Markov Switching Model Research Articles

Optimal Trend-Following in a Markov Switching Model

Optimal Trend-Following in a Markov Switching Model

Fiscal Sustainability, Fiscal Debt and Economic Growth in the South Asian Region

The issue of fiscal sustainability in South Asia is paramount since the tide of economic liberalization in the 1980s and given its ballooning public debt. Using annual data for 1960–2019, we perform a battery of tests to determine fiscal sustainability in Sri Lanka, India, and Pakistan. We adopt the Bohn (1998) model by estimating a fiscal reaction function (FRF). The model is derived from the theoretical foundation that shows a sufficient condition for satisfying the intertemporal government budget constraint whereby the primary balance reacts positively to the lagged debt-to-GDP ratio. A positive response to the primary balance suggests that the government is counteracting the increase in fiscal debt. The results show evidence of fiscal sustainability for the model in all three countries. We relax the assumption of a constant-parameter linear fiscal rule and allow nonlinearity in the FRF by (1) incorporating the Markov-switching fiscal rule into the FRF and (2) estimating a time-varying parameter model of the FRF. The Markov-switching model identifies two fiscal regimes described by periods of sustainable and unsustainable fiscal policies. The model also yields transition probabilities and the expected duration of the regimes, which we use to evaluate the No-Ponzi game condition. The results show that while this condition is satisfied, the long-term debt-stabilizing condition fails to hold. The time-varying parameter model of the FRF shows different episodes when the primary balance reacts positively or negatively to the lagged debt-to-GDP ratio, corroborating the results of the Markov-switching model. We then examine the impact of fiscal debt on growth in these regimes using a threshold regression model. For fiscal debt above some threshold, that is 78 percent of the GDP in Sri Lanka, 67 percent of GDP in India and 58 percent in Pakistan, economic growth in these countries tends to decline. Our results suggest that while fiscal stance in these economies is sustainable, the governments need to curtail the growing fiscal debt below the optimal threshold level to promote economic growth.

Forecasting National Recessions of the United States with State-Level Climate Risks: Evidence from Model Averaging in Markov-Switching Models

Forecasting National Recessions of the United States with State-Level Climate Risks: Evidence from Model Averaging in Markov-Switching Models

Forecasting the instability of Polish banks

The paper presents a formalised procedure of the identification of financial stability EWI (Early Warning Indicators) for the Polish banking sector, in which a two-step procedure was applied. First, the author used a logit model to estimate of the biggest Polish banks’ probabilities of default (PDs). Next, the calculated individual banks’ PDs were used to prepare aggregated domestic banking system stability. In the last step, employing a set of multivariate Markov-switching models with distributed lags (MMSM-DL), the author applied this measure to identify EWI from the candidate macro, private and public debt, banking sector, financial markets and property prices indicators. The best performing EWI were selected with application of area under the receiver operating characteristic (AUROC) metrics and compared with an output of a popular logistic regression (LR) model. To the best author’s knowledge, this article presents for the first time a fully formalised analytical framework based on the MMSM-DL approach that combines microprudential and macroprudential data for the Polish banks financial stability EWI identification. Moreover, the survey supports the hypothesis that the Polish banking sector is stable with use of a formalised econometric procedure.

Testing an Algorithm with Asymmetric Markov-Switching GARCH Models in US Stock Trading

In the present paper, we extend the current literature in algorithmic trading with Markov-switching models with generalized autoregressive conditional heteroskedastic (MS-GARCH) models. We performed this by using asymmetric log-likelihood functions (LLF) and variance models. From 2 January 2004 to 19 March 2021, we simulated 36 institutional investor’s portfolios. These used homogenous (either symmetric or asymmetric) Gaussian, Student’s t-distribution, or generalized error distribution (GED) and (symmetric or asymmetric) GARCH variance models. By including the impact of stock trading fees and taxes, we found that an institutional investor could outperform the S&P 500 stock index (SP500) if they used the suggested trading algorithm with symmetric homogeneous GED LLF and an asymmetric E-GARCH variance model. The trading algorithm had a simple rule, that is, to invest in the SP500 if the forecast probability of being in a calm or normal regime at t + 1 is higher than 50%. With this configuration in the MS-GARCH model, the simulated portfolios achieved a 324.43% accumulated return, of which the algorithm generated 168.48%. Our results contribute to the discussion on using MS-GARCH models in algorithmic trading with a combination of either symmetric or asymmetric pdfs and variance models.

