Risk analytics: data-driven decisions under uncertainty
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; Abingdon, Oxon
CRC Press
2024
|
| Ausgabe: | first edition |
| Schlagworte: | |
| Beschreibung: | xi, 469 Seiten Illustrationen, Diagramme |
| ISBN: | 9780367359614 0367359618 9781032507781 1032507780 |
Internformat
MARC
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| 100 | 1 | |a Rodriguez, Eduardo |e Verfasser |0 (DE-588)135567823 |4 aut | |
| 245 | 1 | 0 | |a Risk analytics |b data-driven decisions under uncertainty |c Eduardo Rodriguez, PhD (Founder and Principal, IQAnalytics Inc., University of Ottawa and Wenzhou Kean University) |
| 250 | |a first edition | ||
| 264 | 1 | |a Boca Raton ; Abingdon, Oxon |b CRC Press |c 2024 | |
| 300 | |a xi, 469 Seiten |b Illustrationen, Diagramme | ||
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| 505 | 8 | |a Intro -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Author -- Introduction -- 1. Fundamental Concepts -- 1.1 All organizations require risk analytics -- 1.2 Evolution of risk analytics -- 1.3 Risk analytics is a crucial concept in risk management processes -- 1.4 Enterprise risk management (ERM) -- 1.5 Measuring, metrics, and business concepts -- 1.6 Understanding data, scales, and data arrays -- 1.7 Basic probability concepts useful in risk analytics -- 1.8 Examples of stochastic processes -- 1.9 Compound Poisson distribution -- 1.9.1 Distribution Function -- 1.9.2 Distribution Function -- 1.9.3 Mean and Variance -- 1.10 Understanding Linear Algebra -- 1.11 Traditional models in finance -- 1.12 RAS: Hybrid system of human and artificial intelligences -- 2. Risk Management, Modeling, and Analytics Processes -- 2.1 Risk management and modeling processes -- 2.2 Risk modeling and risk knowledge development -- 2.2.1 Risk Knowledge Creation -- 2.2.2 Risk Knowledge Storage/Retrieval -- 2.2.3 Risk Knowledge Transfer -- 2.2.4 Risk Knowledge Application and Learning -- 2.3 General analytics process for risk management -- 2.4 Exploratory data analysis (EDA) -- 2.4.1 Illustration Example of EDA-Data Visualization -- 2.5 Data sources and data sampling in risk analytics -- 2.6 Data partition: Training, test, and validation data -- 2.7 Inference and generalization in risk analytics -- 2.8 Method of the moments -- 2.9 Maximum likelihood estimation -- 2.10 Estimators by optimization -- 2.11 Risk analytics road map -- 2.11.1 A View of Combining Marketing Risk and Credit Risk -- 2.11.1.1 Multivariate Analysis -- 2.11.1.2 Clustering Using k-Means -- 3. Decision Making Under Risk and Its Analytics Support -- 3.1 About general tools for supporting decision making -- 3.2 Benchmark approach for defining metrics | |
| 505 | 8 | |a 3.3 Steps to start the definition of metrics -- 3.4 Creating clusters of companies based on their returns -- 3.5 About risk indicators in a multivariate approach -- 3.6 Comparing risk metrics in groups -- 3.7 Decision making on investments in a risk prescriptive analytics approach -- 3.8 Forecasting and time series as a means to support decision making -- 3.8.1 Forecasting Models for Stationary Time Series -- 4. Risk Management and Analytics in Organizations -- 4.1 How to start a risk measurement system -- 4.2 Default rates, probabilities of default, and risk scales -- 4.2.1 Graphical Review of Loss Distribution Fitting -- 4.3 Exposure and default rate construction -- 4.4 An illustration of default rate calculation -- 4.5 Example of particular metric: Probable maximum loss -- 4.6 Adapting metrics to risk conditions: Banks illustration -- 4.6.1 Metrics in a Bank Setting -- 4.7 Exposure management in supply-chain risk -- 5. Tools for Risk Management -- 5.1 