The paper focuses on the interaction between the solvency probability of a banking firm and the diversification potential of its asset portfolio when determining optimal equity capital. The purpose of this paper is to incorporate value at risk (VaR) into the firm-theoretical model of a banking firm facing the risk of asset return. Given the necessity to achieve a confidence level for solvency, we demonstrate that diversification reduces the amount of equity. Notably, the VaR concept excludes a separation of equity policy and asset-liability management.
{"title":"Banking Firm, Equity and Value at Risk","authors":"Udo Broll, A. Sobiech, J. Wahl","doi":"10.5709/CE.1897-9254.67","DOIUrl":"https://doi.org/10.5709/CE.1897-9254.67","url":null,"abstract":"The paper focuses on the interaction between the solvency probability of a banking firm and the diversification potential of its asset portfolio when determining optimal equity capital. The purpose of this paper is to incorporate value at risk (VaR) into the firm-theoretical model of a banking firm facing the risk of asset return. Given the necessity to achieve a confidence level for solvency, we demonstrate that diversification reduces the amount of equity. Notably, the VaR concept excludes a separation of equity policy and asset-liability management.","PeriodicalId":203996,"journal":{"name":"ERN: Value-at-Risk (Topic)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129640181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since the large number of parties involved in corporate failure or ‘business failure’, the avoidance of failure has always been an important issue in the field of corporate finance and business management. In this paper, the model was developed to predict business failure in Thailand particular in technology industry by using four variables from Altman’s model and adding one variable to the model. Descriptive statistics, correlation, and independent T-test are used for testing to see the characteristics of each variable on both failed and non-failed companies. The model was developed by using the stepwise logistic regression. Samples were developed by using financial information from private limited companies based on technology industry in Bangkok. The result from this empirical study can conclude that financial ratios are useful analytical techniques for forecasting financial health of companies in technology industry. The result of independent T-test has pointed out sales to total assets ratio is the only significant independent variable indicating significant differences between failed and non-failed group. The Nagelkerke R2 indicated 42.4% of the variation in the outcome variable. The predictability accuracy of the model is 77.8% which is under 95% confidence level.
{"title":"Logistic Regression Model for Business Failures Prediction of Technology Industry in Thailand","authors":"S. Puagwatana, K. Gunawardana","doi":"10.2139/ssrn.2932026","DOIUrl":"https://doi.org/10.2139/ssrn.2932026","url":null,"abstract":"Since the large number of parties involved in corporate failure or ‘business failure’, the avoidance of failure has always been an important issue in the field of corporate finance and business management. In this paper, the model was developed to predict business failure in Thailand particular in technology industry by using four variables from Altman’s model and adding one variable to the model. Descriptive statistics, correlation, and independent T-test are used for testing to see the characteristics of each variable on both failed and non-failed companies. The model was developed by using the stepwise logistic regression. Samples were developed by using financial information from private limited companies based on technology industry in Bangkok. The result from this empirical study can conclude that financial ratios are useful analytical techniques for forecasting financial health of companies in technology industry. The result of independent T-test has pointed out sales to total assets ratio is the only significant independent variable indicating significant differences between failed and non-failed group. The Nagelkerke R2 indicated 42.4% of the variation in the outcome variable. The predictability accuracy of the model is 77.8% which is under 95% confidence level.","PeriodicalId":203996,"journal":{"name":"ERN: Value-at-Risk (Topic)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133936601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The aim of the paper is to propose a model, namely Delta-Gamma Sensitivity Analysis-Extreme Value Theory (DGSA-EVT). DGSA-EVT is a model to measure HF-LS and LF-HS type of operational risks. The first leg of the proposed model, namely DGSA, is a methodology that deals with propagation of errors in the value adding activities which works by using measures of fluctuations in the activities.The sensitivities of the output, hence, are deployed to estimate the performance volatility. Furthermore, the second leg of the proposed model, Extreme Value Theory (EVT), is a technique to cater for an excess operational loss over a defined threshold which is normally characterized by low frequency and high severity (LF-HS) type of loss.
