Markov Regime-switching Model Research Articles

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.

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.

Optimal investment and reinsurance to reach a bequest goal with random time solvency regulation

ABSTRACT This paper studies optimal investment and proportional reinsurance policies for an insurer with Markov regime-switching model and random time solvency regulation. The goal of the insurer is to maximise the probability that its wealth exceeds a predetermined level l before the regulatory time arrives. By constructing an auxiliary control problem without regime switching, together with the classical results on Hamilton-Jacobi-Bellman (HJB) equation and fixed-point method, we prove the regularity of the value function. When the current state of the Markov chain is given, we found that the optimal policies for an insurer in a multiple-regime market is the same as those in a single-regime market. Explicit optimal policies can be derived when the premium is calculated by the expectation principle. For more general cases, numerical schemes for value functions and feedback optimal policies are given by the Markov chain approximating method.

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.

AN ASSESSMENT OF THE FELDSTEIN–HORIOKA PUZZLE FOR INDIA: A MARKOV REGIME SWITCHING APPROACH

The study aims to examine the Feldstein–Horioka puzzle in the Indian context during 2002Q2 to 2019Q4 to unearth nonlinear patterns in the data. Findings from an augmented Markov regime-switching model reveal two distinct, yet random regimes for correlations between savings and investment-GDP ratios, with higher correlation between the variables persisting for a longer duration. In the low correlation regime, trade-related factors, policy uncertainty and global shocks have a significant impact on the correlation. Fiscal shocks and trade costs explain high correlations in regime 2, while global shocks act to decrease the correlation in this regime. The high correlation regime dominates the study period and indicates that the Feldstein–Horioka puzzle of limited capital mobility persists in India. Economic agents appear to respond rapidly to changing domestic and global policy conditions, besides being affected by incomplete integration of goods markets, explaining the pattern of correlations.

Crude Oil Price Movements and Institutional Traders

We analyze the role of hedge fund, swap dealer, and arbitrageur activity in the crude oil market. The contribution of our work is to examine the role of institutional traders in switching between high-volatility and low-volatility regimes. Using confidential position data on institutional investors, we first analyze the linkages between trader positions and fundamentals. We find that these institutional position changes reflect fundamental economic factors. Subsequently, we adopt a Markov regime-switching model with time-varying probabilities and find that institutional position changes contribute incrementally to the probability of regime changes.

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.

Do human capital and institutional quality contribute to Brazil's long term real convergence/divergence process? A Markov regime-switching autoregressive approach

Abstract This paper assesses Brazil's real convergence (1822–2019) through unit root tests and Markov Regime-Switching (MS) models in three different scenarios: towards (i) other six Latin American countries (LA6); (ii) Portugal; and (iii) the technological frontier country, the US. The extended unit root test results favour Brazil's very long-run real convergence towards LA6 and Portugal, but not the US. The estimated MS models, involving two different regimes, real convergence and real non-convergence/divergence, capture institutional quality's positive effect in promoting Brazil's real convergence.

Interest rate risk of Chinese commercial banks based on the GARCH-EVT model

Interest rate market risk faced by China’s commercial banks is increasing after the announcement that the interest rate marketisation is completed. This paper examines the Value-at-Risk, and statistical properties in the daily price return of Shanghai banks’ overnight offered rate. The study applies two-stage approaches, combining GARCH-type models with extreme value theory. Firstly, the Markov regime switching model is used to test the regime states of the series. Secondly, the performance of different VaR models are examined. Results show that the extreme value approach estimates better at the 99% confidence level. The EGARCH-GED model is the most suitable of the employed GARCH-type models. The back-testing results support the idea that the approach used in this study is appropriate for improving commercial banks’ daily risk management. This paper applies the GARCH-EVT method for interest rate measurement after China’s interest rate marketisation and added regime analysis of interest rate. Suggested policy implications will help formulate policies that guide the activities of commercial banks in China.

부산 경남지역의 지역내총생산 동조성에 관한 연구

This study examined the synchronicity of economic activities between Busan and Gyeongnam (including Ulsan) using per capita regional gross domestic product data from Busan and Gyeongnam from 1985 to 2020. Synchronization of economic activities is a phenomenon in which major macroeconomic variables move similarly at the same time or over time, and can be analyzed through cointegration analysis and Granger causality analysis. First, we examined whether a long-term equilibrium relationship exists between the regional gross domestic product of Busan and Gyeongnam through a cointegration test considering structural changes. In addition, we examined the mutual predictive power between regional GDP growth rates through the Markov regime-switching model. As a result, a structural change occurred in the long-run stable relationship was found. In the case of the cointegration test method reflecting the structural change, it was found that there is a long-run stable relationship between GRDP of Busan and Gyeongnam. In the Markov phase-switching model, GRDP of the two regions had synchrony in the long run regardless of the economic phase, and there was synchronicity with high mutual predictive power between the rates of change in the GRDP of Busan and Gyeongnam. The synchronicity of economic activities in Busan and Gyeongnam is adequately explained by the method considering the structural change and the nonlinear model.

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.