Prevention, resilience, and prediction -- 5.2 Risk analytics knowledge of return -- 5.3 Value of the firm, credit decisions, and intangibles in organizations -- 5.4 Assets and returns -- 5.5 Risk analytics and the analytics of return -- 5.6 General model for financial assets valuation -- 5.7 The equivalence concept -- 5.7.1 Equivalence Between a Future Sum and a Series of Uniform Sums -- 5.7.2 Special Treatment of Annuities -- 5.7.3 Finite Annuities with Arithmetic Progression Change -- 5.7.4 Principles of Loan Amortization -- 5.7.5 Basics of Investments, Cash Flows, and Transactions Analysis -- 5.8 Return analysis in stock markets and bonds -- 5.8.1 Return Analysis for Stocks -- 5.8.2 Return Analytics in Bonds -- 5.8.3 Price of a Bond Between Coupon Payments -- 5.8.3.1 Callable Bonds -- 5.9 Return and Term Structure of Interest: Spot and Forward -- 5.9.1 Spot and Forward Rates | |
| 505 | 8 | |a 5.9.2 Sensitivity Analysis of Interest Rates -- 5.9.2.1 Duration -- 5.9.2.2 Convexity -- 5.9.2.3 About Immunization -- 5.10 Metrics in a portfolio -- 5.11 Analytics of products to deal with risk -- 5.11.1 Interest Rate Swaps -- 5.11.2 Forward Contracts -- 5.11.3 Futures Contracts -- 5.11.4 Options -- 5.11.4.1 Parity Between Put and Call Options -- Black and Scholes Model -- 5.11.4.2 American Options -- 5.11.4.3 Valuation -- 6. Data Analytics in Risk Management -- 6.1 How to use technology, and store and manage structured and unstructured data -- 6.2 Technology in the RAS design -- 7. Machine and Statistical Learning in Risk Analytics -- 7.1 Managing models -- 7.2 Basics of measurement to create groups -- 7.3 Models, validation, testing, and performance -- 7.4 Risk classification: Relationships and predictions -- 7.5 Search of relationships among variables using generalized linear models -- 7.6 Modeling risk unit -- 7.7 Mixed models -- 7.8 Logistic regression -- 7.9 Correspondence analysis -- 7.10 More about multivariate tools -- 7.10.1 Discriminant Analysis -- 7.10.2 Artificial Neural Networks (ANNs), Deep Learning, and Tensorflow -- 7.10.2.1 Structure of the ANN -- 7.10.3 Analysis Based on Trees -- 7.10.3.1 Additional Non-Parametric Analysis -- 7.10.4 Ensembles - Bagging - Boosting and Stacking -- 7.10.4.1 Bagging - Bootstrap Aggregating -- 7.10.4.2 Boosting -- 7.10.4.3 Stacking -- 7.11 Beyond classification related problems -- 7.11.1 Analysis of Variance and Its Components -- 7.11.2 Misclassification Problem -- 7.11.3 Migration of risk levels with Markov's model -- 8. Dealing with Monitoring the Risk Analytics Process -- 8.1 Possible barriers to create a risk analytics system (RAS) -- 8.2 Factors affecting the organizations' RAS -- 8.3 Digging deeper in RAS components -- 8.4 Creating a RAS and the key risk indicators analysis | |
| 505 | 8 | |a 9. Creation of Actions and Value -- 9.1 Possible bases of RAS design -- 9.2 Framework for the risk knowledge management component -- 9.2.1 Knowledge Management Processes -- 9.2.2 Knowledge Management System (KMS) -- Implementation and Change of Plans -- Lessons Learned and Future Steps -- 9.3 Creating risk knowledge through estimating metrics using simulation -- 9.4 Portfolio decisions -- 9.5 Analyzing pro-forma financial statements -- 9.6 About new products as combination of risk -- 9.7 About factors affecting a loss distribution (LGD) -- 9.8 Risk analytics contribution to LGD analysis -- 9.9 LGD learning processes -- 9.10 Classification and LGD -- References -- Index | |
| 650 | 4 | |a Decision making | |
| 650 | 4 | |a Management-Statistical methods | |
| 650 | 4 | |a Organizational effectiveness | |
| 650 | 4 | |a Risk assessment-Data processing | |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-034556430 | ||