Forecasting oil and gold volatilities with sentiment indicators under structural breaks

This paper contributes to the literature on forecasting the realized volatility of oil and gold by (i) utilizing the Infinite Hidden Markov (IHM) switching model within the Heterogeneous Autoregressive (HAR) framework to accommodate structural breaks in the data and (ii) incorporating, for the first time in the literature, various sentiment indicators that proxy for the speculative and hedging tendencies of investors in these markets as predictors in the forecasting models. We show that accounting for structural breaks and incorporating sentiment-related indicators in the forecasting model does not only improve the out-of-sample forecasting performance of volatility models but also has significant economic implications, offering improved risk-adjusted returns for investors, particularly for short-term and mid-term forecasts. We also find evidence of significant cross-market information spilling over across the oil, gold, and stock markets that also contributes to the predictability of short-term market fluctuations due to sentiment-related factors. The results highlight the predictive role of investor sentiment-related factors in improving the forecast accuracy of volatility dynamics in commodities with the potential to also yield economic gains for investors in these markets.

The Impact of Tax Revenues on Economic Growth in Business Cycles of the Iranian Economy: An Application of the Markov Switching Model

The Impact of Tax Revenues on Economic Growth in Business Cycles of the Iranian Economy: An Application of the Markov Switching Model

Early Detection of South Korean Financial Crisis using MS-GARCH Based on Term of Trade Indicator

<p><strong>A</strong><strong>bstract</strong><strong>.</strong> The 1997 Asian financial crisis, which occurred until 1998, had a significant impact on the economies of Asian countries, including South Korea. The crisis brought down the South Korean currency quickly and sent the economy into sudden decline. Because the impact of the financial crisis was severe and sudden, South Korean requires a system which able to sight crisis signals, therefore that, the crisis will be fended off. One in all the indicators that can detect the financial crisis signals is that the term of trade indicator which has high fluctuation and change in the exchange rate regime. The mixture of Markov Switching and volatility models, Generalized Autoregressive Conditional Heteroscedasticity (GARCH), or MS-GARCH could explain the crisis. The MS-GARCH model was built using data from the South Korean term of trade indicator during January 1990 until March 2020. The findings obtained in this research can be inferred that the best model of the term of trade is MS-GARCH (2,1,1). Term of trade indicator on that model could explain the Asian monetary crisis in 1997 and also the global monetary crisis in 2008. The smoothed probability of term of trade indicators predicts in April till December 2020 period, there will be no signs of the monetary crisis in South Korea.</p><p><strong>Keywords</strong><strong>: </strong>financial crisis, MS-GARCH, South Korea, term of trade indicator</p>

Using GAM functions and Markov-Switching models in an evaluation framework to assess countries\u2019 performance in controlling the COVID-19 pandemic

BackgroundThe COVID-19 pandemic has initiated several initiatives to better understand its behavior, and some projects are monitoring its evolution across countries, which naturally leads to comparisons made by those using the data. However, most “at a glance” comparisons may be misleading because the curve that should explain the evolution of COVID-19 is different across countries, as a result of the underlying geopolitical or socio-economic characteristics. Therefore, this paper contributes to the scientific endeavour by creating a new evaluation framework to help stakeholders adequately monitor and assess the evolution of COVID-19 in countries, considering the occurrence of spikes, "secondary waves" and structural breaks in the time series.MethodsGeneralized Additive Models were used to model cumulative and daily curves for confirmed cases and deaths. The Root Relative Squared Error and the Percentage Deviance Explained measured how well the models fit the data. A local min-max function was used to identify all local maxima in the fitted values. The pure Markov-Switching and the family of Markov-Switching GARCH models were used to identify structural breaks in the COVID-19 time series. Finally, a quadrants system to identify countries that are more/less efficient in the short/long term in controlling the spread of the virus and the number of deaths was developed. Such methods were applied in the time series of 189 countries, collected from the Centre for Systems Science and Engineering at Johns Hopkins University.ResultsOur methodology proves more effective in explaining the evolution of COVID-19 than growth functions worldwide, in addition to standardizing the entire estimation process in a single type of function. Besides, it highlights several inflection points and regime-switching moments, as a consequence of people’s diminished commitment to fighting the pandemic. Although Europe is the most developed continent in the world, it is home to most countries with an upward trend and considered inefficient, for confirmed cases and deaths.ConclusionsThe new outcomes presented in this research will allow key stakeholders to check whether or not public policies and interventions in the fight against COVID-19 are having an effect, easily identifying examples of best practices and promote such policies more widely around the world.

