Journal articles: 'Markov-switching regression model'

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Qazi, Atika, Ram Gopal Raj, and Celestine O. Eledo. "PATTERN PREDICTION OF CRUDE OIL USING REGRESSION MODERATED WITH MARKOV SWITCHING MODEL." Malaysian Journal of Computer Science 32, no. 2 (2019): 149–62. http://dx.doi.org/10.22452/mjcs.vol32no2.5.

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Xaba,, Diteboho, Ntebogang Dinah Moroke,, and Ishmael Rapoo. "Modeling Stock Market Returns of BRICS with a Markov-Switching Dynamic Regression Model." Journal of Economics and Behavioral Studies 11, no. 3(J) (2019): 10–22. http://dx.doi.org/10.22610/jebs.v11i3(j).2865.

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This article adopted a Markov-switching dynamic regression (MS-DR) model to estimate appropriate models for BRICS countries. The preliminary analysis was done using data from 01/1997 to 01/2017 and to study the movement of 5 stock market returns series. The study further determined if stock market returns exhibit nonlinear relationship or not. The purpose of the study is to measure the switch in returns between two regimes for the five stock market returns, and, secondly, to measure the duration of each regime for all the stock market returns under examination. The results proved the MS-DR model to be useful, with the best fit, to evaluate the characteristics of BRICS countries.

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Bojanic, Antonio N. "A Markov-Switching Model of Inflation in Bolivia." Economies 9, no. 1 (2021): 37. http://dx.doi.org/10.3390/economies9010037.

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The Bolivian inflation process is analyzed utilizing a time-varying univariate and multivariate Markov-switching model (TMS). With monthly data and, beginning in the late 1930s, inflation is accurately described by a univariate TMS. The intercept for the high-inflation regime is significantly higher than for the low-inflation regime and the actual inflation rate mirrors the smoothing probabilities of the Markov process. Additionally, the predicted duration of each regime closely fits the periods when the country experienced low and inordinate high inflation rates. From a long-run perspective and utilizing a multivariate TMS, the results generally fall in line with what the quantity theory of money predicts. In the high-inflation regime, money growth increases inflation (almost) one-for-one, as classical economics contends. From a short-run perspective and in the high-inflation regime, inflation is almost exclusively explained by a negative output gap. In the low-inflation regime, lagged inflation is the most important determinant of inflation, in line with price stickiness expectations. Partitioning the sources of inflation demonstrate that, from a long-run perspective and in the high inflation regime, differences in inflation are mostly explained by GDP growth; in the low-inflation regime, money growth and velocity growth are the principal factors explaining the variance of inflation. From a short-run perspective, the output gap explains almost all regression variance in the high-inflation regime, and past inflation does the same during times of low inflation, though in both cases the R2 is low which precludes making definite statements about the sources of variability in inflation.

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Nobrega, Wellington Charles Lacerda, Cássio da Nóbrega Besarria, and Felipe Araújo De Oliveira. "Unemployment rate and wage growth in Brazil: evidence from a Markov-switching model." Economia Aplicada 24, no. 2 (2020): 171–94. http://dx.doi.org/10.11606/1980-5330/ea151926.

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This paper has the purpose to investigate the relationship between unemployment rate and wage growth for the Brazilian economy from 2000to 2016, by means of a Markov-switching regression model. The empirical approach is based on the New-Keynesian Phillips Curve developed by Galí (2011). The estimation results suggest the existence of two well definedregimes, one characterized by the non-validation of the Phillips Curve, while in the other the trade-off between unemployment and wageinflation is validated, with the economic cycle being a key factor in regime switching.

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Li, M., and S. Yen. "Re-examining covariance risk dynamics in international stock markets using quantile regression analysis." Acta Oeconomica 61, no. 1 (2011): 33–59. http://dx.doi.org/10.1556/aoecon.61.2011.1.3.

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This investigation is one of the first to adopt quantile regression (QR) technique to examine covariance risk dynamics in international stock markets. Feasibility of the proposed model is demonstrated in G7 stock markets. Additionally, two conventional random-coefficient frameworks, including time-varying betas derived from GARCH models and state-varying betas implied by Markov-switching models, are employed and subjected to comparative analysis. The empirical findings of this work are consistent with the following notions. First, the beta smile (beta skew) curve for the Italian, U.S. and U.K. (Canadian, French and German) markets. That is, covariance risk among global stock markets in extremely bull and/or bear market states is significantly higher than in stable periods. Additionally, the Japanese market provides a special case, and its beta estimate at extremely bust state is significantly lower, not higher than that at the middle region. Second, the quantile-varying betas are identified as possessing two key advantages. Specifically, the comparison of the system with quantile-varying betas against that with time-varying betas implied by GARCH models provides meaningful implications for correlation-volatility relationship among international stock markets. Furthermore, the quantile-varying beta design in this study relaxes a simple dual beta setting implied by Markov-switching models of Ramchand — Susmel (1998) and can identify dynamics of asymmetry in betas.

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Yamaka, Woraphon, Xuefeng Zhang, and Paravee Maneejuk. "Analyzing the Influence of Transportations on Chinese Inbound Tourism: Markov Switching Penalized Regression Approaches." Mathematics 9, no. 5 (2021): 515. http://dx.doi.org/10.3390/math9050515.

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This study investigates the nonlinear impact of various modes of transportation (air, road, railway, and maritime) on the number of foreign visitors to China originating from major source countries. Our nonlinear tourism demand equations are determined through the Markov-switching regression (MSR) model, thereby, capturing the possible structural changes in Chinese tourism demand. Due to many variables and the limitations from the small number of observations confronted in this empirical study, we may face multicollinearity and endogeneity bias. Therefore, we introduce the two penalized maximum likelihoods, namely Ridge and Lasso, to estimate the high dimensional parameters in the MSR model. This investigation found the structural changes in all tourist arrival series with significant coefficient shifts in transportation variables. We observe that the coefficients are relatively more significant in regime 1 (low tourist arrival regime). The coefficients in regime 1 are all positive (except railway length in operation), while the estimated coefficients in regime 2 are positive in fewer numbers and weak. This study shows that, in the process of transportation, development and changing inbound tourism demand from ten countries, some variables with the originally strong positive effect will have a weak positive effect when tourist arrivals are classified in the high tourist arrival regime.