Datensatz im Suchindex
| _version_ | 1804185787125727232 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Rodriguez, Eduardo |
| author_GND | (DE-588)135567823 |
| author_facet | Rodriguez, Eduardo |
| author_role | aut |
| author_sort | Rodriguez, Eduardo |
| author_variant | e r er |
| building | Verbundindex |
| bvnumber | BV049295088 |
| classification_rvk | QH 233 SK 830 QC 020 QP 327 |
| contents | Intro -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Author -- Introduction -- 1. Fundamental Concepts -- 1.1 All organizations require risk analytics -- 1.2 Evolution of risk analytics -- 1.3 Risk analytics is a crucial concept in risk management processes -- 1.4 Enterprise risk management (ERM) -- 1.5 Measuring, metrics, and business concepts -- 1.6 Understanding data, scales, and data arrays -- 1.7 Basic probability concepts useful in risk analytics -- 1.8 Examples of stochastic processes -- 1.9 Compound Poisson distribution -- 1.9.1 Distribution Function -- 1.9.2 Distribution Function -- 1.9.3 Mean and Variance -- 1.10 Understanding Linear Algebra -- 1.11 Traditional models in finance -- 1.12 RAS: Hybrid system of human and artificial intelligences -- 2. Risk Management, Modeling, and Analytics Processes -- 2.1 Risk management and modeling processes -- 2.2 Risk modeling and risk knowledge development -- 2.2.1 Risk Knowledge Creation -- 2.2.2 Risk Knowledge Storage/Retrieval -- 2.2.3 Risk Knowledge Transfer -- 2.2.4 Risk Knowledge Application and Learning -- 2.3 General analytics process for risk management -- 2.4 Exploratory data analysis (EDA) -- 2.4.1 Illustration Example of EDA-Data Visualization -- 2.5 Data sources and data sampling in risk analytics -- 2.6 Data partition: Training, test, and validation data -- 2.7 Inference and generalization in risk analytics -- 2.8 Method of the moments -- 2.9 Maximum likelihood estimation -- 2.10 Estimators by optimization -- 2.11 Risk analytics road map -- 2.11.1 A View of Combining Marketing Risk and Credit Risk -- 2.11.1.1 Multivariate Analysis -- 2.11.1.2 Clustering Using k-Means -- 3. Decision Making Under Risk and Its Analytics Support -- 3.1 About general tools for supporting decision making -- 3.2 Benchmark approach for defining metrics 3.3 Steps to start the definition of metrics -- 3.4 Creating clusters of companies based on their returns -- 3.5 About risk indicators in a multivariate approach -- 3.6 Comparing risk metrics in groups -- 3.7 Decision making on investments in a risk prescriptive analytics approach -- 3.8 Forecasting and time series as a means to support decision making -- 3.8.1 Forecasting Models for Stationary Time Series -- 4. Risk Management and Analytics in Organizations -- 4.1 How to start a risk measurement system -- 4.2 Default rates, probabilities of default, and risk scales -- 4.2.1 Graphical Review of Loss Distribution Fitting -- 4.3 Exposure and default rate construction -- 4.4 An illustration of default rate calculation -- 4.5 Example of particular metric: Probable maximum loss -- 4.6 Adapting metrics to risk conditions: Banks illustration -- 4.6.1 Metrics in a Bank Setting -- 4.7 Exposure management in supply-chain risk -- 5. Tools for Risk Management -- 5.1 Prevention, resilience, and prediction -- 5.2 Risk analytics knowledge of return -- 5.3 Value of the firm, credit decisions, and intangibles in organizations -- 5.4 Assets and returns -- 5.5 Risk analytics and the analytics of return -- 5.6 General model for financial assets valuation -- 5.7 The equivalence concept -- 5.7.1 Equivalence Between a Future Sum and a Series of Uniform Sums -- 5.7.2 Special Treatment of Annuities -- 5.7.3 Finite Annuities with Arithmetic Progression Change -- 5.7.4 