Markov Regime-switching Model Research Articles

Can Cryptocurrencies Be a Future Safe Haven for Investors? A Case Study of Bitcoin

Purpose: This study attempts to determine whether or not Bitcoin prices are affected by real economic activity, financial markets, and foreign exchange markets; and whether Bitcoin has the potential to be a safe haven for investors. Background: Cryptocurrencies in general, and Bitcoin in particular, have generated a huge interest from both practitioners and academicians as an alternative mechanism for electronic payment systems. While some argue that “Bitcoin works in practice, but not in theory”, others argue that Bitcoin’s stability depends on unknown variables in unknown combination, and thus it is difficult if not impossible to model precisely, thereby raising doubts about the soundness of the system. The research community should assume the task of precisely identifying the factors driving Bitcoin prices, and develop some theoretical foundation to help understand how Bitcoin will be affected when the legal environment and practices change. Significance of the Study: The extensive discussion of Bitcoin on blogs and in the mainstream financial media has primarily focused on the cryptocurrency’s technical, legal and safety issues. Even the limited academic research on the subject has focused on the technical and safety issues, leaving the question of financial and economic aspects largely untouched. The present study thus seeks to determine the extent to which Bitcoin is affected, if at all, by changes in economic growth, financial markets, and forex markets. The paper also seeks to determine whether Bitcoin behaves differently from forex markets and financial markets and if it has a hedging or diversification potential? Design/methodology/approach: A Markov regime-switching regression model is employed in this study to determine the relationship between real economic activity (proxied by the Baltic Dry Index), financial markets (proxied by the Dow Jones Industrial Average Index), and foreign exchange markets (proxied by the USD-Euro & USD-Yen exchange rates). Findings: The results indicate that, unlike USD-Euro and USD-Yen exchange rates, Bitcoin exhibits significantly different behavior in terms of its association with other financial and economic variables. While equity markets or forex markets are significantly related to each other in varying levels in both the bullish and the bearish regimes, real economic activity appears to be unrelated to either of them. Bitcoin meanwhile, is affected neither by real economic activity, nor equity markets, nor forex markets in either bullish and the bearish regimes. Indeed, it differs significantly in its behavior from forex markets. Furthermore, the results indicate that Bitcoin tends largely to exhibit a bullish trend and the probability of transition to a bearish trend is very small. Thus, it can be concluded that Bitcoin may offer potential for both hedging and diversification to varying degrees. Originality/value: Although the technical, legal and safety issues in cryptocurrencies have been widely discussed, financial and economic aspects remain largely unexplored. The current study thus makes a modest contribution to the sparse literature on the subject.

Policy uncertainty and Bitcoin returns

Bitcoin is the digital currency of the digital economy. This article is an attempt to reveal the effects of policy uncertainty on Bitcoin returns with economic policy uncertainty (EPU) in the US, the UK, Japan, China, and Hong Kong. Furthermore, we also present the results of monetary policy uncertainty (MPU) on the Bitcoin market. The robust estimations from the quantile regression and Markov regime-switching model show that Bitcoin returns are affected by EPU. One of the essential findings is that Bitcoin returns are more responsive to EPU in the US, China, and Japan. In the US and Japan, uncertainty has a negative effect on the Bitcoin market whereas in China it has a positive effect. Global MPU uncertainty is also significant in explaining Bitcoin exchange rates. Moreover, the Bitcoin market is negatively affected by uncertainty in Federal Open Market Committee (FOMC), the gross domestic product, and other macroeconomic data. Uncertainty in the equity market and Bitcoin returns are negatively associated.

Do global sentiment shocks spillover towards emerging and frontier markets?