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Zahro, Maratus, and Rika Rahayu. "Nilai Transaksi E-Money di Indonesia dengan Menggunakan Metode Markov Switching Model." Owner 5, no. 2 (2021): 644–52. http://dx.doi.org/10.33395/owner.v5i2.392.

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The purpose of the research is to examine and analyze the interest rates, inflation rates, stock returns, and holiday conditions on the values of e-money transactions based on two conditions, before and after the issuance of Bank Indonesia regulations. The research data used in this study is the monthly statistical data of the Bank Indonesia payment system for the period 2008-2018. While, the research was the quantitative using the Markov Regime Switching Model and hypothesis testing using time series regression. The research results showed that there was a significant effect between the inflation rate and the value of e-money transactions. In addition, there was an insignificant effect on interest rates, stock returns, and holiday conditions on the value of e-money transactions. This research contributes to the development of economic research by predicting the value of e-money transactions and predicting the value e-money transactions and predicting the turning point of e-money transactions value based on two conditions, before and after the issuance of Bank Indonesia regulations and can be used as input to the government regarding the regulations that issued by Bank of Indonesia, regulations regarding increased the value of e-money transactions.

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Dombrovskii, Vladimir V., and Tatiana Yu Pashinskaya. "Optimal predictive control strategies for systems with random parameters described by multidimensional Markov switching regression model." Vestnik Tomskogo gosudarstvennogo universiteta. Upravlenie, vychislitel'naya tekhnika i informatika, no. 48 (September 1, 2019): 4–12. http://dx.doi.org/10.17223/19988605/48/1.

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Samitas, Aristeidis, and Aggelos Armenatzoglou. "Regression tree model versus Markov regime switching: a comparison for electricity spot price modelling and forecasting." Operational Research 14, no. 3 (2014): 319–40. http://dx.doi.org/10.1007/s12351-014-0149-6.

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Makatjane, Katleho, and Diteboho Xaba. "An early warning system for inflation using Markov-Switching and logistic models approach." Risk Governance and Control: Financial Markets and Institutions 6, no. 4 (2016): 30–39. http://dx.doi.org/10.22495/rcgv6i4art5.

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With the adoption of the inflation targeting by the South African Reserve Bank (SARB) in 2000, the average inflation radically went down. Earlier 2000, the inflation rate was recorded at 8.8% that is January 1999; then a year later went down to 2.65%. What’s more, this paper builds up an early warning system (EWS) model for predicting the event of high inflation in South Africa. Periods of high and low inflation were distinguished by utilizing Markov-switching model. Utilizing the results of regime classification, logistic regression models were then assessed with the goal of measuring the likelihood of the event of high inflation periods. Empirical results demonstrate that the proposed EWS model has some potential as a corresponding instrument in the SARB’s monetary policy formulation based on the in-sample and out-of-sample forecasting performance.

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Pastpipatkul, Pathairat, Petchaluck Boonyakunakorn, and Kanyaphon Phetsakda. "The Impact of Thailand’s Openness on Bilateral Trade between Thailand and Japan: Copula-Based Markov Switching Seemingly Unrelated Regression Model." Economies 8, no. 1 (2020): 9. http://dx.doi.org/10.3390/economies8010009.

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The purpose of this paper is to analyze the impact of trade openness and the factors based on the gravity model on the bilateral trade flows between Thailand and Japan. The factors consist of GDP, distance, trade openness, and exchange rate. Bilateral trade is composed of two flows: Thailand’s export flow to Japan, and Thailand’s import flow from Japan. The specified gravity equations are estimated by Copula-based Markov switching seemingly unrelated regression approach. The best-fitting model is chosen based on the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC). The Normal and Student’s t distributions are for Thailand’s export equation and Thailand’s import equation, respectively. The Student’s t copula is applied for joint distribution. Analyzing the bilateral trade flow is separated into two situations, namely the high and the low growth markets. Empirical results show that distance provides a positive effect on the export in a high growth regime, but a negative impact on the export in a low growth regime. As for Thailand’s import flow, all variables, but especially trade openness, provide strong evidence supporting significance for both regimes. For the GDPs of both Thailand and Japan, trade openness and the exchange rate increase import flow in a high growth market. Meanwhile, the exchange rate decreases import flow in a low growth market. The Markov Switching Probability Estimation notes that Thailand’s trading with Japan is mostly in the fast-growing market.

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Xaba, Diteboho, Ntebogang Dinah Moroke, Johnson Arkaah, and Charlemagne Pooe. "A Comparative Study Of Stock Price Forecasting Using Nonlinear Models." Risk Governance and Control: Financial Markets and Institutions 7, no. 2 (2017): 7–17. http://dx.doi.org/10.22495/rgcv7i2art1.

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This study compared the in-sample forecasting accuracy of three forecasting nonlinear models namely: the Smooth Transition Regression (STR) model, the Threshold Autoregressive (TAR) model and the Markov-switching Autoregressive (MS-AR) model. Nonlinearity tests were used to confirm the validity of the assumptions of the study. The study used model selection criteria, SBC to select the optimal lag order and for the selection of appropriate models. The Mean Square Error (MSE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) served as the error measures in evaluating the forecasting ability of the models. The MS-AR models proved to perform well with lower error measures as compared to LSTR and TAR models in most cases.

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Cifter, Atilla. "Stock returns, inflation, and real activity in developing countries: A Markov-switching approach." Panoeconomicus 62, no. 1 (2015): 55–76. http://dx.doi.org/10.2298/pan1501055c.

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This paper empirically investigates the relationship between real stock returns, inflation, and real activity using the Markov-switching dynamic regression (MS-DR) approach. The MS-DR allows multiple structural breaks in the estimation, and we can check regression coefficients separately in the recession and expansion periods. We selected two major developing countries (Mexico and South Africa) in order to reduce location bias. We use real stock returns, expected inflation, unexpected inflation, and real GDP growth in the estimations, and the ARFIMA model is used for unexpected inflation. The empirical results show that the relationship between real stock returns and inflation is negative only in the recession period. This regime-dependency is also tested with Eugene F. Fama?s (1981) proxy effect hypothesis, and it is found that the stock returns respond differently to inflation in a regime according to the regime-dependent proxy effect hypothesis. These findings suggest that the negative relationship puzzle in the empirical finance literature can be explained with the regime-dependency effect.

