Markov-switching Autoregression Research Articles

Conditional Seasonal Markov-Switching Autoregressive Model to Simulate Extreme Events: Application to River Flow

Extreme events have the potential to significantly impact transportation infrastructure performance. For example, in the case of bridges, climate change impacts the river discharge, hence scouring patterns, which in turn, affects the bridge foundation stability. Therefore, extreme events (river flow) forecasting is mandatory in bridge reliability analysis. This paper approaches this river flow forecasting problem by developing a Markov-Switching Autoregressive model coupled with a conditional hidden seasonal Markov component. In addition, the proposed model is also combined with the deep machine learning neural networks method to forecast river flow from a dataset or from simulations. The proposed method is illustrated by using realistic data: historic river flow values of the Thames River. The results indicate that the proposed model well represented the extreme events within the dataset. In terms of river flow forecasting, the results indicate that the forecasts improve when the training period changes from 20 years to 40 years.

Performance of negatively screened sustainable investments during crisis

We investigate the market performance of negatively screened environment social and governance (ESG) portfolio or sustainable investments prior to and during crisis. A general and simple method is developed under the ESG Capital Asset Pricing Model (CAPM) framework for the assessment. The novelty is that this method can be employed when the parent portfolio is not a market portfolio. In this situation, both coefficients, alpha and beta, in the reduced form of regression have special interpretations and are informative. This paper examines 24 negatively screened ESG indices from the S&P, DJSI and MSCI data across various regions, firm sizes, and criteria of screening, for 2017 to 2021. Markov Switching Autoregressive (MSAR) model is adopted to identify the crisis regime. Our results show that the negatively screened ESG indices provide positive investors’ surpluses for ESG-motivated investors during the crisis, when the corresponding parent indices are the market portfolios. For ESG investments where market portfolios are not their parent indices, half of ESG indices under consideration still provide positive surplus with similar systematic risks as their parent indices during the crisis. The remaining ESG indices under-performs but has relatively lower systematic risks, implying resilience as compared to the corresponding parent indices during the crisis. Furthermore, we demonstrate the sensitivity analysis of treating a parent index as a market portfolio.

A Markov-Switching model of public debt in South Africa

This research aimed to explore the sustainability of public debt with changes in the South African regime from 1960 to 2020. Annual data was obtained from the South Africa Reserve Bank, and the Markov-switching autoregressive (MS-AR) model was employed to describe the public debt process in South Africa. Compared to traditional endogenous modelling, the process explicitly allows for the possibility of regime changes. Regime 1 defines the stages of high public debt, and regime 2 describes periods of a downward trend in public debt. The results reveal that in regime 1, there is a 98% likelihood of remaining in high public debt and a lower probability of 1.76% switching to a lower public debt regime. Also, when the system is in a lower debt regime, there is a 98.2% likelihood of remaining in a lower public debt state and a lower probability of 1.74% switching to a higher public debt state. The expected average duration of a period of higher public debt is 56 years, while the average duration of a lower public debt regime is 57 years. This implies that it is only in an extreme event that public debt can switch from a high-debt regime to a low-debt regime and vice versa. Hence, it is suggested that the government implement policies knowing that public debt will not decline suddenly.

Synchronization in Cycles of China and India During Recent Crises: A Markov Switching Analysis.

We study the impact of recent crisis episodes viz. the Great Recession of 2007-09, the Euro Area crisis of 2010-12 and the COVID-19 pandemic of 2020-21 on the Emerging Market Economies (EMEs) of China and India using data from January, 1986 till June, 2021. A Markov-switching (MS) analysis is applied to discern economy-specific cycles/regimes and common cycles/regimes in the growth rates of the economies. We apply the univariate MS Autoregressive (MS-AR) model to characterize country-specific negative growth, moderate growth and high growth regimes of China and India. We examine the extent of overlap of the identified regimes with the Great Recession, the Eurozone crisis, and the COVID-19 pandemic. Thereafter, we study the regimes depicting common phases in growth rates of China-India and China-India-US by using multivariate MS Vector Autoregressive (MS-VAR) models. The multivariate analysis shows the presence of common negative growth during the turbulent periods during the study period. These results can be explained by the existence of strong trade and financial linkages between the two EMEs and the Advanced economies. The pandemic triggered a recession in the Chinese, Indian and U.S. economies and its impact on growth is much worse than the Great Recession and the Eurozone crises.

