Markov Regime-switching Model Research Articles

Effect of Market-Wide Investor Sentiment on South African Government Bond Indices of Varying Maturities under Changing Market Conditions

The excess levels of investor participation coupled with irrational behaviour in the South African bond market causes excess volatility, which in turn exposes investors to losses. Consequently, the study aims to examine the effect of market-wide investor sentiment on government bond index returns of varying maturities under changing market conditions. This study constructs a new market-wide investor sentiment index for South Africa and uses the two-state Markov regime-switching model for the sample period 2007/03 to 2024/01. The findings illustrate that the effect investor sentiment has on government bond indices returns of varying maturities is regime-specific and time-varying. For instance, the 1–3-year government index return and the over-12-year government bond index were negatively affected by investor sentiment in a bull market condition and not in a bear market condition. Moreover, the bullish market condition prevailed among the returns of selected government bond indices of varying maturities. The findings suggest that the government bond market is adaptive, as proposed by AMH, and contains alternating efficiencies. The study contributes to the emerging market literature, which is limited. That being said, it uses market-wide investor sentiment as a tool to make pronunciations on asset selection, portfolio formulation, and portfolio diversification, which assists in limiting investor losses. Moreover, the findings of the study contribute to settling the debate surrounding the efficiency of bond markets and the effect between market-wide sentiment and bond index returns in South Africa. That being said, it is nonlinear, which is a better modelled using nonlinear models and alternates with market conditions, making the government bond market adaptive.

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

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

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

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

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

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

Does Regime-Dependent Volatility Drive Dynamism in Investor Herding?

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

Energy transition dynamics amid policy uncertainty, environmental regulations, and Geopolitics: Evidence from China

The time-varying dynamics of the transition to renewable energy reflect the effectiveness and efficiency of energy policy. This paper investigates renewable energy transition behavior in China using Markov regime-switching models. Two distinct phases, expansion and contraction, are identified based on the growth rates of renewable energy consumption share. We find that since China started its new-energy national strategy in 2005, there has been an extended period in which it has been in the expansion state, although it occasionally reverts to a contraction state. Intriguingly, we uncover that the timing of transition state changes can be explained by the changes in policy-related, market-based instruments and risk-related indexes. These findings present implications for policymakers in developing more precise and effective policies, that drives investment choices to align with the goal of a sustainable energy system.

Can geopolitical risks impact the long-run correlation between crude oil and clean energy markets? Evidence from a regime-switching analysis

The increasingly volatile and complex geopolitical landscape has impacted the global energy market more frequently, posing serious challenges to the global energy low-carbon transition strategy. However, the heterogeneity of the impact of geopolitical risks (GPR) on the dynamic correlation between oil and clean energy across various market conditions and sub-industries of clean energy remains distinct. To address this gap, this paper combines a dynamic conditional correlation with the mixed data sampling (DCC-MIDAS) model and the Markov regime-switching model to investigate the influence of GPR on the correlation between oil and clean energy, particularly in diverse market conditions. Empirical findings reveal that the impacts of GPR on the long-run correlations of oil-clean energy vary across different market regimes, both in terms of the overall market and industry sectors. Specifically, GPR negatively affects the oil-clean energy correlation with stronger effects in the low-correlation regime. In particular, GPR plays a positive effect on the oil-biofuel nexus, however, the impact of GPR on the oil-fuel cells nexus exhibits opposite directions in high- and low-correlation regimes. Moreover, the transmission of geopolitical risks to the energy markets is predominantly driven by threats (GPT) during periods of low volatility and correlation, while acts (GPA) play a more significant role during periods of high volatility and correlation. These results emphasize the significance for both investors and policymakers in considering the time-varying impact of geopolitical risks across various market conditions and individual clean energy sub-sectors.

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

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

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.

아파트 매매가격, 전세가격, 월세가격 변동성 결정요인

In this study, the financial time series trend is predicted using Hamilton's Markov regime-switching model to observe the factors of apartment sales, jeonse, and monthly rent prices that change from time to time from a Markov perspective. Inflation since the coronavirus pandemic has pushed the United States interest rate higher, and South Korea is also raising its benchmark interest rate to stabilize the economy. Due to the impact of this increase in the base rate, the mortgage interest rate has also risen, which has a chain effect on the sale price of apartments, the price of jeonse, and the price of monthly rent. Changes in the trend over the long term can be interpreted as exogenous shocks, economic measures, or structural changes. As a result of the Markov model analysis, apartment sales prices, jeonse prices, and monthly rent prices all showed a higher probability of maintaining a recession period, and among them, the recession period for apartment sales price, and jeonse prices was relatively more likely to last longer. In the case of the expected duration period, the duration of the depression phase of apartment sales prices and jeonse prices was similar, while the duration of the depression phase of apartment monthly rent prices was short and highly volatile. As a cause of volatility, loan interest rates were found to be a determinant.

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.

Impact of Socioeconomic Globalization and Institutional Quality on Human Capital Development in Nepal: A Markov Regime Switching Analysis

Human capital development is a catalyst for the economic growth of a nation. This study employed the Markov regime-switching model to examine the impact of socioeconomic globalization and institutional quality on human capital development in Nepal, using data from 1980 to 2018. The findings revealed that social globalization promotes human capital development in Nepal, whereas institutional quality impedes it. Additionally, economic globalization either negatively impacted or did not significantly contribute to human capital development in both regimes. Financial development was found to severely hamper human capital development, while inflation had a positive effect in both regimes. The results offer insights that the quality of institutions within the globalized context of Nepal is crucial for enhancing human capital development. This improvement could stimulate private-sector financing and alleviate the crowding-out effects of government spending and policy shifts. Consequently, the policy implication of this research is that Nepal should prioritize enhancing institutional quality and fostering social globalization while addressing the adverse effects of economic globalization and financial development to bolster human capital development.

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.