Impact Of Volatility And Performance Of Major Stock Markets On Sarajevo Stock Exchange In 2008 – 2012 Period

Previous research indicates that performance and volatility of small and regional stock markets can be influenced by the performance of major world exchanges such as New York, Frankfurt or Tokyo stock exchange. This research analyses weekly composite index data for SASE (Sarajevo Stock Exchange), NYSE, NIKKEI, and DAX indices, for the period from 2008 until the end of 2012. This time period contains significant events in the US and the rest of the world, including the housing bubble, and a great recession which followed after. Significant volatility of SASE was noted in 2007 while later periods suggest lesser volatility after a significant drop in index value in mid 2007. The data was analyzed in a side by side comparison, by the method of regression in order to establish a correlation of NYSE, NIKKEI and DAX indexes with Sarajevo Stock Exchange index. Furthermore the performance was visually represented, segmented into several dynamic and steady periods, whose regressions were separately calculated, in order to see the difference in steady and dynamic periods. Previous research suggests strong correlation between regional and major stock market indices at times of crisis, a so called spillover effect, while low correlation at times of low volatility. With these results, we will be able to understand the impact of major world indices on volatility and performance movements of Sarajevo Stock Exchange in the long and short run, as well as at times of low and high volatility. The results of research suggest that when there is less dynamics in major world indices, the SASE market becomes less affected by their results and by the global market trends, thus its performance is then dictated to a higher degree by regional or country specific financial, economic and to some degree political factors. On the other hand we can also deduce that when there are significant events developing in these major world indices, SASE’s composite index performance are highly correlated to the dynamics and trends of major world indices. One such case this paper analyzed is evident in the ‘dynamic period’ of some 18 months, ranging from 01.01.2009-16.06.2010, where the impact of global recession on major world indexes spilled over to smaller regional exchanges; correlation between SASE and NYSE in that period is 0,92

Impact Of Volatility And Performance Of Major Stock Markets On Sarajevo Stock Exchange In 2008 – 2012 Period

Previous research indicates that performance and volatility of small and regional stock markets can be influenced by the performance of major world exchanges such as New York, Frankfurt or Tokyo stock exchange. This research analyses weekly composite index data for SASE (Sarajevo Stock Exchange), NYSE, NIKKEI, and DAX indices, for the period from 2008 until the end of 2012. This time period contains significant events in the US and the rest of the world, including the housing bubble, and a great recession which followed after. Significant volatility of SASE was noted in 2007 while later periods suggest lesser volatility after a significant drop in index value in mid 2007. The data was analyzed in a side by side comparison, by the method of regression in order to establish a correlation of NYSE, NIKKEI and DAX indexes with Sarajevo Stock Exchange index. Furthermore the performance was visually represented, segmented into several dynamic and steady periods, whose regressions were separately calculated, in order to see the difference in steady and dynamic periods. Previous research suggests strong correlation between regional and major stock market indices at times of crisis, a so called spillover effect, while low correlation at times of low volatility. With these results, we will be able to understand the impact of major world indices on volatility and performance movements of Sarajevo Stock Exchange in the long and short run, as well as at times of low and high volatility. The results of research suggest that when there is less dynamics in major world indices, the SASE market becomes less affected by their results and by the global market trends, thus its performance is then dictated to a higher degree by regional or country specific financial, economic and to some degree political factors. On the other hand we can also deduce that when there are significant events developing in these major world indices, SASE’s composite index performance are highly correlated to the dynamics and trends of major world indices. One such case this paper analyzed is evident in the ‘dynamic period’ of some 18 months, ranging from 01.01.2009-16.06.2010, where the impact of global recession on major world indexes spilled over to smaller regional exchanges; correlation between SASE and NYSE in that period is 0,92

Soft Data Modeling via Type 2 Fuzzy Distributions for Corporate Credit Risk Assessment in Commercial Banking

