Markov Regime-switching Approach Research Articles

A Markov regime switching approach to estimating the volatility of Johannesburg Stock Exchange (JSE) returns

The study used the Markov regime switching model to investigate the presence of regimes in the volatility dynamics of the returns of JSE All-Share Index (ALSI). Volatility regimes are as a result of sudden changes in the underlying economy generating the market returns. In all, twelve candidate models were fitted to the data. Estimates from the regime switching model were compared to the industry standard non-switching GARCH (1,1) using the Deviance Information Criteria (DIC). The results show that the two-regime switching EGARCH model with skewed Student t innovations describes better the return of the JSE Index. Additionally, we backtest the model results in order to confirm our findings that the two-regime switching EGARCH is the best of the models for the sample period.

A Markov Regime Switching Approach towards Assessing Resilience of Romanian Collective Investment Undertakings

This paper explores the sensitivity of Romanian collective investment undertakings’ returns to changes in equity, fixed income and foreign exchange market returns. We use a sample of 80 open-end investment funds and pension funds with daily returns between 2016 and 2018. Our methodology consists of measuring changes in the daily conditional volatility for the fund returns (EGARCH) and changes in their conditional correlation with selected market risk factors (DCC MV-GARCH) throughout different volatility regimes identified using a Markov Regime Switching model. We argue that, on average, the level of conditional correlations between funds and market risk factors remained stable and unconcerned by the volatility regimes. In addition, for only less than half of the funds in the sample, their volatility regimes were synchronized with those of the selected market risk factors. We found that, on average, fund returns are more correlated with equity returns and less correlated with changes in local bond yields, while not being significantly influenced by changes in foreign bond yields or changes in foreign exchange. During the period investigated equity returns were the most volatile while the funds returns volatility were, on average, much more reduced. Overall, our results show the resilience of the Romanian collective investment sector to the selected market risk factors, during the investigated period.

Regime-dependent linkages between securitized real estate market and major financial markets: some international evidence

ABSTRACTWe employ Markov regime-switching approach to explore the regime-dependent linkages between securitized real estate market and stock, money, bond and foreign exchange markets for 10 economies. During high-volatility periods in securitized property markets, stock market return, and to some extent, changes to foreign exchange market rates impose stronger and positive impact on securitized real estate market returns. Moreover, stock and bond market risks are linked negatively to securitized real estate market risk. Although the asymmetrical regime-dependent influences of the financial market performance indicators on the SRE market do vary across the 10 economies studied in terms of direction and significance, the results highlight the risk of increased exposure of securitized property markets to financial markets during high-volatility market conditions which the investors and policymakers should be alerted to.

Regime-Switching Determinants for Spreads of Emerging Markets Sovereign Credit Default Swaps

Using the Markov regime switching approach, we investigate the dependency of short term sovereign credit default swap (SCDS) spread changes on a nation’s country-specific fundamental factors, local, regional and macroeconomic global factors. We find that the significance of the determinants of SCDS spread changes differ across the two states of our regime-switching model. Specifically, in the good state, the weekly SCDS spread changes are mainly determined by local, regional and fundamental factors; whereas global variables have a stronger influence in the bad regime. In particular, US market returns play a dominant role in influencing the SCDS spread change in the bad state suggesting loss aversion and flight–to–quality behavior of investors. We then examine the cross-sectional differences of the above regime switching effect based on country-specific characters and find that the regime switching effect is associated with a nation’s country-specific characters such as openness, economic size and so forth.

Supply and demand driven oil price changes and their non-linear impact on precious metal returns: A Markov regime switching approach

This paper examines the nonlinear effect of oil price shocks on precious metal returns using Markov regime switching regression. We use Ready's (2018) approach to decompose oil price changes into supply, demand, and risk driven shocks. Results indicate a significant positive impact of demand and supply shocks and a negative impact of risk shocks on precious metal returns. Although we find evidence of switching between low and high volatility regimes, we do not find strong regime effect on supply or demand shocks' contemporaneous relationship with precious metal returns. However, risk shocks' influence on precious metal returns is strongly regime dependent. These results generally hold for different distributional specification of error terms.

