Markov-switching Method Research Articles

Dynamic Asymmetric Effect of Currency Risk Pricing of Exchange Rate on Equity Markets: A Regime-Switching Based C-Vine Copulas Method

This paper investigates whether currency risk is priced differently in the different sectors (industrial, financial, and basic materials) of equity markets in a sample of developed United States of America (USA) and developing economies (Brazil, India, Poland, and South Africa). The paper makes use of the following techniques: (i) Univariate Autoregressive Fractionally Integrated Moving Average and Exponential General Autoregressive Conditional Heteroskedastic (ARFIMA-EGARCH), (ii) the Markov Switching method (MS), and (iii) the Canonical Vine Copulas (C-Vine) techniques. Using a sample of daily data made of the foreign exchange rate against the domestic currency and equity market sectors; our findings show that there is an asymmetry effect between equity markets and the foreign exchange rate: there is a heterogeneous, strong, and positive dependence between the two. Higher equity prices are associated with depreciation of local currencies, according to US dollar (USD) exchange rates. In addition, we find that the selected emerging economies are pricing a positive and considerable currency risk. The pricing of currency risk has a varied effect in both regimes representing the states of the economy. In fact, when currency risk pricing has a beneficial impact on certain sectors of the economy, investors predict better returns.

Analysis of the formation of the price bubble in the financial market: with an emphasis on the price bubble in the insurance industry and the stock market with the Markov-switching approach

This article investigates the formation of price bubbles in the insurance industry index in the Tehran Stock Exchange with the total price index, considering the importance of the price trend and the formation of bubbles in the stock market in order to get a better understanding of the price trend of the insurance industry compared to the total index of Tehran Stock Exchange. The time period studied in this article is from April 2017 to April 2020. The method used here is the Markov switching method. Also, the basis of this study to identify the price bubble is two-regime state-space model of Wu (1995) and Campbell and Shiller (1988), which considers bubble formation in one state and bubble burst in the other. The results show that the trend of price bubble formation in the insurance industry in Tehran Stock Exchange and the total price index of the stock exchange are different and fluctuations in the insurance industry index have been more than fluctuations in the total stock index. Also, based on the results, the number of bubble formation trends related to the insurance industry index is about 26 times and the total index is 19 times. In addition, the bubbles that occur are less compatible with each other, so that studies show that in the period of 2017 to 2018, the total index did not face the formation of bubbles, but the insurance industry index has experienced about 12 bubble formation processes in the same period

Public Debt Composition, Debt Policy Rules and Growth in Selected SADC Countries

Purpose: This study examined the relative effect of debt composition and debt reduction policy rule on economic growth in selected SADC countries which are Mauritius, Tanzania and Zimbabwe. Design/Methodology/Approach The Markov-switching method was used to estimate the debt growth model for the period 1990Q1-2016Q4
 Findings:. The effects of debt proved to be regime dependent which supports the time effects of debt in all countries. High external debt relative to domestic debt had positive effect on growth in Tanzania which is a good reforming country and had negative effects in the case of Zimbabwe which is a debt distressed country. In comparison to Mauritius, a domestic debt dependent country, high domestic debt relative to external debt had negative impact on growth. The effects tend to rise with market pressure and government consumption behaviour. A negative real effect of debt reduction policy rule was confirmed for Zimbabwe and irrelevance in countries with less threat of debt distress. 
 Implications/Originality/Value Therefore the study found support to the quantity-effect rather than type-effect of debt on growth. We recommended that countries should consider both time and quantity effects of debt in debt management; adopt explicit debt reduction rules which constrain fiscal behaviour and force policy commitment towards debt stabilization.

A Hybrid Forecasting Model for Nonstationary and Nonlinear Time Series in the Stochastic Process of CO2 Emission Trading Price Fluctuation

Predicting CO2 emission prices is an important and challenging task for policy makers and market participants, as carbon prices follow a stochastic process of complex time series with nonstationary and nonlinear characteristics. Existing literature has focused on highly precise point forecasting, but it cannot correctly solve the uncertainties related to carbon price datasets in most cases. This study aims to develop a hybrid forecasting model to estimate in advance the maximum or minimum loss in the stochastic process of CO2 emission trading price fluctuation. This model can granulate raw data into fuzzy-information granular components with minimum (Low), average (R), and maximum (Up) values as changing space-description parameters. Furthermore, it can forecast carbon prices’ changing space with Low, R, and Up as inputs to support a vector regression. This method’s feasibility and effectiveness is examined using empirical experiments on European Union allowances’ spot and futures prices under the European Union’s Emissions Trading Scheme. The proposed FIG-SVM model exhibits fewer errors and superior performance than ARIMA, ARFIMA, and Markov-switching methods. This study provides several important implications for investors and risk managers involved in trading carbon financial products.