Markov-Switching Vector Autoregressive (MS-VAR) Modelling (Mean Adjusted): Application to Macroeconomic data

Researcher every so often model economic relationship using linear models. Even when non-linear models are used, they hardly consider the possibility of regimes, the transmission from one regime to another and also the duration of stay in a particular regime. These are not captured by linear models. To address this, we applied the Markov-Switching Mean Vector Autoregressive Model to model and estimate the interdependence between macroeconomic variables (International Trade and Macroeconomic Stability) within the context of the Nigerian economy. Specifically, the study analyzed the trends of total export, total import, exchange rate, and inflation rate in Nigeria within the study period, Estimated MSM-VAR models on the study variables and selected the best model using information criteria. The study empirically investigated the interdependence existing among the study variables, determined the probabilities of transition from one regime to another and the duration of stay in a regime. The study ascertained the relative significance of each random innovation in affecting the study variables. Monthly time series data 246 months spanning through January 2000 to June 2020 was collected form the Central Bank of Nigeria Statistical Bulletin. The study used the Markov-Switching Mean Vector Autoression in the Analysis. The results showed that all variables were stationary at first difference. The study chose 2 regimes and the model selection criteria selected [MS(2)-MVAR(2)]. The variables were largely self-explanatory and very strongly exogenous.

Pipeline capacity and the dynamics of Alberta crude oil price spreads

AbstractWe investigate the effect of binding pipeline capacity on price spreads between crude oil in Alberta, Canada, and competing crude oils in export markets at the other end of the pipeline. Our goal is to determine whether these price spreads are consistent with capacity‐constrained spatial arbitrage models by estimating Markov switching models of the price spreads for both heavy and light crude oil. Our results for heavy crude oil agree with the theory: we find one regime with both a low mean and a low variance, consistent with sufficient pipeline capacity, and another regime with both a higher mean and a higher variance, consistent with constrained pipeline capacity. The difference in mean spreads between regimes then provides an estimate of the mean shadow price of capacity. Price spreads for light crude oil do not display significant differences in mean spreads across regimes, suggesting that pipeline capacity constraints are less important for this commodity. Comparing our results with pipeline capacity data, we find that tight capacity is not always associated with large price spreads.

The nexus of renewable energy equity and agricultural commodities in the United States: Evidence of regime-switching and price bubbles

The opportunity cost of producing efficient energy from renewable energy sources especially from agricultural products amid increasing threat of food insecurity has remained policymakers’ nightmare. On this note, this study employed the regime inference (the Markov switching model) to examine the response of renewable energy equity relative to prices of corn, soybean and wheat for the United States over the period 20/01/2012 -2/08/2018. Additionally, the SADF (Supremum Augmented Dickey Fuller) test is further employed to investigate the evidence of speculative bubbles in the prices of the concern commodities. With a significant evidence of regime switching, the study reveals positive impacts of soybean and wheat on the renewable energy equity in both regimes while the impact is negative in the regimes for corn prices. The positive impact of soybean is an indication that the share of renewable energy and share of its export is highest while corn is being recently preferred and consumed as stable food rather than a source of renewable energy. Furthermore, a sparing evidence of explosive process and collapse bubbles is observed in all the examined commodities except for soybeans. Moreover, with the frequency domain Granger causality approach, the results show overwhelming evidence of bidirectional Granger causality especially between renewable energy equity and the agricultural commodities at varying frequencies. Thus, the study offers effective policy frameworks through the lens of renewable energy development and agriculture for the United States and for other similar economies.