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Singh, Amanjot, and Parneet Kaur. "Does US Financial Stress Explain Risk–Return Dynamics in Indian Equity Market? A Logistic Regression Approach." Vision: The Journal of Business Perspective 21, no. 1 (2017): 13–22. http://dx.doi.org/10.1177/0972262917695116.

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The present study attempts to capture the impact of the US financial stress on the risk–return dynamics in the Indian equity market by employing Markov regime switching and binary logistic regression model. The span of data ranges from 2004 to 2013. The study uses weekly closing local values of benchmark equity indices ‘CNX Nifty 50 and S&P 500’ and St. Louis Fed Financial Stress Index (SFSI). The said index captures stress in the US financial system on a weekly basis. The Markov results support the existence of ‘Bull’ regime as well as ‘Bear’ regime in the Indian equity market. Corresponding to this, the logistic regression model indicates a positive impact of the US financial stress on the probability for the existence of bear regime. Particularly, the probability for the existence of bull regime approaches zero, when the stress in the US financial system crosses the level of two. The results support strong implications for the investors in the Indian equity market against the stress in the US financial system.

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Thananchana, Annop, Pichayakone Rakpho, Woraphon Yamaka, and Songsak Sriboonchitta. "Analysis of Markov switching seemingly unrelated regression model with skewed distributions, and its application to Thai cassava market." Journal of Physics: Conference Series 1053 (July 2018): 012114. http://dx.doi.org/10.1088/1742-6596/1053/1/012114.

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Tsagkanos, Athanasios, Costas Siriopoulos, and Konstantina Vartholomatou. "Foreign direct investment and stock market development." Journal of Economic Studies 46, no. 1 (2019): 55–70. http://dx.doi.org/10.1108/jes-06-2017-0154.

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Purpose The purpose of this paper is to examine two novel theories that concern the relationship between stock market development (SMD) and foreign direct investment (FDI). The authors focus on Greece that was demoted to the emerging market category in 2013–2014 in the international lists. Design/methodology/approach This study is based on the period 1988–2014 that includes the sub-periods 1988–2001 (emerging market) and 2002–2014 (developed market). The authors adopt cointegration methods examining, on the one hand, if the relationship between SMD and FDI is positive or negative and, on the other hand, if it is long run or short run. The authors complete the analysis using the Markov Switching regression model for the test of robustness. Findings The results exhibit a weak positive and symmetric long-run relationship for the full period. In the first sub-period, the relationship is strong but in the second sub-period it is not significant. The results are confirmed by the Markov Switching regression model. Originality/value The precise definition of a theoretical framework that is tested by a compact empirical methodology leads to a novel suggested policy that will upgrade the Greek market to developed market as soon as possible.

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Wibowo, Buddi. "Comovement Indeks Pasar Saham Syariah dan Variabel Makro Ekonomi: Pendekatan Regime-Switching Regression." IQTISHADIA 10, no. 2 (2018): 83. http://dx.doi.org/10.21043/iqtishadia.v10i2.2237.

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Hubungan imbal hasil indeks syariah dengan variabel makroekonomi merupakan topik riset yang cukup banyak menarik perhatian para peneliti. Comovement antara indeks syariah dengan indeks pasar saham konvensional mengindikasikan adanya hubungan kointegrasi antara kedua pasar tersebut. Comovement antara indeks syariah dan indeks pasar konvensional jauh lebih kuat pada saat volatilitas rendah dibandingkan pada regime volatilitas yang tinggi. Signifikannya hubungan antara imbal hasil indeks syariah di Bursa Efek Indonesia dengan perubahan suku bunga memunculkan pertanyaan apakah saham-saham perusahaan yang termasuk di dalam Jakarta Islamic Index telah disaring secara ketat sehingga komponen biaya bunga sudah minimal atau karena mikro struktur pasar saham Indonesia yang didominasi investor asing. Model regresi Markov regime-Switching mengungkapkan adanya perbedaan signfikan pengaruh perubahan nilai tukar terhadap imbal hasil indeks syariah antara regime volatilitas yang tinggi dengan regime volatilitas yang rendah. Pengaruh nilai tukar hanya signifikan pada saat regime volatilitas yang rendah. Hal ini tidak dapat diungkap jika kita hanya menggunakan model regresi linier OLS biasa.

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Tian, Fengjun, Yang Yang, Zhenxing Mao, and Wenyue Tang. "Forecasting daily attraction demand using big data from search engines and social media." International Journal of Contemporary Hospitality Management 33, no. 6 (2021): 1950–76. http://dx.doi.org/10.1108/ijchm-06-2020-0631.

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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.

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Temkeng, Serge Djoudji, and Achille Dargaud Fofack. "A Markov-switching dynamic regression analysis of the asymmetries related to the determinants of US crude oil production between 1982 and 2019." Petroleum Science 18, no. 2 (2021): 679–86. http://dx.doi.org/10.1007/s12182-021-00549-y.

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AbstractThe structural changes brought about by shale oil revolution have inspired this paper of which the aim is to analyze the potential asymmetries related to the determinants of crude oil production in the USA. Thus, using a Markov-switching dynamic regression model in which parameters change when oil production moves from one regime to the other, it is found that for both oil production and oil relative importance, the regime that was dominant during the 1980s and the early 1990s when oil production in the USA was substantially high is the same regime that has once again become dominant in the decade corresponding to the shale oil revolution. Furthermore, the study reveals the existence of asymmetries in the relationship between US crude oil production and both manufacturing production and the consumer price index. Asymmetries are also found in the relationship between the relative importance US crude oil and manufacturing production. Finally, it is found that the intercept and the variance parameter also vary from one regime to the other, thus justifying the use of regime-dependent models.

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Tang, Ying-Chan, Yu-Mei Wang, and Jiun-Yan Huang. "Optimal promotional strategy for intra-category cross-selling." British Food Journal 116, no. 1 (2013): 80–90. http://dx.doi.org/10.1108/bfj-12-2011-0306.

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Purpose – The aim of this paper is to investigate an optimal promotional strategy of intra-category cross-selling on culinary products for the fiercely competitive, fast-moving consumer goods (FMCG) industry. Design/methodology/approach – A linear regression model and a Markov switching autoregressive model is used, that incorporates a retailing demand process to capture a nonlinear structure among promotional budget allocation, and evaluate promotional performance, and optimal promotional frequency within a given time span. Three product categories are applied with 39 months of time-series data from a multinational packaged food company in Taiwan. Findings – The result shows that most previous decisions on promotional budget allocation are non-optimal – most promotional investments were either extended too long or allocated too low in stimulating sales. Research limitations/implications – This study suggests implications for the brand or category manager in removing such non-optimal promotional policies. Originality/value – Previous promotional investment is evaluated by comparing the changes in promotional budget allocation. Markov's switching feedback rules are then applied to determine the proper length of equilibrium state with and/or without promotion. Finally, effective decision rules on magnitude, duration, and frequency of intra-category promotional strategy are induced.