Conditional Seasonal Markov-Switching Autoregressive Model to Simulate Extreme Events: Application to River Flow

Conditional Seasonal Markov-Switching Autoregressive Model to Simulate Extreme Events: Application to River Flow

Modeling the hourly consumption of electricity during period of power crisis

<abstract><p>In this paper, we capture the dynamic behavior of hourly consumption of electricity during the period of power crisis ("dumsor'' period) in Ghana using two-state Markov switching autoregressive (MS-AR) and autoregressive (AR) models. Hourly data between the periods of January 1, 2014 and December 31, 2014 was obtained from the Ghana Grid company and used for the study. Using different information criteria, the MS(2)-AR(4) is selected as the optimal model to describe the dynamic behavior of electricity consumption during periods of power crisis in Ghana. The parameters of the MS(2)-AR(4) model are then estimated using the expectation-maximization algorithm. From the results, the likelihood of staying under a low electricity consumption regime is estimated to be 87%. The expected duration for a low electricity consumption regime is 7.8 hours, and the high electricity consumption regime is expected to last 2.3 hours before switching to the low demand regime. The proposed model is robust as compared to the autoregressive model because it effectively captures the dynamics of electricity demand over time through the peaks and significant fluctuations in consumption patterns. Similarly, the model can identify distinct regime changes linked to electricity consumption during periods of power crises.</p></abstract>

A time-varying copula approach for constructing a daily financial systemic stress index

This paper develops a financial systemic stress index (FSSI) for the US financial market. We propose a time-varying copula method to model the dependence structure among financial sectors in order to build a correlated financial stress model that can signal systemic financial risks. The copula method is preferable to the traditional approach, enabling the modeling of non-linear correlations. Our analyses show that the dependencies across banking, security, and forex markets are best modeled by Archimedian copulas. Finally, we conduct a Markov Switching Autoregressive (MS-AR) model for FSSI and identify high financial stress episodes taking place in 2008–2009, 2011 and 2020.

Co-movement and causal relationships between conventional and Islamic stock market returns under regime-switching framework

PurposeThis paper aims at examining the co-movement dependent regime and causality relationships between conventional and Islamic returns for emerging, frontier and developed markets from November 2008 to August 2020.Design/methodology/approachFirst, the authors used the Markov-switching autoregression (MS–AR) model to capture the regime-switching behavior in the stock market returns. Second, the authors applied the Markov-switching regression and vector autoregression (MS-VAR) models in order to study, respectively, the co-movement and causality relationship between returns of conventional and Islamic indexes across market states.FindingsResults show the presence of two different regimes for the three studied markets, namely, stability and crisis periods. Also, the authors found evidence of a co-movement relationship between the conventional and Islamic indexes for the three studied markets whatever the regime. For the Granger causality, it is proved only for emerging and developed markets and only during the stability regime. Finally, the authors conclude that Islamic indexes can act as diversifiers, or safe-haven assets are not strongly supported.Originality/valueThis paper is the first study that examines the co-movement and the causal relationship between conventional and Islamic indexes not only across different financial markets' regimes but also during the COVID-19 period. The findings may help investors in making educated decisions about whether or not to add Islamic indexes to their portfolios especially during the recent outbreak.