The impact of the North American shale gas technology on the US’ energy security: the case of natural gas

ABSTRACT Energy security is sensitive to the behavioural characteristics of resource trade, especially for the US as the world’s largest energy-consuming economy. This paper applies the Markov switching model and dynamic network connectedness measures of Diebold-Yilmaz to explore the impact of the North American shale gas technology on behavioural regimes and spillover effects of natural gas trade cycles, regarding the US’ energy security. The findings indicate asymmetric and time-varying behaviour of the US’ energy security pre- and post-the revolution. Specifically, it seems that overall interaction between substitution- and scale effect of the shale gas revolution through the gravity theory develops the US’ energy security as shocks occur in the energy-trading process. Therefore, the alternating analysis of behavioural regimes and spillover effects, through potential and actual trade links in world natural gas trade networks, is essential to develop availability, affordability, accessibility as well as acceptability dimensions of energy security.

Prosperity or Real Estate Bubble? Exuberance Probability Index of Real Housing Prices in Chile

In this paper, we approached the concept of real estate bubble, analyzing the risk its bursting could generate for the Chilean financial market. Specifically, we analyzed the relationship between real housing prices, the economic activity index, and mortgage interest rates denominated in inflation-linked units from 1994 to 2020. The analysis was based on a second order Markov switching model with the predetermined variables mentioned later, whose parameters were obtained through the expectation–maximization algorithm. Then, we built a probability index as early warning indicator for potential imbalances in the real estate price that could put financial market stability at risk. The indicator is important to evaluate economic policy calibrations in time. A main finding was that the real housing price had a non-linear relationship with economic activity and the mortgage interest rate. Therefore, the evolution of the real estate price has been consistent with fundamental macroeconomic variables, even under a high growth regime, with increases above 12% per year. About 92% of housing price variability derived from changing macrofinancial conditions, suggesting a low margin of speculative behavior.

Measuring the evolution of competition and the impact of the financial reform in the Mexican banking sector, 2008–2019

This paper analyzes the monthly evolution of bank competition in Mexico from 2008 to 2019 using different measures. Subsequently, we analyze whether the 2014 financial reform had an effect on some of our competition measures. We use ordinary and quantile regression techniques and Markov switching models to identify changes in regimes. We find partial empirical evidence supporting the idea that the reform had a positive average effect and increased banks’ competition intensity during a few years. However, we also document heterogeneity as some large banks benefited from an increase in their market power. We perform several robustness tests and report that our measures lead to values that are congruent and similar to those available in the literature. The main policy lesson of our research is that regulators could benefit from the monitoring of competition evolution using a finer time frequency.

Hidden Markov Models of Evidence Accumulation in Speeded Decision Tasks

Speeded decision tasks are usually modeled within the evidence accumulation framework, enabling inferences on latent cognitive parameters, and capturing dependencies between the observed response times and accuracy. An example is the speed-accuracy trade-off, where people sacrifice speed for accuracy (or vice versa). Different views on this phenomenon lead to the idea that participants may not be able to control this trade-off on a continuum, but rather switch between distinct states (Dutilh et al., Cognitive Science 35(2):211–250, 2010). Hidden Markov models are used to account for switching between distinct states. However, combining evidence accumulation models with a hidden Markov structure is a challenging problem, as evidence accumulation models typically come with identification and computational issues that make them challenging on their own. Thus, an integration of hidden Markov models with evidence accumulation models has still remained elusive, even though such models would allow researchers to capture potential dependencies between response times and accuracy within the states, while concomitantly capturing different behavioral modes during cognitive processing. This article presents a model that uses an evidence accumulation model as part of a hidden Markov structure. This model is considered as a proof of principle that evidence accumulation models can be combined with Markov switching models. As such, the article considers a very simple case of a simplified Linear Ballistic Accumulation. An extensive simulation study was conducted to validate the model’s implementation according to principles of robust Bayesian workflow. Example reanalysis of data from Dutilh et al. (Cognitive Science 35(2):211–250, 2010) demonstrates the application of the new model. The article concludes with limitations and future extensions or alternatives to the model and its application.

Endogenous health groups and heterogeneous dynamics of the elderly

SummaryWe propose a methodology to classify individuals into few but meaningful health groups by estimating a panel Markov switching model that exploits rich information from panel household surveys. Using the HRS, we identify four persistent health groups, depending on individual's physical and mental disabilities. Our classification outperforms existing health measures at explaining entry in nursing homes, home health care, out‐of‐pocket medical expenses, and mortality for individuals in the HRS, ELSA, and SHARE. Through a workhorse model of savings, we recover an asset cost of bad health that is twice as big as when using self‐reported health.

