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A Non-Linear Analysis of Operational Risk and Operational Risk Management in Banking Industry

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University of Wollongong Research Online University of Wollongong Thesis Collection 2017+ University of Wollongong Thesis Collections 2017 A non-linear analysis of operational risk and operational risk management in banking industry Yifei Li University of Wollongong Follow this and additional works at: https://ro.uow.edu.au/theses1 University of Wollongong Copyright Warning You may print or download ONE copy of this document for the purpose of your own research or study. The University does not authorise you to copy, communicate or otherwise make available electronically to any other person any copyright material contained on this site. You are reminded of the following: This work is copyright. Apart from any use permitted under the Copyright Act 1968, no part of this work may be reproduced by any process, nor may any other exclusive right be exercised, without the permission of the author. Copyright owners are entitled to take legal action against persons who infringe their copyright. A reproduction of material that is protected by copyright may be a copyright infringement. A court may impose penalties and award damages in relation to offences and infringements relating to copyright material. Higher penalties may apply, and higher damages may be awarded, for offences and infringements involving the conversion of material into digital or electronic form. Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily represent the views of the University of Wollongong. Recommended Citation Li, Yifei, A non-linear analysis of operational risk and operational risk management in banking industry, Doctor of Philosophy thesis, School of Accounting, Economics and Finance, University of Wollongong, 2017. https://ro.uow.edu.au/theses1/235 Research Online is the open access institutional repository for the University of Wollongong. For further information contact the UOW Library: [email protected] A Non-linear Analysis of Operational Risk and Operational Risk Management in Banking Industry Yifei Li This thesis is presented as part of the requirements for the conferral of the degree: Doctor of Philosophy Supervisor: Associate Professor John Evans The University of Wollongong School of School of Accounting, Economics and Finance October, 2017 This work c copyright by Yifei Li, 2018. All Rights Reserved. No part of this work may be reproduced, stored in a retrieval system, transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of the author or the University of Wollongong. This research has been conducted with the support of an Australian Government Research Training Program Scholarship. Declaration I, Yifei Li, declare that this thesis is submitted in partial fulfilment of the requirements for the conferral of the degree Doctor of Philosophy, from the University of Wollongong, is wholly my own work unless otherwise referenced or acknowledged. This document has not been submitted for qualifications at any other academic institution. Yifei Li April 3, 2018 Abstract The analysis of operational risk events in banks is complicated as the financial institutions themselves, as well as the external environment in which financial institutions operate, are complex adaptive systems. As a complex adaptive system, it is not possible to analyse the outcomes of the system from a reductionist perspective as it is the interaction of the agents in the system that drive the system outcome. Most analysis of operational risk in banks has involved statistical analysis of the frequency and severity of historical events, but this analysis does not identify the drivers of the events that is necessary to assist management in managing the future events to an acceptable level. This study aims at providing insights into operational risk management from a complex adaptive systems view. An empirical study with AU, US and EU data will use cladistics analysis, networks analysis and complexity theory for the validation of the methodology. The result of the empirical study shows relatively stable important drivers of operational risk events. The findings present the feasibility of applying the theories from other fields, i.e. cladistics analysis and physics theory into analysing operational risk. Further, it reveals the man- agement environment and management style in different markets is relatively stable after the GFC. iv Acknowledgments First and foremost, I would like to express my deepest gratitude to my supervisor As- sociate Professor John Evans. It is an honour and my fortune to be his student. He has guided me throughout the journey and has been my mentor both in research and in be- haviour. The enthusiasm he has for research always