Principles of Loan Amortization -- 5.7.5 Basics of Investments, Cash Flows, and Transactions Analysis -- 5.8 Return analysis in stock markets and bonds -- 5.8.1 Return Analysis for Stocks -- 5.8.2 Return Analytics in Bonds -- 5.8.3 Price of a Bond Between Coupon Payments -- 5.8.3.1 Callable Bonds -- 5.9 Return and Term Structure of Interest: Spot and Forward -- 5.9.1 Spot and Forward Rates 5.9.2 Sensitivity Analysis of Interest Rates -- 5.9.2.1 Duration -- 5.9.2.2 Convexity -- 5.9.2.3 About Immunization -- 5.10 Metrics in a portfolio -- 5.11 Analytics of products to deal with risk -- 5.11.1 Interest Rate Swaps -- 5.11.2 Forward Contracts -- 5.11.3 Futures Contracts -- 5.11.4 Options -- 5.11.4.1 Parity Between Put and Call Options -- Black and Scholes Model -- 5.11.4.2 American Options -- 5.11.4.3 Valuation -- 6. Data Analytics in Risk Management -- 6.1 How to use technology, and store and manage structured and unstructured data -- 6.2 Technology in the RAS design -- 7. Machine and Statistical Learning in Risk Analytics -- 7.1 Managing models -- 7.2 Basics of measurement to create groups -- 7.3 Models, validation, testing, and performance -- 7.4 Risk classification: Relationships and predictions -- 7.5 Search of relationships among variables using generalized linear models -- 7.6 Modeling risk unit -- 7.7 Mixed models -- 7.8 Logistic regression -- 7.9 Correspondence analysis -- 7.10 More about multivariate tools -- 7.10.1 Discriminant Analysis -- 7.10.2 Artificial Neural Networks (ANNs), Deep Learning, and Tensorflow -- 7.10.2.1 Structure of the ANN -- 7.10.3 Analysis Based on Trees -- 7.10.3.1 Additional Non-Parametric Analysis -- 7.10.4 Ensembles - Bagging - Boosting and Stacking -- 7.10.4.1 Bagging - Bootstrap Aggregating -- 7.10.4.2 Boosting -- 7.10.4.3 Stacking -- 7.11 Beyond classification related problems -- 7.11.1 Analysis of Variance and Its Components -- 7.11.2 Misclassification Problem -- 7.11.3 Migration of risk levels with Markov's model -- 8. Dealing with Monitoring the Risk Analytics Process -- 8.1 Possible barriers to create a risk analytics system (RAS) -- 8.2 Factors affecting the organizations' RAS -- 8.3 Digging deeper in RAS components -- 8.4 Creating a RAS and the key risk indicators analysis 9. Creation of Actions and Value -- 9.1 Possible bases of RAS design -- 9.2 Framework for the risk knowledge management component -- 9.2.1 Knowledge Management Processes -- 9.2.2 Knowledge Management System (KMS) -- Implementation and Change of Plans -- Lessons Learned and Future Steps -- 9.3 Creating risk knowledge through estimating metrics using simulation -- 9.4 Portfolio decisions -- 9.5 Analyzing pro-forma financial statements -- 9.6 About new products as combination of risk -- 9.7 About factors affecting a loss distribution (LGD) -- 9.8 Risk analytics contribution to LGD analysis -- 9.9 LGD learning processes -- 9.10 Classification and LGD -- References -- Index |
| ctrlnum | (OCoLC)1403381952 (DE-599)BVBBV049295088 |
| dewey-full | 658.00727 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.00727 |
| dewey-search | 658.00727 |
| dewey-sort | 3658.00727 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Mathematik Wirtschaftswissenschaften |
| edition | first edition |
| format | Book |
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| id | DE-604.BV049295088 |
| illustrated | Illustrated |
| index_date | 2024-07-03T22:37:56Z |
| indexdate | 2024-07-10T10:00:47Z |
| institution | BVB |
| isbn | 9780367359614 0367359618 9781032507781 1032507780 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034556430 |
| oclc_num | 1403381952 |
| open_access_boolean | |
| owner | DE-521 DE-N2 DE-11 |
| owner_facet | DE-521 DE-N2 DE-11 |