The work reported in this paper aims to present possibility distribution model of soft data used for corporate client credit risk assessment in commercial banking by applying Type 2 fuzzy membership functions (distributions) for the purpose of developing a new expert decision-making fuzzy model for evaluating credit risk of corporate clients in a bank. The paper is an extension of previous research conducted on the same subject which was based on Type 1 fuzzy distributions. Our aim in this paper is to address inherent limitations of Type 1 fuzzy dis-tributions so that broader range of banking data uncertainties can be handled and combined with the corresponding hard data, which all affect banking credit deci-sion making process. Banking experts were interviewed about the types of soft variables used for credit risk assessment of corporate clients, as well as for providing the inputs for generating Type 2 fuzzy logic membership functions of these soft variables. Similar to our analysis with Type 1 fuzzy distributions, all identified soft variables can be grouped into a number of segments, which may depend on the specific bank case. In this paper we looked into the following segments: (i) stability, (ii) capability and (iii) readiness/willingness of the bank client to repay a loan. The results of this work represent a new approach for soft data modeling and usage with an aim of being incorporated into a new and superior soft-hard data fusion model for client credit risk assessment

Fuzzy Logic Model of Soft Data Analysis for Corporate Client Credit Risk Assessment in Commercial Banking

This paper deals with the use of fuzzy logic as a support tool for evaluation of corporate client credit risk in a commercial banking environment. It defines possibilistic distribution of soft data used for corporate client credit risk assessment by applying fuzzy logic modeling, with a major goal to develop a new expert decisionmaking fuzzy model for evaluating credit risk of corporate clients in a bank. Currently, predicting a credit risk of companies is inaccurate and ambiguous, as well as affected by many internal and external factors that cannot be precisely defined. Unlike traditional methods for credit risk assessment, fuzzy logic can easily incorporate linguistic terms and expert opinions which makes it more adapted to cases with insufficient and imprecise hard data, as well as for modeling risks that are not fully understood. Fuzzy model of soft data, presented in this paper, is created based on expert experience of corporate lending of a commercial bank in Bosnia and Herzegovina. This market is very small and it behaves irrationally and often erratically and therefore makes the risk assessment and management decision making process very complex and uncertain which requires new methods for risk modeling to be evaluated. Experts were interviewed about the types of soft variables used for credit risk assessment of corporate clients, as well as for providing the inputs for generating membership functions of these soft variables. All identified soft variables can be grouped into following segments: stability, capability and readiness/willingness of the client to repay a loan. The results of this work represent a new approach for soft data usage/assessment with an aim of being incorporated into a new and superior soft-hard data fusion model for client credit risk assessment

Fuzzy Logic Model of Soft Data Analysis for Corporate Client Credit Risk Assessment in Commercial Banking

This paper deals with the use of fuzzy logic as a support tool for evaluation of corporate client credit risk in a commercial banking environment. It defines possibilistic distribution of soft data used for corporate client credit risk assessment by applying fuzzy logic modeling, with a major goal to develop a new expert decisionmaking fuzzy model for evaluating credit risk of corporate clients in a bank. Currently, predicting a credit risk of companies is inaccurate and ambiguous, as well as affected by many internal and external factors that cannot be precisely defined. Unlike traditional methods for credit risk assessment, fuzzy logic can easily incorporate linguistic terms and expert opinions which makes it more adapted to cases with insufficient and imprecise hard data, as well as for modeling risks that are not fully understood. Fuzzy model of soft data, presented in this paper, is created based on expert experience of corporate lending of a commercial bank in Bosnia and Herzegovina. This market is very small and it behaves irrationally and often erratically and therefore makes the risk assessment and management decision making process very complex and uncertain which requires new methods for risk modeling to be evaluated. Experts were interviewed about the types of soft variables used for credit risk assessment of corporate clients, as well as for providing the inputs for generating membership functions of these soft variables. All identified soft variables can be grouped into following segments: stability, capability and readiness/willingness of the client to repay a loan. The results of this work represent a new approach for soft data usage/assessment with an aim of being incorporated into a new and superior soft-hard data fusion model for client credit risk assessment