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Loukianova, I. A., M. A. Shkliarova, and S. Yu Vysotsky. "Modelling of Fiscal and Monetary Policy Interactions in the Republic of Belarus." Journal of Tax Reform 5, no. 3 (2019): 220–35. http://dx.doi.org/10.15826/jtr.2019.5.3.069.

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The article discusses classical and modern macroeconomic models of interaction of fiscal and monetary policies in Belarus. The hypothesis of this research is that the interaction of fiscal and monetary policies has a synergistic effect on economic growth and that at certain stages, one of these policies prevails over the other. This hypothesis was tested with the help of an IS-LM model, which was used to investigate the joint effects of monetary and fiscal policies on business activity in Belarus. A Markov switching model was developed in Eviews software to analyze the interaction between these policies. Regression dependences of the average tax burden (including the burden imposed by social security contributions) and GDP, investment and the refinancing rate were built by using Excel software. To solve the IS-LM model, the value of autonomous consumption was computed with the help of the adjusted value of the average propensity to consume. It was found that autonomous consumption is comparable with the budget of subsistence minimum in Belarus. The share of government spending in the GDP structure was on average 35.01%. The comparison of gross savings and investment showed that in the majority of periods, gross savings insignificantly exceeded the amount of investment, that is, the available funds were used for consumer lending rather than for investment. Analysis of the Markov switching model has led us to the conclusion that from the first quarter 2005 until the fourth quarter of 2009, the fiscal policy in Belarus was in the active regime. The passive fiscal policy regime was observed in the period between the first quarter of 2010 and the first quarter of 2019. In this period, a rise in the public debt was accompanied by an increase in the budget surplus. In the second quarter of 2019, there was a transition to a more active fiscal policy, which points to the need to intensify tax reforms.

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Nurfalah, Irfan, and Aam Slamet Rusydiana. "THE REGIME SWITCHING OF CYCLE INSTABILITY OF ISLAMIC BANKING AND THE ECONOMY: EVIDENCE FROM INDONESIA, MALAYSIA, AND PAKISTAN." Journal of Islamic Monetary Economics and Finance 7, no. 2 (2021): 233–62. http://dx.doi.org/10.21098/jimf.v7i2.1362.

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This study aims to examine the cyclical instability of Islamic banking in Indonesia, Malaysia, and Pakistan. A stable Islamic banking system can give the public confidence to conduct transactions and thus grow the economy. The proxy variable for stability used is the z-score, with 156 periods of research data from January 2007 to December 2019. The Markov Switching Vector Autoregression (MS-VAR) method was employed. The results show that Islamic banking stability in Indonesia based on the z-score is more stable than others. Nevertheless, in terms of the regression of all the variables, regime shifting, and the duration of the crisis, overall Malaysian Islamic banking displays the best performance. The instability of the Indonesian model is mostly affected by inflation, whereas Malaysia and Pakistan are affected by the financing to deposit ratio and the fluctuation in global oil, respectively.

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Chen, Jieting. "What explains the investment anomaly in the Chinese stock market?" Nankai Business Review International 8, no. 4 (2017): 495–520. http://dx.doi.org/10.1108/nbri-05-2016-0021.

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Purpose This paper aims to examine the Chinese investment anomaly and dissect it from a perspective of rational expectation framework. Design/methodology/approach Characteristic-based sorting and Fama–MacBeth two-stage cross-sectional regression are adopted to test the relationship between corporate investment and expected returns in both portfolio and individual stock levels. Under the framework of pricing kernels, an investment-based common risk factor is constructed to test the role of risk played in the negative investment-return relationship. Moreover, a Markov regime switching model is adopted to investigate the time-varying risk premium across market regimes. Findings Empirical results provide ample evidence showing that there is a negative relationship between investment and expected returns in the Chinese stock market. The new investment-based risk factor is found to capture the return differences across characteristic-based portfolios. In addition, risk premium of the new risk factor is not only statistically positive throughout the sample period, but also has an asymmetry that is higher during market downturn but lower under bull market. Research limitations/implications This paper merely tests the hypotheses derived from rational school. Practical implications Investment strategies based on characteristic-sorted portfolios should be adjusted to different market regimes. Originality/value First, this paper provides comprehensive empirical results by adopting different methodologies for investigating the investment anomaly in China. Second, an investment-based factor is constructed specifically for the Chinese stock market for the first time. Finally, this is the first paper to investigate the asymmetric risk premium across the Chinese bear and bull regimes by using a multivariate Markov regime switching model.

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Kim, Hong-Bae, Yeonjeong Lee, Sang Hoon Kang, and Seong-Min Yoon. "Regime Dependent Determinants of Credit Default Swap Spread." Journal of Derivatives and Quantitative Studies 20, no. 1 (2012): 41–64. http://dx.doi.org/10.1108/jdqs-01-2012-b0002.

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This study investigates the influence of theoretical determinants on the Korea sovereign CDS spreads from January 2007 to September 2009 based on structural credit risk model. For the analysis of determinants on the sovereign CDS spread, this study adopts interest swap rate as reference interest rate, and decomposes yields curve into two components, ie, interest level and slope. Considering multivariate regression in level and difference variables, Stock returns and Interest rates have a significant effect on the CDS spreads among the theoretical determinants of structural credit risk models. CDS spreads may behave quite differently during volatile regime compared with their behavior in tranquil regime. We therefore apply Markov switching model to investigate the possibility that the influence of theoretical determinants of CDS spread has a regime dependent behavior. In all regimes Korean sovereign CDS spreads are highly sensitive to stock market returns, whereas in tranquil regime interest rates also have influence on CDS spreads. We conclude that for the efficient hedging of CDS exposure trader should adjust equity hedge ratio to the relevant regime.

