Abstract
Abstract
 For risk and capital measurement, banks and other financial institutions need to meet forthcoming regulatory requirements. However, it is a serious issue to think that meeting regulatory requirements is the sole or even the most important reason for establishing a scientific, sound risk management system. To direct capital to activities with the best risk/reward ratios, managers need reliable risk measures. To stay within the limits imposed by readily available liquidity, by creditors, customers, and regulators, they need estimates of the size of potential losses. They need mechanisms to monitor positions and create incentives for prudent risk-taking by divisions and individuals. Risk measurement deals with the quantification of risk exposures, whereas risk management refers to the overall process by which managers satisfy these needs and follows to define a business strategy, to detect the risks to which are visible, quantifying those risks, and to control and understand the nature of the risks it faces. This research focuses on the economic vulnerability faced by banks in the financial sector in terms of the crises issues perspective of economic distress. Here, the methodology followed is based on the CAMELS framework variables. CAMELS is an abbreviation for: capital adequacy (C), asset (A), management (M), earnings (E), liquidity (L) and sensitivity to market risk (S). Based on these terminologies, a couple of variables should be selected, such as capital asset ratio, non-performing loan, cost income ratio, industry production index, non-interest income, reserve of gold, inflation, stock turnover ratio, real interest rate as component series and return on equity (RoE) as reference series to identify the turning points of economic vulnerability in the banking sector in Bangladesh. Thus, by forecasting the directional changes it could make policymakers aware of changes in the financial markets and banking economy and allow them to undertake preventive steps for remedial purposes. The constructed MPI should have a remarkable lead time of about not less than 6 months on average in case of prediction against the leading for reference Series.By mending the financial efficacy of investment banks. Bangladesh also should improve their corresponding banking system to implement these suggestions.
Highlights
The incidence of the financial crises worldwide over past two decades has raised concerns about all the interdependence with other sectors of the economy and stability of financial system of an economy
We present the construction under the assumption of this optimal approximation that by a pure economic background analysis, the data is generated in regard of Bangladesh banking economy perspective
In predicting the movement of the financial market cycle in Bangladesh with a prominent lead time and reliability as an early signalling tool, the constructed macro-prudential indicator (MPI) demonstrated a strong ability to work as a predictor for the financial market economy roadmap in Bangladesh
Summary
The incidence of the financial crises worldwide over past two decades has raised concerns about all the interdependence with other sectors of the economy and stability of financial system of an economy. Following the worldwide financial crises during 2008-09, ensuring and monitoring financial system in accordance to stability has become the overarching goal of central banks around the world. According to European Central Bank (2007) financial stability is a condition in which all the financial system consisting of markets, financial intermediaries and market infrastructure is capable of withstanding shocks and the sorting out of financial imbalances, thereby mitigating all the likelihood of disruptions as overall the financial intermediary process which are extreme enough to significantly impair all the allocation of savings towards profitable investment opportunities. MPI consists of CAMELS (capital adequacy, asset quality, management, earnings, liquidity, and sensitivity to market risk) frameworks. CAMELS frameworks that will be used in MPI enable prediction for crisis detection of financial markets banks
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