Examining the long term relationships between energy commodities prices and carbon prices on electricity prices using Markov Switching Regression

The present work aims to quantitatively measure the relationships between the price of energy commodities, coal, gas natural, fuel oil, carbon prices and the price of wholesale electricity in the Iberian Electricity Market, using 2018 daily data. To examine this relationship, we considered both techniques, Markov-Switching Dynamic Regression and Markov-Switching Autoregressive Regression, and proposed two equations with electricity price and coal price as dependent variables. According to the parameters estimated in the model, coal and gas affect the cost of electricity moderately at times in the day that are highly recessive. During the 2018 daily periods analysed, the relative changes in gas and coal prices led to a loss of competitiveness of natural gas, increased by the moderate evolution of carbon prices, and therefore the cost of coal fell sharply in the recent past. The evolution of both time-varying transition probabilities and energy commodities prices variables is informative. The transition probabilities of staying in the same state change throughout our sample of energy commodities and wholesale electricity prices.

Regime-switching empirical similarity model: a comparison with baseline models

In this paper, I extend the standard specification of the empirical similarity (ES) model of Gilboa et al. (Rev Econ Stat 88:433–444, 2006) to account for changes in parameters. I implement this by allowing for a combination of component ES models in the spirit of Gaussian mixture models. The predictive power of the modified model, along with that of the standard specification, will be assessed and compared to the baseline models consisting of autoregressions and Markov-switching autoregressions within a simulation exercise. Finally, we also compare the predictive ability of models using data on quarterly US real GDP growth. The results indicate that in situations of a more complex regime-switching behavior and a moderate to high autocorrelation in series, modified ES model demonstrates a better empirical fit. In addition, results of the empirical example show that modified ES models can better predict more extreme observations.

Several explorations on how to construct an early warning system for local government debt risk in China.

This paper aims to explore several ways to construct a scientific and comprehensive early warning system (EWS) for local government debt risk in China. In order to achieve this goal, this paper studies the local government debt risk from multiple perspectives, i.e., individual risk, contagion risk, static risk and dynamic risk. Firstly, taking China's 30 provinces over the period of 2010~ 2018 as a sample, this paper establishes early warning indicators for individual risk of local government debt, and uses the network model to establish early warning indicators for contagion risk of local government debt. Then, this paper applies the criteria importance though intercrieria correlation (CRITIC) method and coefficient of variation method to obtain the proxy variable Ⅰ, which combines the above two risks. Secondly, based on the proxy variable Ⅰ, both the Markov-switching autoregressive (MS-AR) model and coefficient of variation method are used to obtain the proxy variable Ⅱ, which comprehensively considers the individual risk, contagion risk, static risk and dynamic risk of local government debt. Finally, machine learning algorithms are adopted to generalize the EWS designed in this paper. The results show that: (1) From different perspectives of local government debt risk, the list of provinces that require early warning is different; (2) The support vector machines can well generalize our EWS.

Are CDS spreads predictable during the Covid-19 pandemic? Forecasting based on SVM, GMDH, LSTM and Markov switching autoregression

This paper investigates the forecasting performance for credit default swap (CDS) spreads by Support Vector Machines (SVM), Group Method of Data Handling (GMDH), Long Short-Term Memory (LSTM) and Markov switching autoregression (MSA) for daily CDS spreads of the 513 leading US companies, in the period 2009–2020. The goal of this study is to test the forecasting performance of these methods before and during the Covid-19 pandemic and to check whether there are changes in the market efficiency. MSA outperforms all other methods most frequently. GMDH breaks the efficient market hypothesis more frequently (75%) than other methods. The change of the relative predictability during Covid-19 is small with some increase of the advantage of the investigated methods over a benchmark. We find that the market has been less efficient during Covid-19, however, there are no huge differences in prediction performances before and during the Covid-19 period.

Regime Shifts in the Behaviour of International Currency and Equity Markets: A Markov-Switching Analysis.