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Chai, Shanglei, Mo Du, Xi Chen, and Wenjun Chu. "A Hybrid Forecasting Model for Nonstationary and Nonlinear Time Series in the Stochastic Process of CO2 Emission Trading Price Fluctuation." Mathematical Problems in Engineering 2020 (August 4, 2020): 1–13. http://dx.doi.org/10.1155/2020/8978504.

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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.

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Živkov, Dejan, Boris Kuzman, and Jonel Subić. "What Bayesian quantiles can tell about volatility transmission between the major agricultural futures?" Agricultural Economics (Zemědělská ekonomika) 66, No. 5 (2020): 215–25. http://dx.doi.org/10.17221/127/2019-agricecon.

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This paper investigates an idiosyncratic volatility spillover effect between the four agricultural futures – corn, wheat, soybean, and rise. In order to avoid biased measurements of the volatilities, we use the Markov switching generalized autoregressive conditional heteroskedasticity (MS-GARCH) model. The created volatilities are imbedded in the Bayesian quantile regression framework which can produce accurate quantile estimates. We report that soybean and wheat receive relatively high levels of volatility shocks from the other markets, and that excludes soybean and wheat as primary investment assets in a portfolio. On the other hand, rice receives the lowest amount of volatility shocks from all other agricultural futures. The reason could be the policy of rice price stability that is conducted by countries in the Asia and Pacific region. This result favours rice futures, from the four commodities, as the primary asset in a portfolio. All other futures are suitable to be an auxiliary asset in a portfolio with rice, because rice receives the weakest volatility shocks spillover effect from the other three markets.

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Wang, Yiwei, Shuwang Yang, Canmian Liu, and Shiying Li. "How Would Economic Development Influence Carbon Productivity? A Case from Hubei in China." International Journal of Environmental Research and Public Health 15, no. 8 (2018): 1730. http://dx.doi.org/10.3390/ijerph15081730.

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Carbon productivity, defined as the gross domestic product (GDP) per unit of CO2 emissions, has been used by provincial governments in China as in indicator for effort and effect in addressing climate-change problems. The aggregate impact of economic growth on carbon productivity is complex and worthy of extensive investigation to design effective environmental and economic policies. Based on a novel combination of the smooth transition regression model and the Markov regime-switching regression model, this paper analyzes time series data on carbon productivity and economic growth from Hubei Province in China. The results show that the influence of economic growth on carbon productivity is highly nonlinear. In general, economic growth has a positive impact on improving carbon productivity. From a longitudinal perspective, this nonlinear positive impact is further divided into three stages, transiting from a high regime to a low regime and then back to a high regime. The high regime stage, in which economic growth has stronger positive influence on enhancing carbon productivity, is expected to last for considerably longer time than the low regime stage. It is more probable for a low regime stage to transit to a high regime. Once the relation of carbon productivity and economic growth enters the high regime status it becomes relatively stable there. If the government aims to achieve higher carbon productivity, it is helpful to encourage stronger economic development. However, simply enhancing carbon productivity is not enough for curbing carbon emissions, especially for fast growing economies.

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Bărbuță-Mișu, Nicoleta, Tuna Can Güleç, Selim Duramaz, and Florina Oana Virlanuta. "Determinants of Dollarization of Savings in the Turkish Economy." Sustainability 12, no. 15 (2020): 6141. http://dx.doi.org/10.3390/su12156141.

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This study aims to analyze the nature of the dollarization that takes place in the Turkish economy and to decompose the factors that have contributed to its increase in recent years. With this purpose, we first identify the events that have significantly affected the dollarization trend in Turkey using the Iterative Cumulative Sum of Squares (ICSS) and Markov Switching Dynamic Regression (MS-Dynamic) structural break models. Then, we proceed to analyze the relationship between the percentage of Forex deposits of the residents over total deposits of the residents and the TRY/USD exchange rate using the Johansen cointegration test. USD, EUR, and TRY interest rates are also added to the model as independent variables to account for the effects of the difference between exchange rates. Long-term and short-term effects are tested with the Vector Error Correction Model, and causality is tested using the Granger causality test. The results of the study indicate that speculative trading is not the cause of the dollarization of deposits in Turkey. Additionally, results suggest that the political events have a stronger influence over dollarization compared to economic events. Collectively, our findings suggest that domestic citizens dollarize their deposits with the motivation to protect against political ambiguity rather than economic volatility. The results of the study are in line with the literature in the sense that they support the claim that dollarization can be averted in the short run with an increase in interest rates.

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Jiang, Xiaochun, Wei Sun, Peng Su, and Ting Wang. "The Synergy of Financial Volatility between China and the United States and the Risk Conduction Paths." Sustainability 11, no. 15 (2019): 4151. http://dx.doi.org/10.3390/su11154151.

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Based on monthly data of six major financial variables from January 1996 to December 2018, this paper employs a structural vector autoregressive model to synthesize financial conditions indices in China and the United States, investigates fluctuation characteristics and the synergy of financial volatility using a Markov regime switching model, and further analyzes the transmission paths of the financial risk by using threshold regression. The results show that there is an approximately three-year cycle in the financial fluctuations of both China and the United States, and such fluctuations have a distinct asymmetry. Two thresholds were applied (i.e., 0.361 and 0.583), taking the synergy index (SI) as the threshold variable. The impact of the trade factor is significant across all thresholds and is the basis of financial linkages. When the SI is less than 0.361, the exchange rate factor is the main cause of the financial cycle comovement change. As the financial volatility synergy increases, the asset factor and interest rate factor start to become the primary causes. When the level of synergy breaks through 0.583, the capital factor based on stock prices and house price is still the main path of financial market linkage and risk transmission, but the linkage of monetary policy shows a restraining effect on synergy. Therefore, it is necessary to monitor the financial cycle and pay attention to the coordination between countries in terms of policy regulation.

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Gunay, Samet, Walid Bakry, and Somar Al-Mohamad. "The Australian Stock Market’s Reaction to the First Wave of the COVID-19 Pandemic and Black Summer Bushfires: A Sectoral Analysis." Journal of Risk and Financial Management 14, no. 4 (2021): 175. http://dx.doi.org/10.3390/jrfm14040175.