{"title":"Measuring Operational Risk Exposures in Islamic Banking: A Proposed Measurement Approach","authors":"Hylmun Izhar","doi":"10.2139/ssrn.1906569","DOIUrl":"https://doi.org/10.2139/ssrn.1906569","url":null,"abstract":"The aim of the paper is to propose a model, namely Delta-Gamma Sensitivity Analysis-Extreme Value Theory (DGSA-EVT). DGSA-EVT is a model to measure HF-LS and LF-HS type of operational risks. The first leg of the proposed model, namely DGSA, is a methodology that deals with propagation of errors in the value adding activities which works by using measures of fluctuations in the activities.The sensitivities of the output, hence, are deployed to estimate the performance volatility. Furthermore, the second leg of the proposed model, Extreme Value Theory (EVT), is a technique to cater for an excess operational loss over a defined threshold which is normally characterized by low frequency and high severity (LF-HS) type of loss.","PeriodicalId":203996,"journal":{"name":"ERN: Value-at-Risk (Topic)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121460679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a rating methodology that is based on a non-linear classification method, the support vector machine, and a non-parametric technique for mapping rating scores into probabilities of default. We give an introduction to underlying statistical models and represent the results of testing our approach on Deutsche Bundesbank data. In particular we discuss the selection of variables and give a comparison with more traditional approaches such as discriminant analysis and the logit regression. The results demonstrate that the SVM has clear advantages over these methods for all variables tested.
{"title":"Estimating Probabilities of Default with Support Vector Machines","authors":"W. Härdle, R. Moro, Dorothea Schaefer","doi":"10.18452/14066","DOIUrl":"https://doi.org/10.18452/14066","url":null,"abstract":"This paper proposes a rating methodology that is based on a non-linear classification method, the support vector machine, and a non-parametric technique for mapping rating scores into probabilities of default. We give an introduction to underlying statistical models and represent the results of testing our approach on Deutsche Bundesbank data. In particular we discuss the selection of variables and give a comparison with more traditional approaches such as discriminant analysis and the logit regression. The results demonstrate that the SVM has clear advantages over these methods for all variables tested.","PeriodicalId":203996,"journal":{"name":"ERN: Value-at-Risk (Topic)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121348987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Theorem 15 of Embrechts et al. [Embrechts, Paul, Hoing, Andrea, Puccetti, Giovanni, 2005. Worst VaR scenarios. Insurance: Math. Econom. 37, 115-134] proves that comonotonicity gives rise to the on-average-most-adverse Value-at-Risk scenario for a function of dependent risks, when the marginal distributions are known but the dependence structure between the risks is unknown. This note extends this result to the case where, rather than no information, partial information is available on the dependence structure between the risks. A result of Kaas et al. [Kaas, Rob, Dhaene, Jan, Goovaerts, Marc J., 2000. Upper and lower bounds for sums of random variables. Insurance: Math. Econom. 23, 151-168] is also generalized for this purpose.