| physical | xi, 469 Seiten Illustrationen, Diagramme |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | CRC Press |
| record_format | marc |
| spelling | Rodriguez, Eduardo Verfasser (DE-588)135567823 aut Risk analytics data-driven decisions under uncertainty Eduardo Rodriguez, PhD (Founder and Principal, IQAnalytics Inc., University of Ottawa and Wenzhou Kean University) first edition Boca Raton ; Abingdon, Oxon CRC Press 2024 xi, 469 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Intro -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Author -- Introduction -- 1. Fundamental Concepts -- 1.1 All organizations require risk analytics -- 1.2 Evolution of risk analytics -- 1.3 Risk analytics is a crucial concept in risk management processes -- 1.4 Enterprise risk management (ERM) -- 1.5 Measuring, metrics, and business concepts -- 1.6 Understanding data, scales, and data arrays -- 1.7 Basic probability concepts useful in risk analytics -- 1.8 Examples of stochastic processes -- 1.9 Compound Poisson distribution -- 1.9.1 Distribution Function -- 1.9.2 Distribution Function -- 1.9.3 Mean and Variance -- 1.10 Understanding Linear Algebra -- 1.11 Traditional models in finance -- 1.12 RAS: Hybrid system of human and artificial intelligences -- 2. Risk Management, Modeling, and Analytics Processes -- 2.1 Risk management and modeling processes -- 2.2 Risk modeling and risk knowledge development -- 2.2.1 Risk Knowledge Creation -- 2.2.2 Risk Knowledge Storage/Retrieval -- 2.2.3 Risk Knowledge Transfer -- 2.2.4 Risk Knowledge Application and Learning -- 2.3 General analytics process for risk management -- 2.4 Exploratory data analysis (EDA) -- 2.4.1 Illustration Example of EDA-Data Visualization -- 2.5 Data sources and data sampling in risk analytics -- 2.6 Data partition: Training, test, and validation data -- 2.7 Inference and generalization in risk analytics -- 2.8 Method of the moments -- 2.9 Maximum likelihood estimation -- 2.10 Estimators by optimization -- 2.11 Risk analytics road map -- 2.11.1 A View of Combining Marketing Risk and Credit Risk -- 2.11.1.1 Multivariate Analysis -- 2.11.1.2 Clustering Using k-Means -- 3. Decision Making Under Risk and Its Analytics Support -- 3.1 About general tools for supporting decision making -- 3.2 Benchmark approach for defining metrics 3.3 Steps to start the definition of metrics -- 3.4 Creating clusters of companies based on their returns -- 3.5 About risk indicators in a multivariate approach -- 3.6 Comparing risk metrics in groups -- 3.7 Decision making on investments in a risk prescriptive analytics approach -- 3.8 Forecasting and time series as a means to support decision making -- 3.8.1 Forecasting Models for Stationary Time Series -- 4. Risk Management and Analytics in Organizations -- 4.1 How to start a risk measurement system -- 4.2 Default rates, probabilities of default, and risk scales -- 4.2.1 Graphical Review of Loss Distribution Fitting -- 4.3 Exposure and default rate construction -- 4.4 An illustration of default rate calculation -- 4.5 Example of particular metric: Probable maximum loss -- 4.6 Adapting metrics to risk conditions: Banks illustration -- 4.6.1 Metrics in a Bank Setting -- 4.7 Exposure management in supply-chain risk -- 5. Tools for Risk Management -- 5.1 Prevention, resilience, and prediction -- 5.2 Risk analytics knowledge of return -- 5.3 Value of the firm, credit decisions, and intangibles in organizations -- 5.4 Assets and returns -- 5.5 Risk analytics and the analytics of return -- 5.6 General model for financial assets valuation -- 5.7 The equivalence concept -- 5.7.1 Equivalence Between a Future Sum and a Series of Uniform Sums -- 5.7.2 Special