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In this study, we investigated the impact of the first wave of the COVID-19 pandemic on various sectors of the Australian stock market. Market capitalization and equally weighted indices were formed for eleven Australian sectors to examine the influence of the pandemic on them. First, we examined the financial contagion between the Chinese stock market and Australian sector indices through the dynamic conditional correlation fractionally integrated generalized autoregressive conditional heteroskedasticity (DCC-FIGARCH) model. We found high time-varying correlations between the Chinese stock market and most of the Australian sector indices, with the financial, health care, information technology, and utility sectors displaying a decrease in co-movements during the pandemic. The Modified Iterative Cumulative Sum of Squares (MICSS) analysis results indicated the presence of structural breaks in the volatilities of most of the sector indices around the end of February 2020, but consumer staples, industry, information technology and real estate indices did not display any break. Markov regime-switching regression analysis depicted that the pandemic has mainly affected three sectors: consumer staples, industry, and real estate. When we considered the firm size, we found that smaller companies in the energy sector exhibited gradual deterioration, whereas small firms in the consumer staples sector experienced the largest positive impact from the pandemic.

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Kim, Chang-Jin, Jeremy Piger, and Richard Startz. "Estimation of Markov regime-switching regression models with endogenous switching." Journal of Econometrics 143, no. 2 (2008): 263–73. http://dx.doi.org/10.1016/j.jeconom.2007.10.002.

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Loría, Eduardo. "Poverty trap in Mexico, 1992-2016." International Journal of Development Issues 19, no. 3 (2020): 277–301. http://dx.doi.org/10.1108/ijdi-11-2019-0192.

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Purpose The paper aims to prove that between 1992 and 2016, people in poverty as a proportion of the total population has not been reduced. In particular, food poverty (FP) represented an average of 22%, despite the fact that gross domestic product (GDP) per capita and GDP, social development expenditure and food programme expenditure (both as GDP proportion) grew by 0.96%, 1.9%, 2.7% and −17.4% on an annual average, respectively. Design/methodology/approach There are non-linear relationships between economic growth and food poverty expenditure to reduce poverty. Three econometric models were estimated as follows: a linear model [ordinary least squares (OLS)] that addresses the capability of the economic growth to reduce FP (which detects a structural change in 2007) and three models of regime change (Markov–Switching Regression) that prove the existence of two different regimes. Findings The author proved that economic growth has lost the capability to reduce poverty and that there are decreasing effects of expenditure in addressing poverty since 2007. These results point out that Mexico is in a poverty trap and suggests that for the economy as for life and even more in the case of social (public) policies, more is not necessarily better than less. Therefore, the author suggests that the resources allocated in response to poverty may well have generated perverse incentives that yielded the opposite results. Originality/value There is no official measure of the public expenditure for poverty. Therefore, an accurate series was built to estimate the government effort and do the econometrics that proves the main hypothesis. This is another contribution.

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Ye, Wuyi, Yangguang Zhu, Yuehua Wu, and Baiqi Miao. "Markov regime-switching quantile regression models and financial contagion detection." Insurance: Mathematics and Economics 67 (March 2016): 21–26. http://dx.doi.org/10.1016/j.insmatheco.2015.11.002.

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Kim, In-Moo. "A dynamic programming approach to the estimation of markov switching regression models." Journal of Statistical Computation and Simulation 45, no. 1-2 (1993): 61–76. http://dx.doi.org/10.1080/00949659308811472.

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Van Gysen, Michael, Chun-Sung Huang, and Ryan Kruger. "The Performance Of Linear Versus Non-Linear Models In Forecasting Returns On The Johannesburg Stock Exchange." International Business & Economics Research Journal (IBER) 12, no. 8 (2013): 985. http://dx.doi.org/10.19030/iber.v12i8.7994.

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In this paper we provide a comprehensive comparison of the predictive accuracy of linear and non-linear models when forecasting financial returns, using a number of macroeconomic variables, on the Johannesburg Stock Exchange. We implement a range of linear specifications, Markov switching ARMA and Dynamic Regression models, and univariate models in which the conditional heteroskedasticity is captured by GARCH or EGARCH innovations. Our results indicate that Markov switching models provide the most significant in-sample fit. However, results for the stable portion of the out-of-sample period and the recent recovery period are mixed with both EGARCH-based linear models and 2-state Dynamic Regression models outperforming the alternatives. Over the market crisis period we find that the forecast performance of the nonlinear models is worse than that of the linear models, which suggests that the benefit of the nonlinear treatment of conditional volatility diminishes over this period.

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Leblang, David, and Bumba Mukherjee. "Presidential Elections and the Stock Market: Comparing Markov-Switching and Fractionally Integrated GARCH Models of Volatility." Political Analysis 12, no. 3 (2004): 296–322. http://dx.doi.org/10.1093/pan/mph020.

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Existing research on electoral politics and financial markets predicts that when investors expect left parties—Democrats (US), Labor (UK)—to win elections, market volatility increases. In addition, current econometric research on stock market volatility suggests that Markov-switching models provide more accurate volatility forecasts and fit stock price volatility data better than linear or nonlinear GARCH (generalized autoregressive conditional heteroskedasticity) models. Contrary to the existing literature, we argue here that when traders anticipate that the Democratic candidate will win the presidential election, stock market volatility decreases. Using two data sets from the 2000 U.S. presidential election, we test our claim by estimating several GARCH, exponential GARCH (EGARCH), fractionally integrated exponential GARCH (FIEGARCH), and Markov-switching models. We also conduct extensive forecasting tests—including RMSE and MAE statistics as well as realized volatility regressions—to evaluate these competing statistical models. Results from forecasting tests show, in contrast to prevailing claims, that GARCH and EGARCH models provide substantially more accurate forecasts than the Markov-switching models. Estimates from all the statistical models support our key prediction that stock market volatility decreases when traders anticipate a Democratic victory.

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Chen, Cathy W. S., Khemmanant Khamthong, and Sangyeol Lee. "Markov switching integer‐valued generalized auto‐regressive conditional heteroscedastic models for dengue counts." Journal of the Royal Statistical Society: Series C (Applied Statistics) 68, no. 4 (2019): 963–83. http://dx.doi.org/10.1111/rssc.12344.

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Kučinskas, Simas. "DATING BUSINESS CYCLES IN LITHUANIA BY SIMPLE UNIVARIATE METHODS." Ekonomika 90, no. 2 (2011): 7–27. http://dx.doi.org/10.15388/ekon.2011.0.950.