This paper examines regime switching behaviour and dynamic linkages among currency and equity markets of Eurozone, India, Japan and U.S. using a Markov-switching framework. First, we seek to characterize the market specific and common regime shifts in international stock and currency markets. Second, we aim to study regime-dependent conditional correlations across these markets. We estimate state-dependent models for the financial markets in a univariate Markov-switching Autoregression (MS-AR) as well as a multivariate Markov-switching Vector Autoregression (MS-VAR) framework. The paper utilizes weekly data from July, 1999 to October, 2020 to model the interactions among the markets. Our univariate results identify two-states viz. bull state (bear state) characterized by high returns (low returns) and low volatility (high volatility) for the stock market indices and Euro/USD and INR/USD returns. For the Yen/USD market the bull state corresponds to depreciation accompanied by low volatility. Further, we employ a multivariate formulation to study the regimes across asset classes which provides additional insights into the common states across the markets. Using the MS-VAR model encompassing stocks and currencies, we find a tranquil regime characterized by lower volatility and higher returns and a turbulent regime depicted by higher volatility and lower returns. Contemporaneous correlations among asset market pairs are sharper during the crises. Some of the turbulent periods highlighted in the analysis include the dot-com bubble burst, South American crisis, 9/11, Iraq war, housing bubble burst, global financial crisis, Eurozone debt crisis, Taper Tantrum, Brexit, U.S. Federal Government Shutdown, U.S.-China Trade War and the recent COVID-19 pandemic.

Forecasting daily attraction demand using big data from search engines and social media

Purpose This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media. Design/methodology/approach Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy. Findings Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error. Practical implications Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions. Originality/value This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

Regime shifts in the COVID-19 case fatality rate dynamics: A Markov-switching autoregressive model analysis

The 2019 novel coronavirus disease (COVID-19) has spread rapidly to many countries around the world from Wuhan, the capital of China’s Hubei province since December 2019. It has now a huge effect on the global economy. As of 13 September 2020, more than 28, 802, 775, and 920, 931 people are infected and dead, respectively. The mortality of COVID-19 infections is increasing as the number of infections increase. Many countries published control measures to contain its spread. Even though there are many drugs and vaccines under trial by pharmaceutical companies and research groups, no specific vaccine or drug has yet been found. Therefore, it is necessary to explain the behaviour of the case fatality rate (CFR) of COVID-19 using the most updated COVID-19 epidemiological data before 13 September 2020. The dynamics in the CFR were analyzed using the Markov-switching autoregressive (MSAR) models. Results showed that the two-regime and three-regime MSAR approach better captured the non-linear dynamics in the CFR time series data for each of the top heavily infected countries including the world. The results also showed that rises in CFRs are more volatile than drops. We believe that this information can be useful for the government to establish appropriate policies in a timely manner.

Pemodelan Laju Perubahan Nilai Tukar Rupiah (IDR) terhadap Dolar Amerika (USD) dengan Metode Markov Switching Autoregressive (MSAR)

This study aims to determine the modeling of the exchange rate change rate of the Rupiah (IDR) against the US dollar (USD) using the Markov Switching Autoregressive (MSAR) method. The research data is sourced from secondary data through website investing to see the Rupiah exchange rate against the US dollar with a time span from January to December 2020. The results show that the best model obtained is MS(2)AR(3) with parameters = 0.031119 and = -0.000504, where when state = 1, the average rate of change in the rupiah exchange rate against the US dollar is 0.031119 per day, while when state = 2 the average rate of change in the rupiah exchange rate against the US dollar is -0.000504 per day.

High-frequency volatility modeling: A Markov-Switching Autoregressive Conditional Intensity model

We develop a Markov-Switching Autoregressive Conditional Intensity (MS-ACI) model with time-varying transitional probability, and show that it can be reliably estimated via the Stochastic Approximation Expectation–Maximization algorithm. Applying our model to high-frequency transaction data, we detect two distinct regimes in the intraday volatility process: a dominant volatility regime that is observable throughout the trading day representing the risk-transferring trading activity of investors, and a minor volatility regime that concentrates around market liquidity shocks which mainly capture impacts of firm-specific news arrivals. We propose a novel daily volatility decomposition based on the two detected volatility regimes.

Can We Reckon Bitcoin as a Hedge, a Safe Haven or a Diversifier for US Dollars?