Do the Markov Switching-based Hybrid Models Perform Better in Forecasting Exchange Rates?

ABSTRACT In this study, we extend the traditional monetary model and the random walk model with Markov-switching method and propose two new forecasting models called the Markov switching monetary model (MSMM) and Markov switching random walk model (MSRW). Then, we evaluate the forecasting ability of these two new mixed models, MSMM and MSRW, and compare their performance with the traditional pure monetary model and pure Random walk model based on Mean Squared Forecast Error and Mean Absolute Forecast Error. The results show that the two hybrid models significantly improve the forecasting ability compared with the two traditional models in most scenarios. Moreover, we reexamine the role of data frequency in determining the number of regimes and in affecting the accuracy of forecast evaluation with different data frequency.

Elasticidade-renda das importações e Lei de Thirlwall: uma aplicação do Método Markov-Switching para o Brasil (1995-2016)

Este artigo propôs-se a analisar o comportamento da elasticidade renda das importações brasileiras, no período entre 1995 e 2016, e se existem evidências de não-linearidade no que se refere à elasticidade. A elasticidade calculada permitiu testar a validade da Lei de Thirlwall, isto é, se o crescimento econômico do Brasil foi restrito pelo balanço de pagamentos. Para estimar a elasticidade foi o utilizado o método de Markov-Switching que permitiu a segmentação dos parâmetros estimados em regimes. Foram definidos dois regimes, para o primeiro a elasticidade foi de 4,78 com duração esperada de 1,3 trimestres, já para o segundo regime, obteve-se como resultado elasticidade igual a 0,62 e duração de 3,4 trimestres. Baseado nessas elasticidades confirmou-se a hipótese subjacente à Lei de Thirlwall de que o crescimento econômico brasileiro foi restrito pelo balanço de pagamentos.

IDENTIFICATION AND PREDICTION OF CURRENCY CRISIS: MARKOV SWITCHING-BASED APPROACH

This paper investigates ways of identifying and predicting currency crises in world-wide markets, with special focus on 1997 and 2008 currency crises. A novel Markov switching method is proposed for identifying currency crisis based on two states model, the turmoil state and tranquil state, which is the most suitable model considering the balance between model performance and computational demand. Compared with previous Markov switching currency crisis studies, the contribution of this paper comes from several ways. First, the dependent variable is different. While other papers use the exchange rate directly or the estimation of devaluation probability, this study uses the market pressure index calculated from nominal exchange rate and foreign reserves. Secondly, we allow different volatilities in different states, whereas other papers assume the same volatility in two states. Thirdly, our transition probabilities are constant rather than time-varying. The model shows evidence of state switching before crisis in many different currency markets. Lastly, we compare the Markov switching method with the widely used probit model which proposed an early warning system in terms of forecasting performance, and the empirical results show that the novel Markov switching method performs better than the probit model.

Can Markov Switching Based Hybrid Models Improve the Performance of Forecasting Exchange Rates?

This paper extends the traditional monetary model and the random walk model with Markov-switching method and proposes two new forecasting models called Markov switching monetary model (MSMM) and Markov switching random walk model (MSRW). Compared with the traditional models, the evidence shows the two hybrid models perform better on forecasting the exchange rate.

Economic Growth and Formal Sector Employment

Economic data for the last decade indicates a failure of the South African economy to generate sufficient jobs to absorb the unemployed. With economic growth also underperforming, the question becomes whether the lack of employment growth is due to a lack of economic growth. The objective of this article is to establish the relationship between economic growth and formal sector employment growth (not enough time-series data exist to include the informal sector). Using a Markov-switching method to distinguish economic up- and downswings the article's second objective is to explore whether or not there is a business cycle-related relationship between economic growth and formal sector employment growth.

Estimation and Solution of Models with Expectations and Structural Changes

SummaryIn this paper, we develop solutions for linearized models with forward‐looking expectations and structural changes under a variety of assumptions regarding agents' beliefs about those structural changes. For each solution, we show how its associated likelihood function can be constructed by using a ‘backward–forward’ algorithm. We illustrate the techniques with two examples. The first considers an inflationary program in which beliefs about the inflation target evolve differently from the inflation target itself, and the second applies the techniques to estimate a new Keynesian model through the Volcker disinflation. We compare our methodology with the alternative in which structural change is captured by switching between regimes via a Markov switching process. We show that our method can produce accurate results much faster than the Markov switching method as well as being easily adapted to handle beliefs departing from reality. Copyright © 2016 John Wiley & Sons, Ltd.