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In this paper, we use three basic univariate techniques, namely, BBQ algorithm, time series filtering, and Markov-switching models, to date and characterize Lithuanian business cycles from 1995 to 2010. We find that economic growth in Lithuania was relatively balanced after the Russian Crisis until late 2006. After that, the economy experienced an extreme, although relatively brief, period of an overheated economic climate before plunging into a very deep recession at the end of 2008. Using the BBQ algorithm, we provide some simple comparisons of the two recessions as well as international data obtained in other studies. Our Markov-switching regression exercise, confirming the findings above, additionally indicates that recessionary periods may have shocks with non-finite variances and economically significant permanent effects on output.

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Elliott, Robert J., Vikram Krishnamurthy, and Jrn Sass. "Moment based regression algorithms for drift and volatility estimation in continuous-time Markov switching models." Econometrics Journal 11, no. 2 (2008): 244–70. http://dx.doi.org/10.1111/j.1368-423x.2008.00246.x.

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Cassou, Steven P., and Hedieh Shadmani. "Do US Government Tax Revenues and Expenditures Respond to Debt Levels and Economic Conditions Asymmetrically over the Business Cycle?" Applied Economics and Finance 5, no. 3 (2018): 8. http://dx.doi.org/10.11114/aef.v5i3.3004.

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This paper empirically investigates whether there are asymmetries in the responses of US government tax revenue and expenditure to debt levels and economic conditions over the business cycle. State of the art regime switching regression models, including Threshold Regression and Markov Switching, are investigated. Both sides of the government budget show asymmetries, but the asymmetries for tax revenue show greater statistical significance. The results show that both tax revenue and expenditure respond to high debt levels, with the asymmetry in this response showing that fiscal authorities take weaker action in response to debt during poor economic times. In addition, the asymmetric response to economic conditions for both sides of the budget shows that stronger countercyclical policy is taken during poor economic times.

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Liow, KimHiang, and Qing Ye. "Volatility causality and contagion in international securitized real estate markets." Journal of European Real Estate Research 11, no. 2 (2018): 244–168. http://dx.doi.org/10.1108/jerer-11-2017-0042.

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Purpose This paper aims to investigate volatility causality and return contagion on nine international securitized real estate markets by appealing to Markov-switching (MS) regime approach, from July 1992 to June 2014. Design/methodology/approach An MS causality interaction model (Psaradakis et al., 2005), an MS vector auto-regression mode (Krolzig, 1997) and a multivariate return contagion model (Dungey et al., 2005) were used to implement the empirical investigations. Findings There exist regime shifts in the volatility causality pattern, with the volatility causality effects more pronounced during high volatility periods. During high volatility period, real estate markets’ causality interactions and inter-linkages contribute to strong spillover effect that leads to extreme volatility. However, there is relatively limited return contagion evidence in the securitized real estate markets examined. As such, the US financial crisis might probably be due to cross-market interdependence rather than contagion. Research limitations/implications Because international investors incorporate into their portfolio allocation not only the long-run price relationship but also the short-run market volatility connectedness and return correlation structure, the results of this MS causality and contagion study have provided valuable information on the evaluation of regime-dependent securitized real estate market risk, as well as useful guidance on asset allocation and portfolio management decisions for institutional investors. Practical implications Financial crisis is one of the key determinants of cross-market volatility interactions. Portfolio managers should be alerted of the observation that the US and the other developed securitized real estate markets are increasingly sharing “common market cycles” in recent years, thereby diminishing the diversification benefits. For policymakers, this research indicates that the volatilities of the US securitized real estate market could be helpful to predict those of other developed markets. It is also important for them to pay attention to those potential risk factors behind the amplified causality, contagion and volatility spillover at times of crisis. Finally, a wider implication for policymakers is to manage the transmission channels through which global stock market return and volatility shocks can affect the local economies and domestic financial markets, including securitized real estate markets. Originality/value Real estate investments have emerged to show low correlation with stocks and bonds and contributed to portfolio optimization. With real estate that can serve as a type of consumption commodity and an investment tool, the risk-return profile of real estate is different from that of the underlying stock markets. Therefore, the performance and investment dynamics and real estate-stock link are not theoretically expected to be similar, that requires separate empirical investigations. This paper aims to stand out from the many papers on the same or similar topics in the application of the three MS methodologies to regime-dependent real estate market integration.

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Yamamoto, Hiroshi, Shigehiro Ano, and Katsuyuki Yamazaki. "Modeling of Dynamic Latency Variations Using Auto-Regressive Model and Markov Regime Switching for Mobile Network Access on Trains." Journal of Information Processing 23, no. 4 (2015): 420–29. http://dx.doi.org/10.2197/ipsjjip.23.420.

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Makatjane, Katleho, Ntebogang Moroke, and Diteboho Xaba. "On the Prediction of the Inflation Crises of South Africa Using Markov-Switching Bayesian Vector Autoregressive and Logistic Regression Models." Journal of Social Economics Research 5, no. 1 (2018): 10–28. http://dx.doi.org/10.18488/journal.35.2018.51.10.28.

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Badea, Leonardo, Daniel Ştefan Armeanu, Dan Costin Nițescu, Valentin Murgu, Iulian Panait, and Boris Kuzman. "A Study of the Relative Stock Market Performance of Companies Recognized for Supporting Gender Equality Policies and Practices." Sustainability 12, no. 9 (2020): 3558. http://dx.doi.org/10.3390/su12093558.

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This paper explores the relative stock market performance of well-diversified gender equality equity indices in comparison with the overall market, taking both a cross-sectoral and a financial sector approach, for the period January 2017 to March 2020, with a sample of 11 indices and 834 daily observations, and using several different statistical and econometric methods. Our results show a high level of dynamic conditional correlation of daily returns among the gender equality and the overall indices. We also found comparable levels of conditional volatility (resulting from an Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH)model) and an elevated degree of synchronization of the volatility regimes (identified by a Markov switching model). Calibrating simple linear quantile regressions, we found that the value of the slope coefficients of the hypothetical linear relationship between the gender equality indices and the overall market indices are close to one, and relatively stable in relation with the value of the quantile. Using separate Vector Autoregressive (VAR) models for the cross-sectoral indices and for the financial sector indices, we found only very little evidence of causality and spill-over effects. Based on these results, we argue that the daily returns of the gender equality indices exhibited very similar characteristics with the daily returns of the overall market indices. In our interpretation, this could mean that, limited to our sample and methods of investigation, there were not significant differences in the investors’ preferences towards the equity issued by public companies committed to supporting gender equality, in comparison with their approach towards listed equity in general. It could also mean that investors do not yet anticipate the significantly different financial performance of listed companies stemming from their approach towards gender equality.