Dynamic Linkages between Stock Market and Exchange Rate in mila Countries: A Markov Regime Switching Approach (2003-2016)

This paper aims to analyse the dynamic relationship between the stock market returns and exchange rates movements for the mila (Mercado Integrado Latinoamericano) countries: Colombia, Chile, Mexico and Peru, over the period 01:2003 to 09:2016. Univariate (Markov Switching-Autoregressive) and multivariate (Markov Switching-Vector Autoregressive) regime-switching models approach are used. The univariate analysis offers evidence indicating that stock returns of the mila countries evolve according to two different regimes: a low volatility regime and a high volatility regime. The Markov Switching Vector Autoregressive models point out that stock markets have more influence on exchange rate than exchange rate has on stock markets. Results for the Peruvian and Chilean markets contribute evidence about contagion between the stock and the exchange rate markets. JEL Code: G15; C58; F31; D53

Modeling dynamics of short-term international capital flows in China: A Markov regime switching approach

In this paper, we analyze the dynamics of short-term international capital flows in China using time-varying transition probability Markov switching models. We provide empirical evidence that exchange rates may prove to be useful information variables for detecting the states of inflow or outflow. Moreover, the short-term international capital of “currency arbitrage” has high speculations. In addition, the results show that inflows and outflows last about 25 months and 4 months, respectively, and after 2007, inflows dominate the dynamics of short-term international capital.

An analysis of diversification benefits of commodity futures using Markov regime-switching approach

This study investigates the hedge and safe haven properties of individual commodity futures against stock market movements using a nonlinear regime-switching framework. Based on the results of Brock, Dechert and Scheinkman (BDS) test and information selection criterion, Markov-switching vector auto-regression (MS-VAR) model is applied with three regimes for gold and silver futures and with two regimes for crude oil, copper and zinc futures. The results demonstrate strong hedge and weak safe haven property of gold and silver futures, while it shows a weak hedge and weak safe haven potential of copper and zinc futures. Conversely, crude oil futures cannot be used as a safe haven against extreme stock market movements. In addition, portfolio analysis confirms that these findings provide significant information to investors for the construction of better risk-adjusted return portfolio.

Modeling and Simulation of a Four-Rotor Rake Loading for Predicting Accumulated Fatigue Damage: A Markov Regime-Switching Approach

Abstract. The prediction of agricultural machinery’s fatigue life is of increasing importance for machine developers who must produce durable and reliable machines for a globalized market with different local operating conditions. Mathematical tools that can model and simulate the variable loading of agricultural machines are necessary for fatigue life prediction. Modeling should be based on measured loads from real-world operations. In this article, the loads of a four-rotor rake were recorded during grass swathing. Markov chains were used to model the transitions between the machine’s operating conditions (in-field swathing and headland turning) and the sequences of turning points present in the load signals. The Markov transition probabilities were trained using the recoded data and then fatigue life was predicted via executing 10,000 Monte Carlo simulations based on the trained Markov models. The differences between the accumulated fatigue damage predicted from the simulations and from the measured data had mean value and standard deviation equal to -22% and 12.8%, respectively. Evaluation of the trained model on new data (not present in the training dataset) that were recorded during swathing on a different grass field resulted in fatigue damage difference with mean and standard deviation equal to 34% and 7.5%, respectively. The fatigue damage difference was in a reasonable region considering how fatigue life is affected by high-amplitude individual cycles. Keywords: Accumulated damage, Agricultural machinery, Durability, Fatigue life.

An analysis of diversification benefits of commodity futures using Markov regime-switching approach

An analysis of diversification benefits of commodity futures using Markov regime-switching approach

Asymmetric effects of oil price shocks on stock returns: evidence from a two-stage Markov regime-switching approach

ABSTRACTThe asymmetric effects of oil price shocks on stock returns have attracted the attention of many researchers in the past several decades. Most of these researchers’ studies, however, do not separate out the sources of oil price shocks when examining the asymmetric effects. In this article, we address this limitation using a two-stage Markov regime-switching approach. Our results indicate that oil supply and demand shocks have a null or minimal impact on stock returns in a low-volatility regime and a statistically significant impact in a high-volatility regime. We observe that oil demand shocks affect stock returns significantly more than oil supply shocks. A positive aggregate demand shock significantly increases stock returns, whereas a positive oil-specific demand shock markedly decreases stock returns. These results have important implications for policymakers and investors.