Treatment of Annuities -- 5.7.3 Finite Annuities with Arithmetic Progression Change -- 5.7.4 Principles of Loan Amortization -- 5.7.5 Basics of Investments, Cash Flows, and Transactions Analysis -- 5.8 Return analysis in stock markets and bonds -- 5.8.1 Return Analysis for Stocks -- 5.8.2 Return Analytics in Bonds -- 5.8.3 Price of a Bond Between Coupon Payments -- 5.8.3.1 Callable Bonds -- 5.9 Return and Term Structure of Interest: Spot and Forward -- 5.9.1 Spot and Forward Rates 5.9.2 Sensitivity Analysis of Interest Rates -- 5.9.2.1 Duration -- 5.9.2.2 Convexity -- 5.9.2.3 About Immunization -- 5.10 Metrics in a portfolio -- 5.11 Analytics of products to deal with risk -- 5.11.1 Interest Rate Swaps -- 5.11.2 Forward Contracts -- 5.11.3 Futures Contracts -- 5.11.4 Options -- 5.11.4.1 Parity Between Put and Call Options -- Black and Scholes Model -- 5.11.4.2 American Options -- 5.11.4.3 Valuation -- 6. Data Analytics in Risk Management -- 6.1 How to use technology, and store and manage structured and unstructured data -- 6.2 Technology in the RAS design -- 7. Machine and Statistical Learning in Risk Analytics -- 7.1 Managing models -- 7.2 Basics of measurement to create groups -- 7.3 Models, validation, testing, and performance -- 7.4 Risk classification: Relationships and predictions -- 7.5 Search of relationships among variables using generalized linear models -- 7.6 Modeling risk unit -- 7.7 Mixed models -- 7.8 Logistic regression -- 7.9 Correspondence analysis -- 7.10 More about multivariate tools -- 7.10.1 Discriminant Analysis -- 7.10.2 Artificial Neural Networks (ANNs), Deep Learning, and Tensorflow -- 7.10.2.1 Structure of the ANN -- 7.10.3 Analysis Based on Trees -- 7.10.3.1 Additional Non-Parametric Analysis -- 7.10.4 Ensembles - Bagging - Boosting and Stacking -- 7.10.4.1 Bagging - Bootstrap Aggregating -- 7.10.4.2 Boosting -- 7.10.4.3 Stacking -- 7.11 Beyond classification related problems -- 7.11.1 Analysis of Variance and Its Components -- 7.11.2 Misclassification Problem -- 7.11.3 Migration of risk levels with Markov's model -- 8. Dealing with Monitoring the Risk Analytics Process -- 8.1 Possible barriers to create a risk analytics system (RAS) -- 8.2 Factors affecting the organizations' RAS -- 8.3 Digging deeper in RAS components -- 8.4 Creating a RAS and the key risk indicators analysis 9. Creation of Actions and Value -- 9.1 Possible bases of RAS design -- 9.2 Framework for the risk knowledge management component -- 9.2.1 Knowledge Management Processes -- 9.2.2 Knowledge Management System (KMS) -- Implementation and Change of Plans -- Lessons Learned and Future Steps -- 9.3 Creating risk knowledge through estimating metrics using simulation -- 9.4 Portfolio decisions -- 9.5 Analyzing pro-forma financial statements -- 9.6 About new products as combination of risk -- 9.7 About factors affecting a loss distribution (LGD) -- 9.8 Risk analytics contribution to LGD analysis -- 9.9 LGD learning processes -- 9.10 Classification and LGD -- References -- Index Decision making Management-Statistical methods Organizational effectiveness Risk assessment-Data processing Erscheint auch als Online-Ausgabe 978-0-429-34289-9 Erscheint auch als Online-Ausgabe, PDF 978-1-000-89308-3 Erscheint auch als Online-Ausgabe, EPUB 978-1-000-89309-0 (DE-604)BV049293658 |