A comparison of economic indicator analysis and Markov switching methods concerning the cycle phase dynamics

This paper compares the dating of growth rate cycles obtained from a Markov switching approach with the reference chronologies based on Economic Indicator Analysis (EIA) given by the Economic Cycle Research Institute (ECRI), focusing on a set of developed and emerging economies. The developed countries include US, UK, Germany and Japan, which are compared with an emerging economy, India. Using a univariate Markov regime switching model we characterise growth rate cycle phenomena for these countries by identifying turning points and distinct economic regimes, employing data on the growth rate of the coincident index given by ECRI.

International Investor Sentiment and Emerging Equity Markets in Central and Eastern Europe

This paper uses the vector Markov switching method of Hamilton (1990) to measure market sentiment in a group of countries. We investigate the apparent co-movement of equity returns in the Czech Republic, Hungary and Poland. We argue that the main underlying forces moving stock returns in small open emerging markets are of an exogenous nature. The main factor driving prices in the region is modeled as an unobservable variable labeled "international investor sentiment". This latent variable is represented as a two-state Markov chain and makes stock returns switch from a growth regime to a depression regime, or in the opposite direction. In such a framework, the stock return process comes from a mixture of two multivariate normal distributions. The estimated latent variable shows significant correlation with a number of data series on global capital flows, mutual fund flows, regional emerging and developed markets' equity returns as well as with other popular market sentiment or economic uncertainty indicators. It does not show a strong association with a comprehensive set of contemporaneous local economic factors with the exception of the quarterly change in industrial production.

The structure and sustainability of current account deficit: Turkish evidence from regime switching

ABSTRACTThis study focuses on the current account deficit dynamics and sustainability, using data of the period between 1990:Q1 and 2014:Q2 in the context of Turkish economy. The main findings of the study can be put into two categories. The first category covers energy consumption, openness rate, gross domestic product, exchange rate and investments, which are the most important determinants of the current deficit. The second one asserts that sustainability is weak for Turkish economy; however, it is even weaker during the economic contraction. There is extensive literature about structure and sustainability of the current account deficit. However, many of the studies have analysed the sustainability of the current account deficits without considering the economic conjuncture. For this purpose, the study employs the Markov-switching method which is a non-linear time series model.

Will the oil price change damage the stock market in a bull market? A re-examination of their conditional relationships

This paper applies Markov-switching method to identify bear and bull market regimes and adopts interactive double-dummy variable approach to re-investigate the conditional relationship between the real oil price return and the international real stock return in 15 OECD countries when the sample is split into bear markets and bull markets. The empirical results indicate that, once the stock index is in the bull trend, an increase in oil price cannot affect the real stock return, while a decrease in oil price can lead to higher stock returns. On the contrary, if the stock market is in the bear era, the oil price growth cannot significantly affect the stock returns. Remarkably, oil price shocks cannot always damage the broad stock index, especially in a bull market era. Furthermore, regardless of the oil price shock, long-term investors need not adopt any policy and strategy to reduce the impact of the oil price on the stock market because the effect of a bull stock market will weaken the negative effect of an oil price shock. On the other hand, regardless of oil price shocks, when the stock market exhibits a bear trend, investors should adopt coping policies and strategies to avoid the impact of other non-oil factors shock, such as declines in real GDP to the stock market in the 15 selected countries. Clearly, regardless of whether the stock market exhibits a bear trend or a bull trend, the stock market trend will surpass the effect of an oil price shock.

Bayesian Markov Regime-Switching Models for Cointegration

This paper introduces a Bayesian Markov regime-switching model that allows the cointegration relationship between two time series to be switched on and off over time. Unlike classical approaches for testing and modeling cointegration, the Bayesian Markov switching method allows for estimation of the regime-specific model parameters via Markov Chain Monte Carlo and generates more reliable estimation. Inference of regime switching also provides important information for further analysis and decision making.