PurposeThis study aims to investigate the impact of sentiment shocks based on US investor sentiments, bearish and bullish market conditions. Earlier studies, though very few, only consider the effect of investor sentiments on stock returns of emerging frontier Asian (EFA) markets.Design/methodology/approachThis study uses the application of regime switching model because of its capability to explore time-varying causality across different regimes unlike traditional linear models. The Markov regime switching model uses regime switching probabilities for capturing the potential asymmetries or non-linearity in a model, in this study’s case, thereby adjusting investor sentiments shocks to stock market returns.FindingsThe results of the Markov regime switching method suggests that US sentiment, bullish and bearish market shocks act as a main contributors for inducing variation in EFA stock market returns. The study’s non-parametric robustness results highlight an asymmetric relationship across the mean series, whereas a symmetric relationship across variance series. The study also reports Thailand as the most sensitive market to global sentiment shocks.Research limitations/implicationsThe sensitivity of the EFA markets to these global sentiment shocks highlights their sensitivity and implications for investors relying merely on returns correlation and spillover. These findings also suggest that spillover from developed to emerging and frontier equity markets only in the form of returns following traditional linear models may not be appropriate.Practical implicationsThis paper supports the behavioral aspect of investors and resultant spillover from developed market sentiments to emerging and frontier market returns across international equity markets offering more rational justification for an irrational behavior.Originality/valueThe study’s motivation to use the application of regime switching models is because of its capability to explore time-varying causality across different regimes unlike traditional linear models. The Markov regime switching model uses regime switching probabilities for capturing the potential asymmetries or non-linearity in a model, in the study’s case, thereby adjusting investor sentiments shocks to stock market returns. It is also useful of the adjustment attributable to exogenous events.

The Impact of Fiscal Policy on Aggregate Economic Activity: A Regime Dependent Impulse Response Analysis

Asymmetries in fiscal policies cannot be captured by linear time series models. In order to examine the asymmetry responses of output in different phases of the business cycle, Markov Regime Switching (MRS) model is an alternative technique that is used to achieve the objective. The main objective of this study is to empirically explore the effects of fiscal shocks (spending and taxes) on Pakistan’s overall economic activity GDP while utilizing Markov Switching MS-VAR model. The model is characterized to allow for the variation in mean, coefficients and in error variances. The study results show that the effect of shocks and the size of multiplier varies across regimes confirming the asymmetric behavior of fiscal policy transmission mechanism. Moreover, the impact of positive spending shock has a stronger effect on output in the recession as compared to boom. One surprising result of the study is that the tax shock increases the output both in recession and boom. Lastly, spending and revenue behave a-cyclically.
 JEL Classification Codes: C11; C32; E62

Optimal quantile hedging under Markov regime switching

In this study, we introduce a new quantile hedging method by extending the conventional quantile hedging with two-state Markov regime switching models. Using daily data from 16 futures markets, we discover that the conventional quantile hedge ratio displays an inverted U shape to various extents for different futures. When looking into high- and low-volatility states, quantile hedge ratios show different results compared with conventional models. While the quantile hedge ratio in low-volatility state is relatively flat, in high-volatility state, the quantile hedge varies with the spot return distribution and displays a U-type relationship. Moreover, the U shape is more prominent for agricultural futures and less prominent for others. Also, by comparing hedging effectiveness, the quantile hedge strategy is found to be more effective than the no-hedge strategy and the hedging strategy derived from error correction models.

Optimal mean-variance reinsurance and investment strategy with constraints in a non-Markovian regime-switching model

This paper is devoted to study the proportional reinsurance/new business and investment problem under the mean-variance criterion in a continuous-time setting. The strategies are constrained in the non-negative cone and all coefficients in the model except the interest rate are stochastic processes adapted the filtration generated by a Markov chain. With the help of a backward stochastic differential equation driven by the Markov chain, we obtain the optimal strategy and optimal cost explicitly under this non-Markovian regime-switching model. The cases with one risky asset and Markov regime-switching model are considered as special cases.

