Transition Assessment of the Bangladeshi Financial Market Stress Regimes: A Markov Switching Modeling Approach

Maria Afreen (1)
(1) PhD (Financial Economics), Faculty of Economics and Business, University Malaysia Sarawak, Malaysia. , Bangladesh


Purpose of this study: In view of the global financial crises and the ensuing consequences, this research presents the utility of demonstrating an assessment that can forecast the Bangladeshi financial market’s well-being by analysing episodes of economic crises which may prevent market distress. By graphically demonstrating eventual economic episodes in the financial sector, this study sets out to illustrate the chronological scenario of economic turning points. The scope of this research is to study the vulnerable aspects of financial instability in Bangladesh and seek possible remedies.

Methodology: The Bangladeshi financial market regimes will be constructed based on Hamilton's Markov Switching Model (1989). This paper is the first attempt in utilising a standardised methodology found in business cycle literatures so as to determine the turning points of economic episodes in the Bangladeshi financial dynamic cycle.

Main Findings: This study examines the financial crises and economic distress experienced by banks as forms of economic vulnerabilities. Thus, it describes the financial regimes of transition period movements in the context of the vulnerability of the Bangladeshi financial market sector using the Markov Switching Modeling (MSM) Approach and shows ways to possibly achieve recovery.

Research Limitations/Implications: This research focuses on the current financial episodes of the economic sector’s dynamic movements in a condensed area, while the selection of a broad financial arena of parameters results in more significant and robust outcomes.

Novelty/Originality: Further studies are needed to define and measure the financial cycle concept and its relationship with business cycles, as well as to delineate dynamic models that can offer substantial probabilistic assessments regarding changes in financial cycle regimes. This can significantly develop the capability of the financial market supervisory authorities to forecast macro-prudential systemic risks and to avoid or reduce the consequences of economic crises. This current study provides a platform for future studies in similar fields.


Full Article

Generated from XML file


Abiad, A. (2007): “Early Warning Systems for Currency Crises: A Regime-Switching Approach,” in Hidden Markov Models in Finance, ed. by R. S. Mamon and R. Elliott, New York: Springer, chap. 10, 155–185.

Arias, G. and U. Erlandsson (2004): “Regime switching as an alternative early warning system of currency crises - an application to south-east asia,” Lund University Working Paper.

Afreen, M. (2020). Review Paper on Composite Leading Index Creation for Forecasting the Bangladeshi Financial Sector. International Journal of Finance & Banking Studies, 9(4), 23–32.

Augustyniak, M. (2014). Maximum likelihood estimation of the Markov-switching GARCH model. Computational Statistics & Data Analysis, Elsevier, 76(2), 61–75.

Brunetti, C., R. S. Mariano, C. Scotti, and A. H. Tan (2007): “Markov switching garch models of currency turmoil in southeast asia,” Federal Reserve Board, International Finance Discussion Papers No. 889.

Cerra, V. and S. C. Saxena (2002): “Contagion, monsoons, and domestic turmoil in Indonesia’s currency crisis,” Review of International Economics, 10, 36–44.

Diebold, F. X., J.-H. Lee, and G. C. Weinbach (1994): “Regime switching with time-varying transition probabilities,” in Nonstationary Time Series Analysis and Cointegration, ed. by C. Hargreaves, Oxford University Press, 283–302.

Duprey, T., B. Klaus, and T. A. Peltonen (2015): “Dating systemic financial stress episodes in the EU countries,” ECB Working Paper No. 1873.

Engel, C. and C. S. Hakkio (1996): “The distribution of exchange rates in the EMS,” International Journal of Finance and Economics, 1, 55–67.

Filardo, A. (1994): “Business-cycle phases and their transitional dynamics,” Journal of Business and Economic Statistics, 12, 299–308.

Gadea-Rivas, M. D. and G. Perez-Quiros (2015): “The failure to predict the great recession - A view through the role of credit,” Journal of the European Economic Association, 13, 534–559.

Goldfeld, S., & Quandt, R. (1972). Nonlinear methods in econometrics. Amsterdam: North-Holland Publ. Co., Amsterdam and London.

Hamilton, J. D. (1989). A new approach to the economic analysis of non-stationary time series and the business cycle. Econometrica, 57(2), 357-384.

Hartmann, P., K. Hubrich, M. Kremer, and R. J. Tetlow (2013): “Melting down: Systemic financial instability and the macroeconomy,” Working Paper.

Hollo, D., M. Kremer, and M. Lo Duca (2012): “CISS - a composite indicator of systemic stress in the financial system,” ECB Working Paper No. 1426.

Kuan, C. M. (2002). Lecture on Markov Switching Model. Institute of Economics, Academia Sinica, 8(15), 1-30.

Maria, A. (2020) " Construction of an Industry Cycle Indicator for Profitability Prediction Analysis of Aggregate Firms in Bangladesh. International Journal of Social Sciences and Economic Review. 2(4), 9-18.

Martinez-Peria, M. S. (2002). “A regime-switching approach to the study of speculative attacks: A focus on EMS crises,” Empirical Economics, 27, 299–334.

Maria, A. (2020). Analysing the Return on Asset to Construct Foretelling Indicator for Bangladeshi Banking Sector. International Journal of Finance & Banking Studies. 9(4), 11–22.

Maria, A. (2020). Building Vulnerability Predictive Indicator for the Banking Sector: Perspective of Bangladesh. International Journal of Finance & Banking Studies. 9(3), 01–14.

Romer, C. D. and Romer. D. H. (2015). “New evidence on the impact of financial crises in advanced countries,” NBER Working Paper No. 21021.

Schularick, M. H. and Taylor, A. M. (2012). “Credit Booms Gone Bust: Monetary Policy, Leverage Cycles, and Financial Crises, 1870-2008,” American Economic Review, 102, 1029–1061.

Simorangkir, I. (2012). “Early warning indicators study of bank runs in Indonesia : Markov-switching approach,” Bulletin of Monetary Economics and Banking, 15, 3–39.

Turner, M., Startz, H., & Nelson, R. (1989). A Markov Model of heteroskedasticity, risk, and learning in the stock market. Journal of Financial Economics, 25(1989), 3-22.

Thibaut, D. & Benjamin, K. (2017). How to predict financial stress? An assessment of Markov switching models. European Central Bank. ECB Working Paper Series. No: 2057.


Maria Afreen (Primary Contact)
Author Biography

Maria Afreen, PhD (Financial Economics), Faculty of Economics and Business, University Malaysia Sarawak, Malaysia.

 Dr. Maria Afreen Completed her Ph.D. in "Financial Economics" from University Malaysia Sarawak, Malaysia in 2018 with a merit-based scholarship. She has expertise in data analysis in the areas of Volatility Forecasting & Macro-economic Modeling, Credit Risk Management, Constructing Risk Indicator & Filtering tools. She has a remarkable number of high impact factor indexed peer-reviewed journal publication records at the international level.

Afreen, M. (2021). Transition Assessment of the Bangladeshi Financial Market Stress Regimes: A Markov Switching Modeling Approach. Innovation Journal of Social Sciences and Economic Review, 3(1), 07–11.

Article Details

Smart Citations via scite_