stimulates me. I also appreciate the pleasant journey we had for entire research period. Very special thanks to my family, for all the support during my whole life. Without their support, I would not have a chance to go so far in research. I am very thankful of Neil Allan, for the continuous help and support throughout this research. I appreciate the reviewers of my thesis, Dr Frank Ashe and Professor Ian McCarthy, for the precious insights. I would like to thank my colleagues and friends, Lei Shi and Xingzhuo Wang, for the discussions, inspirations, and company during the journey. I would like to thank Yaonan Pan, Zheng Xie, Xu Huang, Jiale Zhu, Zhuo Chen, Xueting Zhang, Hao Jiang, Alex Maclaren for their invaluable emotional support and company. In regards to programming, I would like to thank my friends Guanhua Zhao and Wen Chen. Finally, special thanks to Jun Wu, for the continuous stimulation to let me step for- ward. v Contents Abstract iv 1 Introduction1 1.1 Objectives of the study...........................2 1.2 Research Method..............................2 1.3 Organisation of the study..........................2 2 Operational Risk and Operational Risk Management4 2.1 Introduction.................................4 2.2 Classification of Risks and Jurisdiction of Operational Risk........5 2.3 Definition of Operational Risk.......................6 2.4 Categorisation of Operational Risk.....................7 2.5 Distinctive features of Operational Risk..................8 2.6 Operational Risk Management Process...................9 2.6.1 Identification and Assessment................... 10 2.6.2 Mitigation.............................. 11 2.6.3 Reporting.............................. 11 2.7 Discussion.................................. 12 3 Complexity in Financial Markets 15 3.1 Introduction................................. 15 3.2 Complex Adaptive Systems......................... 16 3.3 Phase transition, Self-organized Criticality and Power Law Distributions. 20 3.4 Network................................... 22 3.4.1 Random network.......................... 23 3.4.2 Small world network........................ 23 3.4.3 Scale free network......................... 25 3.4.4 Centrality.............................. 26 3.5 Operational Risk Environment as Complex Adaptive System....... 29 3.6 Discussion.................................. 33 vi CONTENTS vii 4 Cladistics Analysis 34 4.1 Introduction................................. 34 4.2 Justification of Using Cladistics Analysis in Analysing Operational Risk. 35 4.3 Basics of Cladistics Analysis........................ 37 4.4 Encoding.................................. 40 4.5 Maximum Parsimony............................ 42 4.6 Summary.................................. 44 5 Cladistics Analysis for AU, US and EU Markets 46 5.1 Introduction................................. 46 5.2 Description of data............................. 46 5.3 Derivation of characteristics for Cladistics Analysis............ 48 5.3.1 Basel Based Characteristics.................... 48 5.3.2 Dataset Derived Characteristics without Descriptive Characteristics 48 5.3.3 Descriptive Characteristics..................... 50 5.4 A presentation of tree outcome....................... 50 5.5 Results for AU with Descriptive Characteristic Set............. 55 5.6 Results for US with Descriptive Characteristic Set............. 59 5.7 Results for EU with Descriptive Characteristic Set............. 61 5.8 Comparison of AU, EU and US results with Descriptive Characteristic Set 64 5.9 Analysis with Basel Characteristics Set.................. 65 5.9.1 Results for AU........................... 65 5.9.2 Results for US........................... 65 5.9.3 Results for EU........................... 68 5.9.4 Comparison of AU, US, and EU results.............. 70 5.10 Implications for Operational Risk Management.............. 71 6 Existence of Power Law and Tipping Points 73 6.1 Introduction................................. 73 6.2 Phase Transition and Critical Point Analysis................ 73 6.3 Test for Operational Risk.......................... 75 6.3.1 Estimation of parameters and Lower boundaries.......... 75 6.3.2 Performance of power law distribution............... 75 6.3.3 Comparison with other distributions................ 75 6.4 Discussion.................................. 76 7 An Analysis of Extreme Operational risk Pooling 79 7.1 Introduction................................. 79 7.2 Sustainable Pooling Issues......................... 80 7.3 Empirical Analysis............................. 81 CONTENTS viii 7.4 Qualitative Analysis............................. 82 7.5 Network Analysis.............................. 84 7.6 Feasibility of an Extreme Operational Risk Pool.............. 85 7.7 Potential Pooled Arrangements......................
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