibrium strategy for mean-variance Portfolio selection with investment constraints in a non-Markovian regime-switching jump-diffusion model

<p style='text-indent:20px;'>This paper is devoted to study the open-loop equilibrium strategy for a mean-variance portfolio problem with investment constraints in a non-Markovian regime-switching jump-diffusion model. Specially, the investment strategies are constrained in a closed convex cone and all coefficients in the model are stochastic processes adapted to the filtration generated by a Markov chain. First, we provide a necessary and sufficient condition for an equilibrium strategy, which involves a system of forward and backward stochastic differential equations (FBSDEs, for short). Second, by solving these FBSDEs, we obtain a feedback representation of the equilibrium strategy. Third, we prove a theorem ensuring the almost everywhere uniqueness of the equilibrium solution. Finally, the results are applied to solve an example of the Markovian regime-switching model.</p>

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.

Stock market volatility predictability in a data-rich world: A new insight

This study develops a shrinkage method, LASSO with a Markov regime-switching model (MRS-LASSO), to predict US stock market volatility. A set of 17 well-known macroeconomic and financial factors are used. The out-of-sample results reveal that the MRS-LASSO model yields statistically and economically significant volatility predictions. We further investigate the predictability of MRS-LASSO with respect to different market conditions, business cycles, and variable selection. Three factors (equity market returns, a short-term reversal factor, and a consumer sentiment index) are the most frequent predictors. To investigate the practical implications, we construct the expected variance risk premium (VRP) by using volatility forecasts generated from the LASSO and MRS-LASSO models to forecast future stock returns and find that those models are also powerful.

Macroeconomic Determinants of Credit Risk: Evidence on the Impact on Consumer Credit in Central and Eastern European Countries

Although empirical studies show that different types of loans have different risks (moreover, consumer credit risk is higher compared to other types of loans), it is common to study the credit risk of the banking sector as a whole, or of an individual bank’s whole loan portfolio, and the macro-economic factors affecting it (without grouping them by type of loan). Thus, an analysis of the credit risk of the whole loan portfolio (measured by all non-performing loans) is insufficient. Therefore, the aim of this research is to identify the macroeconomic determinants of the consumer loan credit risk and quantitatively assess their impact in Central and Eastern European countries. After the analysis of scientific literature in the field of credit risk determinants, a detailed classification of factors influencing banking credit risk is proposed. The distinguishing feature of the classification is that the factors influencing credit risk are classified at five different levels; twelve groups of general macroeconomic conditions variables were selected as the potential factors of NPLs. This classification can be useful to better understand and investigate the factors influencing banking credit risk for the whole loan portfolio (in the same way as the factors that affect the credit risk of different types of loans, e.g., consumer loans). Using the methods of constant, fixed and random-effects panel analysis, simple OLS, least squares with breakpoints regression analysis and Markov regime-switching models, the impact of the macroeconomic variables from twelve separate groups is evaluated. The data from 11 CEE countries are used, and the period from 2008 to 2020 is covered. The results of this assessment reveal that in the group of CEE countries, such variables as GDP and labour market variables appeared to have contributed to the increase in the share of non-performing consumer loans, while inflation and real estate market variables were related to the decrease in consumer NPLs; at the same time, the impact of variables form other groups appeared to be mixed-nature or insignificant. The results of this research are useful in that they allow the identification of the most important determinants of consumer loan credit risk and thus allow making assumptions about NPL changes due to the changing macroeconomic situation. In the case of Lithuania, this kind of study (assessment of macroeconomic determinants of consumer loan credit risk) was conducted for the first time. Consumer loan credit risk assessment is especially relevant in an increasing interest rate environment, and deeper analysis can help banks and other financial institutions to manage credit risk. On the other hand, a better understanding of the main influencing factors of the macroeconomic environment can help central banks and other official institutions take appropriate monetary and fiscal policy decisions to ensure a good credit transmission channel for sustainable economic growth.

Time-Varying Risks in ESG Equity Investments during the COVID-19 Pandemic

We analyse the time-varying risks associated with ESG equity investments in developed, emerging and BRIC equity markets in the wake of the COVID-19 pandemic which has once again underscored the vulnerability of the financial space to shocks. For this purpose, the nonlinear Markov regime switching model is used to analyse the time-varying beta and idiosyncratic volatility of the World ESG Leaders, Emerging Markets ESG Leaders and BRIC ESG Leaders equity portfolios provided by Morgan Stanley Capital International (MSCI). To further complement the evidence, we also refer to the global ACWI ESG Leaders index which represents both developed markets and emerging markets. The evidence suggests that the risk dynamics of the ESG equity portfolios representing the developed markets, emerging markets, BRIC markets and the global markets are distinct during the crisis and calm period. ESG equity investments have higher systematic risk exposure in emerging markets and BRIC markets during the crisis period as well as calm periods. On the other hand, ESG equity investments have higher systematic risk exposure during the crisis period only in case of developed markets. The results of the study provide insights on the time -varying risk dynamics of ESG investing and thus, facilitate informed investment decisions.

Geopolitical risk and renewable energy asset prices: Implications for sustainable development

This study intends to investigate the impact of geopolitical uncertainty, proxied by the geopolitical risk (GPR) index, on the volatility of renewable energy exchange traded funds (ETFs). Employing a two-state Markov regime switching model reveals that an upturn in the GPR index increases (reduces) the likelihood of being in the low (high) volatility regime. This finding could be attributed to the fact that when the geopolitical risk increases, users of crude oil, which is highly sensitive to such risk, tend to consider clean energy as a substitute for traditional energy sources. This causes a growth in the equity prices of new energy firms, further leading to a drop in the levels of volatility. Additionally, the results of generalized autoregressive conditional heteroscedasticity (GARCH) models also confirm that higher GPR implies lower risk for these green assets. The outcomes have implications to policymakers and investors participating in clean energy markets.