Forecasting crude oil price volatility via a HM-EGARCH model

This paper compares uni-regime GARCH-type models, GARCH-type models with Markov and hidden Markov (HM) switching regimes on their forecasting abilities in WTI and Daqing crude oil markets, respectively. Empirical results indicate a HM-EGARCH model outperforms the competitive models, namely the regular GARCH-type models and Markov regime-switching models as well as the other models with hidden Markov regimes through results of six loss functions and the superior predictive ability (SPA) test. More significantly, we find the HM-EGARCH not only performs well in developed crude oil markets, but also in emerging crude oil markets. Therefore, the HM-EGARCH model can be regard as an effective measure of volatility when accounting for different volatility states in the time-changing process.

Dinámica y desempeño de los fondos de pensión en México (1997-2018): un análisis de volatilidad condicional con cambios estructurales

El objetivo es analizar la volatilidad condicional de los rendimientos del sistema de pensiones, la dinámica de la volatilidad y los cambios estructurales durante el periodo (julio/1997-abril/2018). La metodología corresponde a modelos GARCH (1,1), se estimó un modelo autorregresivo con cambio de régimen markoviano MS-AR, distinguiendo entre dos regímenes de volatilidad. La contribución consiste en ofrecer un análisis de la volatilidad del sistema de pensiones, mediante la detección de los cambios de régimen. Se concluyó que los rendimientos se encuentran en dos regímenes, alta y baja volatilidad, con aproximadamente la misma probabilidad.

A segmented generalized Markov regime-switching model with its application in financial time series data

ABSTRACTMovements of equity indices are very important information for an investment decision. Empirical studies illustrate that the movements switch among different regimes. The Markov regime-switching model has important applications to such analysis. However, parameters estimated under normality assumption might not be stable and the corresponding change-point detection algorithm might face some challenges when either the error distribution is heavy-tailed or observed data contain outliers. In this paper, we relax the normality assumption and propose a generalized Markov regime-switching (GMRS) model. We propose a GMRS model based change-point detection algorithm, which is tested on both simulation data and Hang Seng monthly index. Simulation studies show that this algorithm can improve the accuracy of identifying change-points when either the error distribution is heavy-tailed or observed data contain outliers. It is also evident that the identified change-points on Hang Seng monthly index data match the observed market behaviours.

Equilibrium investment and risk control for an insurer with non-Markovian regime-switching and no-shorting constraints

In this paper, we study the time-consistent equilibrium investment and risk control policies for an insurer under the mean-variance criterion. The dynamics of liability and assets are given by non-Markovian regime-switching models driven by a Brownian motion and a continuous time finite-state Markov chain. It is assumed that the insurer has a time-varying preference of risk depending dynamically on current wealth, and is not allowed to short sell the risky assets. With the aid of backward stochastic differential equations and bounded mean oscillation martingales, we obtain feedback representations of the open-loop equilibrium strategies. Finally, the result is also applied to solve an example of the Markovian regime-switching model.

Does globalization matter for environmental sustainability? Empirical investigation for Turkey by Markov regime switching models

Many efforts have been made to theoretically explain and/or empirically investigate how globalization plays a role on environmental quality. However, as in theoretical explanations, empirical literature as well has not reached a consensus yet to enlighten possible effects in developing countries especially. On the one hand, Globalization is expected to contribute to decrease environmental degradation through the technique effect that underlines the role of obtaining clean technologies. On the other hand, this expectation may become reversed since globalization triggers economic activities through the scale effect and movements of dirty industries to developing countries. This paper focuses on how environmental sustainability reacts to globalization in Turkey. To this end, ecological footprint as a proxy for environmental sustainability and KOF globalization measurements are analyzed by using Markov regime switching models. Estimations indicate that growth in financial globalization, politic globalization, trade globalization, human capital, and capital stock reduce the ecological footprint growth of Turkey. Results reveal as well that interpersonal globalization growth diminishes ecological footprint growth. All three regime-switching models yield that growths of economic globalization and social globalization result in an increase in ecological footprint growth in Turkey.

