000			05694nam a2200373 i 4500
008			081120s2009 enka b 001 0 eng
010			_a2008047074
020			_a9780470517703 _qcloth
020			_a0470517700 _qcloth
035			_a(OCoLC)276274564
040			_aDLC _beng _cDLC _dBWKUK _dBWK _dYDXCP _dCDX _erda
049			_aBAUN_MERKEZ
050	0	4	_aHD61 _b.M428 2009
082	0	0	_222
245	0	0	_aMeasuring operational and reputational risk : _ba practitioner's approach / _cAldo Soprano ... [and others]
264		1	_aChichester, England ; _aHoboken, NJ : _bWiley, _c[2009]
264		4	_c©2009
300			_axv, 207 pages : _billustrations ; _c24 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	1		_aWiley finance series
504			_aIncludes bibliographical references (pages [193]-200) and index
505	0	0	_t Table Of Contents: _tForeword (Andrea Sironi). _tPreface. _tAcknowledgments. _t1 The Development of ORM in UniCredit Group. _t1.1 A brief history of a fast-growing group. _t1.2 Creating a new function. _t1.3 Developing the new control system. _t1.4 Challenges in the early stages. _t1.5 Methodology to measure operational risk. _t1.6 Training and internal communication focus. _t1.7 International regulatory challenges. _t1.8 Reputational risk management. _t2 The Calculation Dataset. _t2.1 Definitions. _t2.2 Rules of thumb. _t2.3 Internal loss data. _t2.3.1 Business line mapping. _t2.3.2 Event type classifications. _t2.3.3 Data quality analysis. _t2.3.4 Special cases. _t2.4 Minimum loss threshold. _t2.5 External data. _t2.5.1 Public or external data sources. _t2.5.2 Consortium data. _t2.5.3 Scenario data. _t2.6 Business environment and internal control factors. _t2.7 Scenarios. _t2.8 Insurance information. _t2.9 Scaling data. _t2.10 The Unicredit Group Operational Risk database Evolution. _t2.11 Final considerations. _t3 Loss Distribution Approaches. _t3.1 Calculation dataset building. _t3.1.1 Internal calculation dataset. _t3.1.2 External calculation dataset. _t3.1.3 Scenario-generated calculation dataset. _t3.1.4 Risk indicators calculation dataset. _t3.2 General LDA framework. _t3.3 Operational risk classes. _t3.3.1 Identically distributed risk classes. _t3.3.2 Inflation adjustment. _t3.3.3 Data independence. _t3.4 Parametric estimation and goodness-of-fit Techniques. _t3.4.1 Severity distributions. _t3.4.2 Graphical methods. _t3.4.3 Analytical methods. _t3.4.4 Frequency distributions. _t3.5 Applying extreme value theory. _t3.6 g-and-h distribution theory. _t3.7 Calculating operational capital at risk. _t3.7.1 Loss severity distribution. _t3.7.2 Loss frequency distribution. _t3.7.3 Annual loss distribution. _t3.7.4 Single class capital at risk. _t3.8 Insurance modeling. _t3.8.1 Appropriate haircuts reflecting the policy’s declining residual term. _t3.8.2 Payment uncertainty. _t3.8.3 Counterparty risk. _t3.8.4 Application of insurance. _t3.9 Adjustment for risk indicators. _t3.10 Operational risk classes aggregation. _t3.10.1 Copulae functions. _t3.10.2 Elliptical copulae. _t3.10.3 Archimedean copulae. _t3.10.4 Choice of copula. _t3.10.5 Correlation coefficients. _t3.11 The closed-form approximation for OpVaR. _t3.11.1 Effect of the minimum threshold on capital at risk. _t3.12 Confidence band for capital at risk. _t3.13 Stress testing. _t3.14 Loss data minimum threshold setting. _t3.15 Empirical application on Algo OpData. _t3.15.1 Descriptive statistics. _t3.15.2 Autocorrelation analysis. _t3.15.3 Capital at risk estimates using parametric models. _t3.15.4 Capital at risk estimates using EVT. _t3.15.5 Capital at risk estimates using the g-and-h distribution. _t3.15.6 Capital at risk estimates considering Correlation. _t3.16 Regulatory capital requirement. _t3.16.1 The consolidated capital requirement. _t3.16.2 The individual capital requirement. _t3.17 Economic capital requirement. _t3.18 Integration of operational risk in the budgeting process. _t4 Analyzing Insurance Policies. _t4.1 Insurance management and risk transfer. _t4.2 Qualifying criteria in the Basel 2 capital Framework. _t4.2.1 Rating of the insurance company. _t4.2.2 Duration and residual term of the insurance contract. _t4.2.3 Policy termination requisites. _t4.2.4 Claims reimbursement uncertainty and ineffective coverage. _t4.2.5 Conclusions. _t4.3 A practical application to traditional insurance. _t4.3.1 Insurance policies to cover financial institutions’ operational risks. _t4.3.2 Operational event types and available insurance coverage. _t5 Managing Reputational Risk. _t5.1 Introducing reputational risk. _t5.2 A financial institution’s reputational risk exposure. _t5.3 Managing reputational risk: a matter of policy. _t5.4 Reputational risk measurement. _t5.4.1 Reputational risk as a function of share price volatility. _t5.4.2 Measuring reputational risk using scenarios. _t5.4.3 Scoring-card-based models for reputational risk assessment. _t5.5 A recent example of reputational event. _t5.5.1 A description of the event. _t5.5.2 Background. _t5.5.3 How the fake trading occurred. _t5.5.4 The discovery and first reactions. _t5.5.5 Measures planned and taken. _t5.5.6 Immediate consequences for SocGen. _t5.5.7 Reputational issues and comments. _t5.5.8 The lessons learned – what can we do to avoid being next? _t5.5.9 Psychological, ‘soft’ factors. _t5.5.10 Control instruments. _t5.5.11 Managing data and signals. _t6 Conclusions. _tReferences. _tFurther reading. _tIndex.
650		0	_aRisk management
650		0	_aRisk assessment
650		0	_aOperational risk
650		0	_aCorporate image
700	1		_aSoprano, Aldo
830		0	_915983 _aWiley finance series.
900			_a33813
900			_bsatın
942			_2lcc _cKT
999			_c30380 _d30380