This article aims to analyse the hedging, diversifier and safe-haven properties of Bitcoin for the US Dollar Index (USDI). We explore the long-term relationship between USDI and Bitcoin by estimating a Markov-switching autoregressive (MS-AR) model with two regimes. Thus, the data used cover the period from 18 January 2010 to 30 June 2017 for both USDI and Bitcoin. The empirical findings based on the analysis of the MS-AR model report that investing in Bitcoin involves more benefits than USDI even if the economy is in a recession. However, by examining Bitcoin and USDI volatility, the research findings underline positive dependency between the two. Such results denote that Bitcoin does not act as a hedge, and not even as a safe haven, against USDI. We found that Bitcoin is merely a diversifier for USDI. Accordingly, the outcomes will help investors and portfolio risk managers to make more up-to-date investment analyses and decisions.

Inflation Expectations Modeling: The Application of Markov Switching Autoregression

The shift of the monetary policy framework in Ukraine has led to the transformation of the power of the key transmission channels. Inflation targeting (IT) assumes the interest rates channel as the main one; besides that, since the exchange rate is floating, the influence from the key policy rate change on it is stronger and more effective. Implementation of the IT at the end of 2016 significantly affected the prices; in particular, the inflation has slowed down from almost 60 % in the corresponding month of the previous year to 2.3 % in 2020. Specifically, the interaction between the key policy rate, which is the main monetary policy instrument under the IT, consumer prices, and economic growth has changed. One of the important channels is the inflation expectations channel that became influential after IT has started. Consequently, the article is aimed to investigate how inflation expectations are dependent on the inflation dynamics, the key policy rate changes, and the index of the real wage fluctuations applying Markov switching autoregression model with two regimes, which is widely used to estimate the effects of economic crisis, break of the trend, and instability. The developed model consists of two equations, one of which defines a regime with the high volatility, and the other with the low. The obtained results show the dominance of the high volatility regime during almost all the research period because of essential inflation uncertainty and the overall unstable situation in the country caused by the crisis. Additionally, probability to stay in the first regime is much higher than in the second and is equal to 91 %. Outcomes from the modelling allow to judge about the future inflation and inflation expectations dynamics based on discovered new features. The further steps in the evaluation of effects of monetary policy regimes shift are expansion of the model to three- or four-regime switch (high, low, moderate volatility, etc.), the use of additional independent variables, experiments with the key policy rate, and inflation as dependent variables. JEL classification: E42, E58, C34 Manuscript received 10.04.2020

Stock market volatility and exchange rate movements in the Gulf Arab countries: a Markov-state switching model

Purpose This paper aims to investigate the dynamic linkage between stock prices and exchange rate changes for the Gulf Arab countries (Kuwait, Qatar, Saudi Arabia and United Arab Emirates [UAE]). Design/methodology/approach The author uses the Markov-switching autoregression to detect regime-shift behavior in the stock returns of the Gulf Arab countries and Markov-switching vector autoregressive (MS-VAR) model to capture the dynamic interrelatedness between exchange and stock returns over the period 2000–2018. Findings This study’s analysis finds evidence to support the persistence of two distinct regimes for all markets, namely, a low-volatility regime and a high-volatility regime. The low-volatility regime illustrates more persistence than the high-volatility regime. Specifically, exchange rate changes do not have an influence on the stock market returns of the Gulf Arab countries, regardless of the regimes. On the other hand, stock market returns have a substantial impact on exchange markets for all countries, except Saudi Arabia, and it is more noticeable during the regime of high volatility. Practical implications The findings shed light on the interconnectedness between two of the most important financial markets in the complex international financial environment. They are thus of particular interest for economic policymakers and portfolio investors. Originality/value The author distinguishes this study from previous studies in several ways. First, while previous empirical studies of the dynamic linkage between stock prices and foreign exchange markets are primarily devoted to developed markets or emerging markets, this study’s interest is concentrated on four Gulf Arab financial markets (Kuwait, Qatar, Saudi Arabia and UAE). Second, unlike most investigations in the literature that only estimate this link for the whole period, this study attempts to estimate during the good and bad period by using a two-regime MS-VAR model. To the best of the author’s knowledge, this is the first study of the Gulf Arab countries on the stock and foreign exchange markets to apply this model.