| spellingShingle | Rodriguez, Eduardo Risk analytics data-driven decisions under uncertainty Intro -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Author -- Introduction -- 1. Fundamental Concepts -- 1.1 All organizations require risk analytics -- 1.2 Evolution of risk analytics -- 1.3 Risk analytics is a crucial concept in risk management processes -- 1.4 Enterprise risk management (ERM) -- 1.5 Measuring, metrics, and business concepts -- 1.6 Understanding data, scales, and data arrays -- 1.7 Basic probability concepts useful in risk analytics -- 1.8 Examples of stochastic processes -- 1.9 Compound Poisson distribution -- 1.9.1 Distribution Function -- 1.9.2 Distribution Function -- 1.9.3 Mean and Variance -- 1.10 Understanding Linear Algebra -- 1.11 Traditional models in finance -- 1.12 RAS: Hybrid system of human and artificial intelligences -- 2. Risk Management, Modeling, and Analytics Processes -- 2.1 Risk management and modeling processes -- 2.2 Risk modeling and risk knowledge development -- 2.2.1 Risk Knowledge Creation -- 2.2.2 Risk Knowledge Storage/Retrieval -- 2.2.3 Risk Knowledge Transfer -- 2.2.4 Risk Knowledge Application and Learning -- 2.3 General analytics process for risk management -- 2.4 Exploratory data analysis (EDA) -- 2.4.1 Illustration Example of EDA-Data Visualization -- 2.5 Data sources and data sampling in risk analytics -- 2.6 Data partition: Training, test, and validation data -- 2.7 Inference and generalization in risk analytics -- 2.8 Method of the moments -- 2.9 Maximum likelihood estimation -- 2.10 Estimators by optimization -- 2.11 Risk analytics road map -- 2.11.1 A View of Combining Marketing Risk and Credit Risk -- 2.11.1.1 Multivariate Analysis -- 2.11.1.2 Clustering Using k-Means -- 3. Decision Making Under Risk and Its Analytics Support -- 3.1 About general tools for supporting decision making -- 3.2 Benchmark approach for defining metrics 3.3 Steps to start the definition of metrics -- 3.4 Creating clusters of companies based on their returns -- 3.5 About risk indicators in a multivariate approach -- 3.6 Comparing risk metrics in groups -- 3.7 Decision making on investments in a risk prescriptive analytics approach -- 3.8 Forecasting and time series as a means to support decision making -- 3.8.1 Forecasting Models for Stationary Time Series -- 4. Risk Management and Analytics in Organizations -- 4.1 How to start a risk measurement system -- 4.2 Default rates, probabilities of default, and risk scales -- 4.2.1 Graphical Review of Loss Distribution Fitting -- 4.3 Exposure and default rate construction -- 4.4 An illustration of default rate calculation -- 4.5 Example of particular metric: Probable maximum loss -- 4.6 Adapting metrics to risk conditions: Banks illustration -- 4.6.1 Metrics in a Bank Setting -- 4.7 Exposure management in supply-chain risk -- 5. Tools for Risk Management -- 5.1 Prevention, resilience, and prediction -- 5.2 Risk analytics knowledge of return -- 5.3 Value of the firm, credit decisions, and intangibles in organizations -- 5.4 Assets and returns -- 5.5 Risk analytics and the analytics of return -- 5.6 General model for financial assets valuation -- 5.7 The equivalence concept -- 5.7.1 Equivalence Between a Future Sum and a Series of Uniform Sums -- 5.7.2 Special Treatment of Annuities -- 5.7.3 Finite Annuities with Arithmetic Progression Change -- 5.7.4 Principles of Loan Amortization -- 5.7.5 Basics of Investments, Cash Flows, and Transactions Analysis -- 5.8 Return analysis in stock markets and bonds -- 5.8.1 Return Analysis for Stocks -- 5.8.2 Return Analytics in Bonds -- 5.8.3 Price of a Bond Between Coupon Payments -- 5.8.3.1 Callable Bonds -- 5.9 Return and Term Structure of Interest: Spot and Forward -- 5.9.1 Spot and Forward Rates 5.9.2 Sensitivity Analysis of Interest Rates -- 5.9.2.1 Duration -- 5.9.2.2 Convexity -- 5.9.2.3 About Immunization -- 5.10 Metrics in a portfolio -- 5.11 Analytics of products to deal with risk -- 5.11.1 Interest Rate Swaps -- 5.11.2 Forward Contracts -- 5.11.3 Futures Contracts -- 5.11.4 Options -- 5.11.4.1 