{"title":"Worst VAR Scenarios: A Remark","authors":"Roger J. A. Laeven","doi":"10.2139/ssrn.887178","DOIUrl":"https://doi.org/10.2139/ssrn.887178","url":null,"abstract":"Theorem 15 of Embrechts et al. [Embrechts, Paul, Hoing, Andrea, Puccetti, Giovanni, 2005. Worst VaR scenarios. Insurance: Math. Econom. 37, 115-134] proves that comonotonicity gives rise to the on-average-most-adverse Value-at-Risk scenario for a function of dependent risks, when the marginal distributions are known but the dependence structure between the risks is unknown. This note extends this result to the case where, rather than no information, partial information is available on the dependence structure between the risks. A result of Kaas et al. [Kaas, Rob, Dhaene, Jan, Goovaerts, Marc J., 2000. Upper and lower bounds for sums of random variables. Insurance: Math. Econom. 23, 151-168] is also generalized for this purpose.","PeriodicalId":203996,"journal":{"name":"ERN: Value-at-Risk (Topic)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116928920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Turkish Abstract: Operasyonel risk, kredi ve piyasa riski gibi ölçümü ve yönetimi gerçekleştirilen diğer risklerden oldukça farklı özelliklere sahiptir. Bu nedenle operasyonel riskin ölçümü, halen finansal risklerin ölçümünde kullanılan yöntemlerden farklı ve daha karmaşık yöntemlerin kullanılmasını gerektirmektedir. Çalışmanın temel amacı, operasyonel risklerin stokastik yöntemler kullanılarak ölçümü kapsamında aktüeryal matematik modellerine dayanan Kayıp Dağılımları Yaklaşımının (KDY) metodolojik çerçevesinin geliştirilerek operasyonel risklerin ölçümünde, yönetiminde ve gerekli sermayenin tahsis edilmesinde kullanımına uygun hale getirilmesidir. Çalışmada, öncelikle KDY konusundaki yazın aktarılmış, KDY’nin teorik çerçevesi çizilmiş, çalışmanın yöntem ve kapsamı belirlenerek veri modeli oluşturulmuştur. Oluşturulan ölçüm modelinde operasyonel risk “büyüklük” ve “sıklık” olmak üzere iki farklı stokastik süreçte ele alınmıştır. Ayrı ayrı modellenen büyüklük ve sıklık süreçleri bir araya getirilerek “Toplam Kayıp Modeli” oluşturulmuş, bu model kullanılarak operasyonel riske maruz değer (RMD) hesaplanmıştır. Model, doğru ve güvenilir tahminler yapabilme kabiliyetinin belirlenebilmesi amacıyla geriye dönük teste tabi tutulmuştur. English Abstract: Operational risk has unique features in comparison to other measurable and manageable risks. For this reason, measurement of operational risk requires considerably different and more sophisticated quantitative methods and techniques than the ones currently used in the measurement of financial risks. Within the context of measuring operational risk through stochastic models, in this research, it has been attempted to develop a methodological framework of Loss Distribution Approach (LDA), which originated from actuarial mathematical models. During the research, the LDA is developed and turned out to be suitable for the measurement and management of operational risk and capital allocation. In this research, after a comprehensive literature review and a discussion of the theoretical background of the LDA, the extent and methodology of the research have been given and data issues have been handled. In order to represent the unique features of operational risks, measurement model has been constructed by two stochastic processes namely “severity” and “frequency” of loss events. These two processes have been modeled separately and then brought together to form an aggregate loss model. Using this model, operational VaR has been estimated. Then, operational VaR estimates have been back tested in order to determine the accuracy and reliability of the aggregate loss models.