Measuring multi‐volatility states of financial markets based on multifractal clustering model

AbstractMeasuring multi‐volatility states is an important component of financial risk management. In this paper, taking 17 years' high‐frequency data of the Shanghai Stock Exchange Composite Index (SSEC) as an example, we set out to propose a novel model of multifractal clustering model (MCM), combining multifractal algorithm with k‐mean clustering algorithm, to measure the multi‐volatility states of financial markets. The empirical results present that the financial markets are multifractal and the multifractal parameter Sα measured by multifractal algorithm correlates well with the performance of financial markets. Meanwhile, the multi‐volatility states recognized by k‐mean clustering algorithm based on Sα have obvious statistical significance. More importantly, the experiment results show that by the loss functions and Diebold–Mariano (DM) test, generalized autoregressive conditional heteroscedasticity (GARCH) model with the multi‐volatility state measured by MCM is superior to others measured by Markov switching (MRS) model and hidden Markov model (HMM) as well as that without the multi‐volatility states, which indicates that MCM has best performance of measuring the multi‐volatility states of financial markets. The robustness of MCM is also evaluated by Shenzhen Securities Component Index (SZSE) instance set, SSEC instance sets with different time intervals, and different volatility models. The results still prove that MCM has strong robustness on measuring the multi‐volatility states of financial markets.

UNIVARIATE MODELLING AND FORECASTING OF ENERGY DEMAND

A thorough assessment of energy sources and long-term energy demand predictions are crucial to the development of sustainable energy planning and policy for any country. However, these are uncommon practices in Nigeria, thereby resulting in shortage of energy supplies required by the economy and its growing population. In this study, energy demand in the commercial and public services, industrial, and residential sectors of the Nigerian economy is examined using Autoregressive Conditional Heteroskedasticity, Markov Switching, and Unobserved Component models. The results of the comparative study show that Markov Switching model performs better than Autoregressive Conditional Heteroskedasticity and Unobserved Component Model for energy demand forecasting. The outcomes further suggest that the energy demand in Nigeria will grow at an average annual rate of less than 2% from 2018 to 2050 in all the three sectors investigated. In view of these findings, the Nigerian Government should have made effort to collaborate with relevant stakeholders, aiming at harnessing the abundant energy resources needed to meet the future energy consumption with a guarantee of energy security and sustainability in the country.

Modelling exchange-driven fish price dynamics

PurposeThis paper aims to put forward and compare two accessible approaches to model and forecast spot prices in the fishing industry. The first modelling approach is a Markov-switching model (MSM) in which a Markov chain captures different economic regimes and a stochastic convenience yield is embedded in the spot price. The second approach is based on a multi-factor model (MFM) featuring three correlated stochastic factors.Design/methodology/approachThe two proposed approaches are analysed in terms of parameter-estimation accuracy, information criteria and prediction performance. For MSM’s calibration, the quasi-log-likelihood method was applied directly while for the MFM’s parameter estimation, this paper designs an enhanced multi-variate maximum likelihood method with the aid of moments matching. The numerical experiments make use of both simulated and actual data compiled by the Fish Pool ASA. Data on both the Fish Pool’s forwards and Norwegian T-bill yields were additionally used in the MFM’s implementation.FindingsUsing simulated data sets, the MSM estimation gives more accurate results than the MFM estimation in terms of the norm in ℓ2 between the “true” and “computed” parameter estimates and significantly lower standard errors. With actual data sets used to evaluate the forecast values, both approaches have similar performances based on the error analysis. Under some metrics balancing goodness of fit and model complexity, the MFM outperforms the MSM.Originality/valueWith the aid of simulated and observed data sets examined in this paper, insights are gained concerning the appropriateness, as well as the benefits and weaknesses of the two proposed approaches. The modelling and estimation methodologies serve as prelude to reliable frameworks that will support the pricing and risk management of derivative contracts on fish price evolution, which creates price risk transfer mechanisms from the fisheries/aquaculture sector to the financial industry.