This paper analyses operational risk events from the ORIC database for Australian banks using a t... more This paper analyses operational risk events from the ORIC database for Australian banks using a technique borrowed from biological evolutionary analysis. The technique groups risk events by their characteristics to indicate a hierarchical structure of the characteristics, which then allows an analysis of the relative importance of the characteristics observed across multiple risk events. Analysis of the hierarchical structure then indicates the characteristics where greater management effort could result in reduced risk events, and also permits a cost-benefit analysis as to the payoff from diverting resources to the management of the dominant characteristics. The technique is also capable of identifying the emergence of new characteristic combinations in creating risk events. This may allow for the forward projection of the next combination of characters that may produce new risk events.
We propose a methodology applied to complex systems to analyze operational risk events in banks, ... more We propose a methodology applied to complex systems to analyze operational risk events in banks, with the objective of determining an understanding of the key characteristics and their relationships in initiating operational risk losses. We applied our methodology to operational risk losses in Australian banks over the period 2010-14. The analysis identified that there are a small number of characteristics that are common to many operational risk events, and these "level 1" characteristics are stable across time, which implies operational risk losses could be controlled by managing these characteristics. The methodology adds value to the existing analysis by identifying the main characteristics of operational risk events in a rigorous manner.
Heavy-tailed distributions have been observed for various financial risks and papers have observe... more Heavy-tailed distributions have been observed for various financial risks and papers have observed that these heavy-tailed distributions are power law distributions. The breakdown of a power law distribution is also seen as an indicator of a tipping point being reached and a system then moves from stability through instability to a new equilibrium. In this paper, we analyse the distribution of operational risk losses in US banks, credit defaults in US corporates and market risk events in the US during the global financial crisis (GFC). We conclude that market risk and credit risk do not follow a power law distribution, and even though operational risk follows a power law distribution, there is a better distribution fit for operational risk. We also conclude that whilst there is evidence that credit defaults and market risks did reach a tipping point, operational risk losses did not. We conclude that the government intervention in the banking system during the GFC was a possible cause of banks avoiding a tipping point.
This paper explores the characteristics of 2,141 operational risk events amongst European (EU) an... more This paper explores the characteristics of 2,141 operational risk events amongst European (EU) and US banks over the period 2008–2014. We have analysed the operational risk events using a method originating in biology for the study of interrelatedness of characteristics in a complex adaptive system. The methodology, called cladistics, provides insights into the relationships between characteristics of operational risk events in banks that is not available from the traditional statistical analysis. We have used cladistics to explore if there are consistent patterns of operational risk characteristics across banks in single and different geographic zones. One significant pattern emerged which indicates there are key, stable characteristics across both geographic zones and across banks in each zone. The results identify the characteristics that could then be managed by the banks to reduce operational risk losses. We also have analysed separately the characteristics of operational risk events for “big” banks and extreme events and these results indicate that big banks and small banks have similar key operational risk characteristics, but the characteristics of extreme operational risk events are different to those for the non-extreme events.
We propose a methodology applied to complex systems to analyze operational risk events in banks, ... more We propose a methodology applied to complex systems to analyze operational risk events in banks, with the objective of determining an understanding of the key characteristics and their relationships in initiating operational risk losses. We applied our methodology to operational risk losses in Australian banks over the period 2010-14. The analysis identified that there are a small number of characteristics that are common to many operational risk events, and these "level 1" characteristics are stable across time, which implies operational risk losses could be controlled by managing these characteristics. The methodology adds value to the existing analysis by identifying the main characteristics of operational risk events in a rigorous manner.
This paper analyses operational risk events from the ORIC database for Australian banks using a t... more This paper analyses operational risk events from the ORIC database for Australian banks using a technique borrowed from biological evolutionary analysis. The technique groups risk events by their characteristics to indicate a hierarchical structure of the characteristics, which then allows an analysis of the relative importance of the characteristics observed across multiple risk events. Analysis of the hierarchical structure then indicates the characteristics where greater management effort could result in reduced risk events, and also permits a cost-benefit analysis as to the payoff from diverting resources to the management of the dominant characteristics. The technique is also capable of identifying the emergence of new characteristic combinations in creating risk events. This may allow for the forward projection of the next combination of characters that may produce new risk events.
We propose a methodology applied to complex systems to analyze operational risk events in banks, ... more We propose a methodology applied to complex systems to analyze operational risk events in banks, with the objective of determining an understanding of the key characteristics and their relationships in initiating operational risk losses. We applied our methodology to operational risk losses in Australian banks over the period 2010-14. The analysis identified that there are a small number of characteristics that are common to many operational risk events, and these "level 1" characteristics are stable across time, which implies operational risk losses could be controlled by managing these characteristics. The methodology adds value to the existing analysis by identifying the main characteristics of operational risk events in a rigorous manner.
Heavy-tailed distributions have been observed for various financial risks and papers have observe... more Heavy-tailed distributions have been observed for various financial risks and papers have observed that these heavy-tailed distributions are power law distributions. The breakdown of a power law distribution is also seen as an indicator of a tipping point being reached and a system then moves from stability through instability to a new equilibrium. In this paper, we analyse the distribution of operational risk losses in US banks, credit defaults in US corporates and market risk events in the US during the global financial crisis (GFC). We conclude that market risk and credit risk do not follow a power law distribution, and even though operational risk follows a power law distribution, there is a better distribution fit for operational risk. We also conclude that whilst there is evidence that credit defaults and market risks did reach a tipping point, operational risk losses did not. We conclude that the government intervention in the banking system during the GFC was a possible cause of banks avoiding a tipping point.
This paper explores the characteristics of 2,141 operational risk events amongst European (EU) an... more This paper explores the characteristics of 2,141 operational risk events amongst European (EU) and US banks over the period 2008–2014. We have analysed the operational risk events using a method originating in biology for the study of interrelatedness of characteristics in a complex adaptive system. The methodology, called cladistics, provides insights into the relationships between characteristics of operational risk events in banks that is not available from the traditional statistical analysis. We have used cladistics to explore if there are consistent patterns of operational risk characteristics across banks in single and different geographic zones. One significant pattern emerged which indicates there are key, stable characteristics across both geographic zones and across banks in each zone. The results identify the characteristics that could then be managed by the banks to reduce operational risk losses. We also have analysed separately the characteristics of operational risk events for “big” banks and extreme events and these results indicate that big banks and small banks have similar key operational risk characteristics, but the characteristics of extreme operational risk events are different to those for the non-extreme events.
We propose a methodology applied to complex systems to analyze operational risk events in banks, ... more We propose a methodology applied to complex systems to analyze operational risk events in banks, with the objective of determining an understanding of the key characteristics and their relationships in initiating operational risk losses. We applied our methodology to operational risk losses in Australian banks over the period 2010-14. The analysis identified that there are a small number of characteristics that are common to many operational risk events, and these "level 1" characteristics are stable across time, which implies operational risk losses could be controlled by managing these characteristics. The methodology adds value to the existing analysis by identifying the main characteristics of operational risk events in a rigorous manner.
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