Analyzing Oil Price Shocks and Exchange Rates Movements in Korea using Markov Regime-Switching Models

Korea imports all of its crude oil, and is the world's fifth largest oil importing country. We analyze the effects of oil prices, interest rates, consumer price indexes (CPIs), and industrial production indexes (IPIs) on the regime shift behavior of the Korean exchange rates against the USA from January 1991 to March 2019. We use the Markov regime switching model (MRSM) to detect the regime shift behavior of the movements of Korean exchange rates. In order to select the optimal MRSM, we fit a total of 30 models considering four explanatory variables. The selected model based on Akaike information criteria (AIC) and maximum log likelihood (MLL) includes the log-differentials of oil prices, the log-differentials of CPIs compared to those of the US, and its own auto-regressive terms. Based on the selected MRSM model, throughout all markets, we find evidence to support the existence of two distinct regimes: a stable regime with low-volatility, and an unstable regime with high-volatility. The regime with high-volatility includes the Asian financial crisis of 1997 and the global financial crisis of 2008–2009 in the Korean exchange rates market. In the regime with low-volatility, the Korean exchange rates are not significantly influenced by any of the explanatory variables, except for its own auto-regressive terms. In the regime with high-volatility, the Korean exchange rates are significantly influenced by the CPIs and oil prices. The transition probability from the regime with low-volatility to the regime with high-volatility is about ten times that of the opposite case.

Integrating Solvency and Liquidity Stress Tests

The paper presents a framework to integrate liquidity and solvency stress tests. An empirical study based on European bond trading data finds that asset sales haircuts depend on the total amount of assets sold and general liquidity conditions in the market. To account for variations in market liquidity, the study uses Markov regime-switching models and links haircuts with market volatility and the amount of securities sold by banks. The framework is accompanied by a Matlab program and an Excel-based tool, which allow the calculations to be replicated for any type of traded security and to be used for liquidity and solvency stress testing.

Indonesian Financial Crisis Prediction Using Combined Volatility and Markov Regime Switching Model Based on Indicators of Real Interest Rate on Deposits and Lending Interest Rate/Deposit Interest Rate

The financial crisis that occurred in mid-1997 made it aware of the importance of building an early detection system because the crisis had a severe impact on the Indonesian economy. The crisis occurred due to several macroeconomic indicators experiencing very high fluctuations and the existence of changes in the structure (regime). The combined volatility and Markov regime switching models are very suitable to explain the crisis. Indicators of real interest rates on deposits and lending interest rate/deposit interest rates from 1990 to 2018 were used to build the combined model. The result showed that the MRS-ARCH (2,1) model for real interest rate on deposits indicator and the MRS-GARCH (3,1,1) model for lending interest rate/deposit interest rate indicator could explain the crisis. Markov regime switching predictions indicate that in 2019 there are no signs of a financial crisis in Indonesia.

The risk spillovers from the Chinese stock market to major East Asian stock markets: A MSGARCH-EVT-copula approach

This paper studies the risk spillovers of the Chinese stock market to major East Asian stock markets during turbulent and clam periods. We employ the Markov regime-switching model, the extreme value theory (EVT) and the vine copula function to model their multivariate dependence structures and compute the corresponding conditional Value-at-risk (CoVaR) in direct and indirect ways. In the case of the direct CoVaR, we find some interesting results that downside and upside spillovers are significantly different between the turbulent and calm periods, except for the China-Japan and the China-South Korea for the turbulent period. The evidence on the indirect results indicates the differences between the turbulent and calm periods do exist. The other results indicate the spillovers measured ignore the special nature of the different periods when the whole sample is used to model the dependence structure among the stock markets.

Can the VAR model outperform MRS model for asset allocation in commodity market under different risk preferences of investors?