A regime-switching approach to estimating the nonlinear quantity-based monetary policy rule in China

ABSTRACTThis article focuses on the quantity-based monetary policy rule in China. The article applies a Markov regime-switching approach to estimate the nonlinear policy rule using quarterly data from 1997Q1 to 2015Q2. Overall, the performance of the estimated two-state rule is significantly better than the performance of the linear rule. The regime-switching estimation suggests that the quantity-based policy tool displays a countercyclical response to the inflation gap but a pro-cyclical response to the output gap during recessions. In addition, this article provides notable evidence of the fact that China’s central bank has been targeting inflation over the gradual course of financial liberalization.

Measuring Hedge Fund Performance: A Markov Regime-Switching with False Discoveries Approach

We propose a Markov regime-switching approach accounting for false discoveries in order to measure hedge fund performance. It enables us to extract information from both time-series and cross-sectional dimensions of panels of individual hedge fund returns in order to distinguish between skilled, unskilled and zero-alpha funds for a given state of the economy. Applying our approach to individual hedge funds belonging to the Long/Short Equity Hedge strategy, we find that their performance cannot be explained by luck alone, and that the proportion of zero-alpha funds in the population decreases when accounting for alpha regime dependence. However, the proportion of truly skilled funds is higher during expansion periods, while unskilled funds tend to be more numerous during recession periods. Moreover, sorting on regime dependent alphas instead of unconditional alphas improves investors' ability to select funds that outperform their benchmarks in both regimes of the economy by maximizing the performance persistence effect of top performer fund portfolios.

Public information arrival and stock return volatility: Evidence from news sentiment and Markov Regime-Switching Approach

Using computational linguistic analysis of intraday firm-level news releases, this study models the relation between public information flows and stock volatility under different regimes. We analyze how the hourly return volatility of S&P100 stocks from 2000 to 2010 are linked to the various linguistics-based sentiment scores of the news releases, which are obtained from the RavenPack News Analytics Database. Results from the Markov Regime-Switching GARCH (MRS-GARCH) model indicate that firm-specific news sentiment is more significant in quantifying intraday volatility persistence in the calm (low-volatility) state than the turbulent (high-volatility) state. Furthermore, the impact of news sentiment differs across industries and firm size.

Uncertainty Effects of Inflation on Output: A MRS-IV Approach

In this paper, we propose an analytical framework to explore the level and volatility effects of inflation on the output gap. Using quarterly US data over 1977:q2-2009:q4, we then examine the empirical implications of the model by implementing an instrumental variables Markov regime switching approach. We show that inflation uncertainty has a negative and regime dependent impact on the output gap but the level of inflation does not have any such effect. Our empirical investigation also provides evidence that the US economy is moving towards a period of turmoil before the recent financial crisis was imminent. The results are robust to the use of alternative measures of inflation uncertainty. Keywords: Output gap; inflation uncertainty; Markov-switching modeling; instrumental variables; endogeneity. JEL classification: E31, E32

An evaluation of departure throughputs before and after the implementation of wake vortex recategorization at Atlanta Hartsfield/Jackson International Airport: A Markov regime-switching approach

This paper utilizes a Markov regime-switching model to decompose airport departures into two regimes and to investigate the change in departure throughputs before and after implementing wake recat at ATL. Although analysts may not always know with certainty which regime prevails and how long it may last, they can compute the transition probabilities and expected duration of each regime. After the implementation, there was a 91% chance that departure throughputs would remain unconstrained (up from 86% before implementation) and a 37% chance that departure throughputs would become constrained (up from 35% before implementation).