{"title":"Operational Risk Measurement: Loss Distributions Approaches (Operasyonel Risk Ölçümü: Kayıp Dağılımları Modellemesi)","authors":"Murat Mazibas","doi":"10.2139/ssrn.3008362","DOIUrl":"https://doi.org/10.2139/ssrn.3008362","url":null,"abstract":"<b>Turkish Abstract:</b> Operasyonel risk, kredi ve piyasa riski gibi ölçümü ve yönetimi gerçekleştirilen diğer risklerden oldukça farklı özelliklere sahiptir. Bu nedenle operasyonel riskin ölçümü, halen finansal risklerin ölçümünde kullanılan yöntemlerden farklı ve daha karmaşık yöntemlerin kullanılmasını gerektirmektedir. Çalışmanın temel amacı, operasyonel risklerin stokastik yöntemler kullanılarak ölçümü kapsamında aktüeryal matematik modellerine dayanan Kayıp Dağılımları Yaklaşımının (KDY) metodolojik çerçevesinin geliştirilerek operasyonel risklerin ölçümünde, yönetiminde ve gerekli sermayenin tahsis edilmesinde kullanımına uygun hale getirilmesidir. Çalışmada, öncelikle KDY konusundaki yazın aktarılmış, KDY’nin teorik çerçevesi çizilmiş, çalışmanın yöntem ve kapsamı belirlenerek veri modeli oluşturulmuştur. Oluşturulan ölçüm modelinde operasyonel risk “büyüklük” ve “sıklık” olmak üzere iki farklı stokastik süreçte ele alınmıştır. Ayrı ayrı modellenen büyüklük ve sıklık süreçleri bir araya getirilerek “Toplam Kayıp Modeli” oluşturulmuş, bu model kullanılarak operasyonel riske maruz değer (RMD) hesaplanmıştır. Model, doğru ve güvenilir tahminler yapabilme kabiliyetinin belirlenebilmesi amacıyla geriye dönük teste tabi tutulmuştur. <b>English Abstract:</b> Operational risk has unique features in comparison to other measurable and manageable risks. For this reason, measurement of operational risk requires considerably different and more sophisticated quantitative methods and techniques than the ones currently used in the measurement of financial risks. Within the context of measuring operational risk through stochastic models, in this research, it has been attempted to develop a methodological framework of Loss Distribution Approach (LDA), which originated from actuarial mathematical models. During the research, the LDA is developed and turned out to be suitable for the measurement and management of operational risk and capital allocation. In this research, after a comprehensive literature review and a discussion of the theoretical background of the LDA, the extent and methodology of the research have been given and data issues have been handled. In order to represent the unique features of operational risks, measurement model has been constructed by two stochastic processes namely “severity” and “frequency” of loss events. These two processes have been modeled separately and then brought together to form an aggregate loss model. Using this model, operational VaR has been estimated. Then, operational VaR estimates have been back tested in order to determine the accuracy and reliability of the aggregate loss models.","PeriodicalId":203996,"journal":{"name":"ERN: Value-at-Risk (Topic)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128886215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In financial literature, Value-at-Risk (VaR) and Expected Shortfall (ES) modelling is focused on producing 1-step ahead conditional variance forecasts. The present paper provides a methodological contribution to the multi-step VaR and ES forecasting through a new adaptation of the Monte Carlo simulation approach for forecasting multi-period volatility to a fractionally integrated GARCH framework for leptokurtic and asymmetrically distributed portfolio returns. Accounting for long memory within the conditional variance process with skewed Student-t (skT) conditionally distributed innovations, accurate 95% and 99% VaR and ES forecasts are calculated for multi-period time horizons. The results show that the FIGARCH-skT model has a superior multi-period VaR and ES forecasting performance.
{"title":"A Monte Carlo Simulation Approach to Forecasting Multi-period Value-at-Risk and Expected Shortfall Using the FIGARCH-skT Specification","authors":"Stavros Degiannakis, P. Dent, Christos Floros","doi":"10.2139/ssrn.3259844","DOIUrl":"https://doi.org/10.2139/ssrn.3259844","url":null,"abstract":"In financial literature, Value-at-Risk (VaR) and Expected Shortfall (ES) modelling is focused on producing 1-step ahead conditional variance forecasts. The present paper provides a methodological contribution to the multi-step VaR and ES forecasting through a new adaptation of the Monte Carlo simulation approach for forecasting multi-period volatility to a fractionally integrated GARCH framework for leptokurtic and asymmetrically distributed portfolio returns. Accounting for long memory within the conditional variance process with skewed Student-t (skT) conditionally distributed innovations, accurate 95% and 99% VaR and ES forecasts are calculated for multi-period time horizons. The results show that the FIGARCH-skT model has a superior multi-period VaR and ES forecasting performance.","PeriodicalId":203996,"journal":{"name":"ERN: Value-at-Risk (Topic)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127159633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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