It is universally acknowledged that both linear Vector Autoregressive (VAR) models and nonlinear econometric frameworks, such as Markov Regime Switching (MRS) model, can produce appropriate portfolio allocations that hedge against the bullish and bearish dynamics in commodity markets. Then, whether the linear model can outperform the nonlinear model is worth exploring for modeling when we focus on the asset allocations in commodity markets. Therefore, this paper studies whether simple VAR models can produce commodity portfolios whose performance is superior or similar to that obtained under MRS model, and also discusses the impact of investors' different risk preferences on commodity portfolio performance. The empirical results indicate that, first, VAR models can produce portfolios with better performance than MRS model in the case of a long sample. Second, an increasing risk aversion of investors may significantly reduce the portfolio performance. Among them, investors who prefer profit-making to risk-resisting and who pay same attention to returns and risks are more likely to obtain portfolios with higher returns and lower risks. Finally, WTI is an appropriate investment target for investors who only focus on the maximization of returns, while for investors with other risk preferences, gold proves an ideal investment target.

Prediction of currency crisis in Indonesia using combined volatility and Markov regime switching models based on lending interest rate / deposit interest rate indicator

In mid of 1997, a currency crisis had a severe impact on the economy in Indonesia. Based on this situation, it is necessary to build an early warning system to anticipate the crisis events. The crisis occurred due to some macro-economic indicators fluctuated very high and the change of regime. Combined volatility and Markov regime switching models suited to explain the crisis. In this article it was used indicator of lending interest rate / deposit interest rate from 1990 to 2018. The results showed that MRS-GARCH (3,1,1) model can explain the crisis. Based on this model, it can be predicted that in 2019 there was no sign of a currency crisis in Indonesia.

OIL PRICE AND FIJIAN TOURISM CYCLE: A MARKOV REGIME-SWITCHING MODEL

The fluctuation of oil price tends to have adverse effect on the tourism industry of a nation. This paper investigates the dynamic changes of the inbound tourism market for Fiji and the driving forces of the Fijian tourism cycle. A set of fundamental determinants of tourism demand including international crude oil price has been utilized to predict the Fijian tourism cycle for the period of 2000-2017. The Markov regime-switching model identifies two distinct phases of the Fijian tourism cycle which are an expansion and a recession period. The filtered and smoothed probabilities signalled the Fijian tourism development significantly with the transition probabilities supported. The adequate dating evaluation can offer essential information for policymakers, tourism industry players and even the community in decision making for Fijian tourism to enhance the nation's development. Keywords: Markov-switching model, Fijian tourism cycle, Oil price fluctuation, ForecastingJEL Classifications: Z32; O01; E32DOI: https://doi.org/10.32479/ijeep.8087

Performance analysis of Islamic and conventional portfolios: The emerging markets case

This paper compares the performance of Islamic, conventional, and mixed (Islamic-conventional) optimal international portfolios from the viewpoint of an American investor across tranquil and crisis regimes. The dataset consists of closing prices on MSCI Islamic stock indices and their conventional counterparts in the US and 15 emerging markets in three regions (Latin America, Europe, and Asia) over the period from June 2002 to February 2017. The methodology is based on the Markov regime-switching model and the Ledoit and Wolf (2008) Sharpe ratios difference test. Generally, the results show a difference in performance between conventional, Islamic, and mixed portfolios but it is not statistically significant. Hence, investors will not be worse off by choosing Islamic indices rather than conventional ones.

Sustainability of Public Debt:An Analytical Framework

Sustainability is a term that has been used with increasing frequency in the academic literature and recent multilateral policy discussions. Taking note of the crises in different countries heading for a debt trap, it is necessary to look at the right combination of fiscal parameters and growth to achieve sustainability and stable development. Fiscal prudence involves exercise of good judgment, common sense and even caution in the conduct of fiscal policies, especially on the expenditure front. This paper deals with finding: (a) debt-GDP threshold which will support to find out at what threshold level there is nonlinear relationship between debt and GDP, (b) sustainability indicators which gives the overall depiction of the fiscal side which guides for fiscal prudence policies, and (c) Markov regime switching model which explains the relationship of debt and growth. The period of study is from 1980 to 2016 and it is with respect to the central government.