The non-linear impact of high and growing government external debt on economic growth: A Markov Regime-switching approach

Today, the major reason for external debt is to finance high public deficits. This study aims to examine the relationship between external indebtedness and growth variables. In this context, Markov-switching model is used because it allows the examination of unobservable variables in an observable model and provides steady algorithm to achieve robust optimization by iterations in a dynamic system, and is more flexible than prior models. This paper concentrates on the analysis of Turkey and utilizes the data set for the period of 1974 to 2009. Throughout the analyses, the relationship between growth and external borrowing is examined in terms of public and private external borrowing. Paper yields that, according to results of multivariate dynamic Markov-switching model, the main growth variables such as investment and human capital have positive impact on growth as expected. Findings can be summarized as follows; firstly, public and/or private external borrowing has negative impact on growth both in regime at zero and regime at one. Secondly, the negative impact of public borrowing on economic growth and development is higher than that of private borrowing on economic growth and development. Eventually, the conclusion reveals that the economic development and borrowing variables do not follow a linear path.

Public Information Arrival and Stock Return Volatility: Evidence from News Sentiment and Markov Regime-Switching Approach

Using computational linguistic analysis of intraday firm-level news releases, this study models the relationship between public information flows and stock volatility under different regimes. We analyze how the hourly return volatility of S&P100 stocks from 2000-2010 are linked to the various linguistics-based sentiment scores of the news releases, which are obtained from the RavenPack News Analytics Database. Results from the Markov Regime-Switching GARCH (MRS-GARCH) model indicate that firm-specific news sentiment is more significant in quantifying intraday volatility persistence in the calm (low-volatility) state than the turbulent (high-turbulent) state. Furthermore, the impact of news sentiment differs across industries and firm size.

The determinants of FDI in Turkey: A Markov Regime-Switching approach

This paper considers movements of Foreign Direct Investments (FDI) in Turkey, and therefore, to understand the dynamics of FDI, runs several nonlinear FDI equations in which the basic determinants of FDI in Turkey are determined through Markov Regime-Switching Models (MSMs). The statistical properties of Markov Regime-Switching time series models are more desirable than those of conventional time series or panel regression models. Through these properties of MSMs, i) one can observe structural changes, if they exist, in FDI equations through time, ii) if, in fact, the true FDI regression equation follows a nonlinear relationship, MSMs fit data better than the linear models. This paper eventually follows maximum likelihood methodology of Markov Regime-Switching Model (MSM) to search for the possible structural changes in level and/or trends and possible changes in parameters of independent variables of FDI–MSM equations through the transition probabilities. In conclusion, this paper yields the outcome that Turkish FDI growth equation has significant structural changes in level and trend and that has significant coefficient shifts in explanatory variables. These explanatory variables are Turkish GDP Growth, Labor Cost, the Electricity Price Growth, the growth in average prices of High Sulphur Fuel Oil, Cooking Coal, Steam Coal and Natural Gas, Export Growth, Import Growth, Discount Rate and Country Risk Indexes for Turkey, US and EU, respectively, within the time interval from 1988 first quarter to 2010 second quarter.

On the Equity and Interest Rate Predictability for Balanced Portfolios and Coverage Ratios for Pension Plans

The purpose of this study is twofold. On one hand we derive an optimal allocation framework between stocks and bonds, with and without a momentum factor, taking into account a regime-switching model based on monthly equity and interest rate data since 1870. On the other, we investigate the optimal portfolio construction for the manager of a pension plan, where the pension’s liabilities are valued in present value terms. Assuming such a pension plan is exposed to two risk factors only, an equity factor and an interest rate factor, the manager needs to decide on the exposure to equities (the beta to equities measured at the level of the total portfolio), and the level of exposure to interest rates, relative to the benchmark defined by the liabilities. Using over 140 years of monthly data pertaining to US equities and 10-year yields, I show that combining a momentum factor with a Markov regime switching approach provides a very robust framework with optimal allocations to stocks and bonds in each regime for the traditional manager, and a well defined equities (beta) exposure and interest rate hedge policy for the manager of the pension plan.