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Cox, Raymond, Ajit Dayanandan, Han Donker, and John R. Nofsinger. "Confucius confusion: analyst forecast dispersion and business cycles." Review of Behavioral Finance 10, no. 2 (2018): 130–45. http://dx.doi.org/10.1108/rbf-04-2017-0041.

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PurposeFinancial analysts have been found to be overconfident. The purpose of this paper is to study the ramifications of that overconfidence on the dispersion of earnings estimates as a predictor of the US business cycle.Design/methodology/approachWhether aggregate analyst forecast dispersion contains information about turning points in business cycles, especially downturns, is examined by utilizing the analyst earnings forecast dispersion metric. The primary analysis derives from logit regression and Markov switching models. The analysis controls for sentiment (consumer confidence), output (industrial production), and financial indicators (stock returns and turnover). Analyst data come from Institutional Brokers Estimate System, while the economic data are available at the Federal Reserve Bank of St Louis Economic Data site.FindingsA rise in the dispersion of analyst forecasts is a significant predictor of turning points in the US business cycle. Financial analyst uncertainty of earnings estimate contains crucial information about the risks of US business cycle turning points. The results are consistent with some analysts becoming overconfident during the expansion period and misjudging the precision of their information, thus over or under weighting various sources of information. This causes the disagreement among analysts measured as dispersion.Originality/valueThis is the first study to show that analyst forecast dispersion contributions valuable information to predictions of economic downturns. In addition, that dispersion can be attributed to analyst overconfidence.

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Hauzenberger, Niko, Florian Huber, Michael Pfarrhofer, and Thomas O. Zörner. "Stochastic model specification in Markov switching vector error correction models." Studies in Nonlinear Dynamics & Econometrics, February 24, 2020. http://dx.doi.org/10.1515/snde-2018-0069.

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AbstractThis paper proposes a hierarchical modeling approach to perform stochastic model specification in Markov switching vector error correction models. We assume that a common distribution gives rise to the regime-specific regression coefficients. The mean as well as the variances of this distribution are treated as fully stochastic and suitable shrinkage priors are used. These shrinkage priors enable to assess which coefficients differ across regimes in a flexible manner. In the case of similar coefficients, our model pushes the respective regions of the parameter space towards the common distribution. This allows for selecting a parsimonious model while still maintaining sufficient flexibility to control for sudden shifts in the parameters, if necessary. We apply our modeling approach to real-time Euro area data and assume transition probabilities between expansionary and recessionary regimes to be driven by the cointegration errors. The results suggest that the regime allocation is governed by a subset of short-run adjustment coefficients and regime-specific variance-covariance matrices. These findings are complemented by an out-of-sample forecast exercise, illustrating the advantages of the model for predicting Euro area inflation in real time.

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De Angelis, Luca, and Cinzia Viroli. "A Markov-switching regression model with non-Gaussian innovations: estimation and testing." Studies in Nonlinear Dynamics & Econometrics 21, no. 2 (2017). http://dx.doi.org/10.1515/snde-2015-0118.

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AbstractIn this paper we propose a very general multivariate Markov-switching regression (MSR) model considering the normal inverse Gaussian (NIG) distribution as conditional form of financial returns and model innovations. It is indeed well-known that the Gaussian distribution is not able to capture many stylized facts of the return series such as skewness, excess kurtosis and heavy tails. Through a large simulation study and an empirical analysis of the US stock market, we show that a NIG-based MSR model allows to adequately account for both skewness and fat tails in the data and, according to model selection criteria, is the best overall model in the majority of the cases considered, even preferred over other popular distributional assumptions such as Student-

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Kim, Young Min, and Kyu Ho Kang. "Bayesian Inference of Multivariate Regression Models with Endogenous Markov Regime-Switching Parameters*." Journal of Financial Econometrics, September 20, 2020. http://dx.doi.org/10.1093/jjfinec/nbaa021.

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Abstract This study introduces a multivariate regression model with endogenous Markov regime-switching parameters, in which the regression disturbances and regime switches are allowed to be instantaneously correlated. For the estimation and model comparison, we develop a posterior sampling algorithm for the parameters, regimes, and marginal likelihood calculation. We demonstrate the reliability of the proposed method using simulation and empirical studies. The simulation study shows that neglecting the endogeneity leads to inaccurate parameter estimates, and that our marginal likelihood comparison chooses a correctly specified model. In the business cycle application, we find that the joint dynamics of the U.S. industrial production index (IPI) growth and unemployment rates are subject to three-state endogenous regime shifts. Another application to stock and bond return data suggests that negative shocks to the stock return seem to cause regime shifts from a low volatility state to a high volatility state of the financial markets. (JEL: C11, C53, E43, G12)

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Akram, Vaseem, and Badri Narayan Rath. "Fiscal sustainability in India: evidence from Markov switching and threshold regression models." Studies in Economics and Finance ahead-of-print, ahead-of-print (2019). http://dx.doi.org/10.1108/sef-09-2018-0281.

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PurposeThe purpose of this study is to examine the fiscal sustainability issue by dividing the fiscal deficit into high and low regimes using the quarterly data from 1997: Q1 to 2013: Q3. Further, we obtain the optimum level of public debt at which fiscal sustainability can be achieved.Design/methodology/approachThis study uses the Markov Switching-Vector Error Correction Model (MS-VECM) for examining fiscal sustainability and threshold regression model to obtain the optimum level of debt.FindingsThe results derived from MS-VECM reveal the evidence in favor of fiscal sustainability during low fiscal deficit periods. Similarly, using a threshold regression model, the optimum public debt as a percentage to GDP seems to be around 21 per cent on a quarterly basis, beyond this level, public debt hurts economic growth.Practical implicationsFrom the policy front, the government of India should cut down the fiscal deficit only if debt reaches beyond a threshold level.Originality/valueNoting that the vast literature has focused on examining the fiscal sustainability in India, the novelty of this study is to examine the fiscal sustainability by considering high and low deficits regimes using a non-linear approach.

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Khashanah, Khaldoun, and Chenjie Shao. "Short-term volatility forecasting with kernel support vector regression and Markov switching multifractal model." Quantitative Finance, July 12, 2021, 1–13. http://dx.doi.org/10.1080/14697688.2021.1939116.

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Journal articles on the topic 'Markov-switching regression model'

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