Parity Between Put and Call Options -- Black and Scholes Model -- 5.11.4.2 American Options -- 5.11.4.3 Valuation -- 6. Data Analytics in Risk Management -- 6.1 How to use technology, and store and manage structured and unstructured data -- 6.2 Technology in the RAS design -- 7. Machine and Statistical Learning in Risk Analytics -- 7.1 Managing models -- 7.2 Basics of measurement to create groups -- 7.3 Models, validation, testing, and performance -- 7.4 Risk classification: Relationships and predictions -- 7.5 Search of relationships among variables using generalized linear models -- 7.6 Modeling risk unit -- 7.7 Mixed models -- 7.8 Logistic regression -- 7.9 Correspondence analysis -- 7.10 More about multivariate tools -- 7.10.1 Discriminant Analysis -- 7.10.2 Artificial Neural Networks (ANNs), Deep Learning, and Tensorflow -- 7.10.2.1 Structure of the ANN -- 7.10.3 Analysis Based on Trees -- 7.10.3.1 Additional Non-Parametric Analysis -- 7.10.4 Ensembles - Bagging - Boosting and Stacking -- 7.10.4.1 Bagging - Bootstrap Aggregating -- 7.10.4.2 Boosting -- 7.10.4.3 Stacking -- 7.11 Beyond classification related problems -- 7.11.1 Analysis of Variance and Its Components -- 7.11.2 Misclassification Problem -- 7.11.3 Migration of risk levels with Markov's model -- 8. Dealing with Monitoring the Risk Analytics Process -- 8.1 Possible barriers to create a risk analytics system (RAS) -- 8.2 Factors affecting the organizations' RAS -- 8.3 Digging deeper in RAS components -- 8.4 Creating a RAS and the key risk indicators analysis 9. Creation of Actions and Value -- 9.1 Possible bases of RAS design -- 9.2 Framework for the risk knowledge management component -- 9.2.1 Knowledge Management Processes -- 9.2.2 Knowledge Management System (KMS) -- Implementation and Change of Plans -- Lessons Learned and Future Steps -- 9.3 Creating risk knowledge through estimating metrics using simulation -- 9.4 Portfolio decisions -- 9.5 Analyzing pro-forma financial statements -- 9.6 About new products as combination of risk -- 9.7 About factors affecting a loss distribution (LGD) -- 9.8 Risk analytics contribution to LGD analysis -- 9.9 LGD learning processes -- 9.10 Classification and LGD -- References -- Index Decision making Management-Statistical methods Organizational effectiveness Risk assessment-Data processing |
| title | Risk analytics data-driven decisions under uncertainty |
| title_auth | Risk analytics data-driven decisions under uncertainty |
| title_exact_search | Risk analytics data-driven decisions under uncertainty |
| title_exact_search_txtP | Risk analytics data-driven decisions under uncertainty |
| title_full | Risk analytics data-driven decisions under uncertainty Eduardo Rodriguez, PhD (Founder and Principal, IQAnalytics Inc., University of Ottawa and Wenzhou Kean University) |
| title_fullStr | Risk analytics data-driven decisions under uncertainty Eduardo Rodriguez, PhD (Founder and Principal, IQAnalytics Inc., University of Ottawa and Wenzhou Kean University) |
| title_full_unstemmed | Risk analytics data-driven decisions under uncertainty Eduardo Rodriguez, PhD (Founder and Principal, IQAnalytics Inc., University of Ottawa and Wenzhou Kean University) |
| title_short | Risk analytics |
| title_sort | risk analytics data driven decisions under uncertainty |
| title_sub | data-driven decisions under uncertainty |
| topic | Decision making Management-Statistical methods Organizational effectiveness Risk assessment-Data processing |
| topic_facet | Decision making Management-Statistical methods Organizational effectiveness Risk assessment-Data processing |
| work_keys_str_mv | AT rodriguezeduardo riskanalyticsdatadrivendecisionsunderuncertainty |