Evaluating and Comparing Leading and Coincident Economic Indicators

This article evaluates which economic indicators are the most useful for signaling recessions. The article uses a modified Markov switching method to compare the timing of recession signals across many indicators. In its present form, it is difficult to use the Markov switching methods for comparing recession signals across indicators. First, the regimes in the Markov switching method do not necessarily align with recession periods. Second, the definitions of the two regimes are likely to be different across indicators. However, if some modifications are made to the Markov switching method, the method can be helpful for comparing recession signals across indicators. This article shows that by converting Markov switching probabilities into percentiles, the Markov switching method can be useful in comparing the quality of recession signals across indicators. Using the method, hundreds of indicators are ranked based on their leading ability during different sample periods. Finally, the performance of the indicators during the current recession is evaluated.

Does congress realign or smoothly adjust? A discrete switching model of congressional partisan regimes

Studies of development and change in partisan fortunes in the US emphasize epochs of partisan stability, separated by critical events or turning points. In this paper we study partisan electoral changes in the US Congress using the method of Markov switching. Our estimates are based on election changes from 1854, roughly the date of the establishment of the modern incarnation of the two-party system, to the present. For the Senate, we estimate partisan balance both from 1856 and from 1914, the period of direct elections. Our discrete-state method performs better than one based on smooth cycles, and is more consistent with the existing theory. We use the Markov switching method to estimate an underlying unobserved state parameter, ‘partisan regime’. The method allows the direct estimation of critical transition points between Republican and Democratic partisan coalitions. Republican regimes characterized House elections during three periods: 1860 through 1872, 1894 through 1906, and 1918 through 1928. Senate results were roughly similar. Every recession caused a realignment prior to 1932, but since then none has. For the Senate, the two-state model does not fit adequately. We estimate a three-state model in which a Republican regime dominated from 1914 through 1928; a Democratic regime characterized the period 1930–1934, and a Democratic-leaning regime characterized the period 1938 to the present (1936 is a transition year). The driver for historical realignments up to 1930 seems to have been economic distress, at least for the House. Each recession before 1930 is associated with a realignment, but after 1930, no recession produced a realignment. Since then, however, economic downturns have not led to realignments. We speculate that better economic management since World War II, resulting in shallower recessions, is the cause. For the Senate, however, we detect only one fundamental regime shift: that during the Great Depression.

How volatile are East Asian stocks during high volatility periods?

This study reports estimates of the magnitude of volatility during abnormal times relative to normal periods for seven East Asian economies using a rudimentary univariate Markov-switching ARCH method. The results show that global and regional events such as the 1990 Gulf War and the 1997 Asian currency crisis led to high volatility episodes whose magnitude relative to normal times differ from country to country. Country-specific events such as the opening up of country borders in the mid-1990s are also observed to lead to high volatility periods. Additional insights are obtained when volatility is assumed to evolve according to a three-state Markov regime switching process.

Monetary-fiscal Mix and Inflation Performance: Evidence from the U.S.

There has been a lot of interest recently in developing small scale rule-based empirical macro models for the analysis of monetary policy. These models, based on the conventional view that inflation stabilization should be a concern of monetary policy only, have typically neglected the role of fiscal policy. We start with the evidence that a baseline VAR-augmented Taylor rule can deliver recurrent mispredictions of inflation in the U.S. before 1987. We then show that a fiscal feed-back rule, in which the primary deficit reacts to both the output gap and the government debt, can well characterize the behavior of fiscal policy throughout the sample. However, by employing Markov-switching methods, we find evidence of substantial instability across fiscal regimes. Yet this precisely happens QTR{it}{before 1987}. We then augment the monetary VAR with a fiscal policy rule and control for the endogenous regime switches for both rules. We find that only over time windows belonging to the pre-1987 period the model based on the two rules can predict the behavior of inflation better than the one based just on the monetary policy rule. QTR{it}{After 1987}, when fiscal policy is estimated to switch to a regime of fiscal discipline, the monetary-fiscal mix can be appropriately described as a regime of monetary dominance. Over this period a monetary policy rule based model is always a better predictor of the inflation behavior than the one comprising both a monetary and a fiscal rule.

What Drives Equity Returns in Central and Eastern Europe

This paper investigates apparent comovement of equity prices in Czech Republic, Hungary and Poland. It argues that main underlying forces moving stock prices in small open emerging markets are of exogenous nature. It models main factor driving prices in region as an unobservable variable labeled the international investor sentiment. This latent variable is represented as a two-state Markov chain and makes stock returns switch from a high growth regime to a depression regime, or in opposite direction. In such a framework, stock return process comes from a mixture of two normal distributions with different means. This model is estimated using vector Markov switching method of Hamilton (1989, 1990). The estimated process shows a significant correlation with a number of data series on capital flows as well as with emerging markets' benchmark indices, corroborating main argument of paper.