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El. knyga: Engineering Risk and Finance

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Risk models are models of uncertainty, engineered for some purposes.  They are educated guesses and hypotheses assessed and valued in terms of well-defined future states and their consequences.  They are engineered to predict, to manage countable and accountable futures and to provide a frame of reference within which we may believe that uncertainty is tamed.  Quantitative-statistical tools are used to reconcile our information, experience and other knowledge with hypotheses that both serve as the foundation of risk models and also value and price risk. Risk models are therefore common to most professions, each with its own methods and techniques based on their needs, experience and a wisdom accrued over long periods of time.

This book provides a broad and interdisciplinary foundation to engineering risks and to their financial valuation and pricing.   Risk models applied in industry and business, heath care, safety, the environment and regulation are used to highlight their variety while financial valuation techniques are used to assess their financial consequences.

This book is technically accessible to all readers and students with a basic background in probability and statistics (with 3 chapters devoted to introduce their elements).  Principles of risk measurement, valuation and financial pricing as well as the economics of uncertainty are outlined in 5 chapters with numerous examples and applications.  New results, extending classical models such as the CCAPM are presented providing insights to assess the risks and their price in an interconnected, dependent and strategic economic environment.  In an environment departing from the fundamental assumptions we make regarding financial markets, the book provides a strategic/game-like approach to assess the risk and the opportunities that such an environment implies.  To control these risks, a strategic-control approach is developed that recognizes that many risks resultingby what we do as well as what others do.  In particular we address the strategic and statistical control of compliance in large financial institutions confronted increasingly with a complex and far more extensive regulation.

Recenzijos

From the book reviews:

This is an encyclopedic book on risk management in finance. Intended as a background text for undergraduate and graduate courses in risk finance and in risk engineering and management, yet unlike many textbooks, this book uses an interdisciplinary approach in the sense that it attempts to view risks as faced by many disciplines. As such, it can be used as a reference book for professionals and practitioners with diverse backgrounds. (Youngna Choi, Mathematical Reviews, May, 2014)

The book is recommended as an undergraduate and graduate text in risk finance, risk engineering and management, as well as for professionals that are both concerned and experienced in risk assessment and management techniques. (George Stoica, zbMATH, Vol. 1272, 2013)

1 Engineering Risk
1(32)
1.1 Risks and Uncertainty Everywhere
1(3)
1.2 Many Risks
4(4)
1.2.1 Globalization and Risk
4(1)
1.2.2 Space and Risk
5(1)
1.2.3 Catastrophic Risks
5(3)
2.4 Debt, Credit and Counter-Party Risk
8(3)
1.3 Industry and Other Risks: Deviant or Money
11(3)
1.3.1 Technology and Risks
11(1)
1.3.2 Technology and Networking
12(1)
1.3.3 Technology and Cyber Risks
13(1)
1.3.4 Example: Technology Risks, Simplicity and Complexity Risk Mitigation
13(1)
1.4 Quality, Statistical Controls and he Management of Quality
14(2)
1.5 Health and Safety Risks
16(2)
1.6 Finance and Risk
18(5)
1.6.1 The Risks of Certainty
18(1)
1.6.2 The Risks of Complexity
19(1)
1.6.3 The Risks of Regulation (and Non Regulation)
19(1)
1.6.4 Micro-Macro Mismatch Risks and Politics
19(2)
1.6.5 Risk and Incomplete Markets
21(1)
1.6.6 Risk Models and Uncertainty
22(1)
1.7 Corporate Risks
23(3)
1.8 Risk and Networked Firms
26(2)
1.8.1 Information Asymmetry
27(1)
1.9 Risks---Many Reasons, Many Origins
28(5)
2 Risk Management Everywhere
33(24)
2.1 Elements of Applied Risk Management: A Summary
33(1)
2.2 Risk Management, Value and Money
34(6)
2.2.1 Insurance Actuarial Risk
36(1)
2.2.2 Finance and Risk
37(3)
2.3 Industry Processes and Risk Management
40(4)
2.4 Marketing and Risk Management
44(5)
2.4.1 Reputation Risks
44(1)
2.4.2 Advertising Claims and Branding Risks
45(1)
2.4.3 IPO, Reputation and Risks
46(3)
2.5 Externalities and Risks Management
49(1)
2.6 Networks and Risks
50(7)
3 Probability Elements: An Applied Refresher
57(52)
3.1 Introduction
57(1)
3.2 Risk and Probability Moments
58(3)
3.2.1 Expectations, Variance and Other Moments
58(1)
3.2.2 The Expectation
58(1)
3.2.3 The Variance/Volatility: a measure of "Deviation"
59(1)
3.2.4 Skewness, Kurtosis and Filtration
59(1)
3.2.5 Range and Extreme Statistics
60(1)
3.3 Applications
61(15)
3.3.1 Skewness in Standardized Stocks Rates of Returns
61(1)
3.3.2 Reliability, Probability Risk Constraints and Deviations' Risks
62(2)
3.3.3 The Hazard Rate and Finance
64(1)
3.3.4 Risk Variance and Valuation
65(2)
3.3.5 VaR or Value at Risk
67(1)
3.3.6 Chance Constraints
68(1)
3.3.7 Type I and Type II Statistical Risks
69(1)
3.3.8 Quality Assurance and Chance Constraints Risks
70(1)
3.3.9 Credit and Credit Granting and Estimation of Default Probabilities
71(2)
3.3.10 Chance Constrained Programming
73(2)
3.3.11 Chance Constraint Moments Approximations
75(1)
3.3.12 Transformation of Random Variables into Normally Distributed Random Variables
75(1)
3.4 Generating Functions
76(7)
3.4.1 The Convolution Theorem for Moment and Probability Functions
77(2)
3.4.2 The Probability Generating Function of the Bernoulli Experiment
79(2)
3.4.3 Additional Examples
81(1)
3.4.4 The PGF of the Compound Poisson Process
82(1)
3.5 Probability Distributions
83(10)
3.5.1 The Bernoulli Family
84(1)
3.5.2 The Binomial and Other Distributions
85(5)
3.5.3 The Poisson Distribution
90(1)
3.5.4 The Conditional Sum Poisson and the Binomial Distribution
91(1)
3.5.5 Super and Hyper Poisson Distributions
92(1)
3.5.6 The Negative Binomial Distribution (NBD)
92(1)
3.6 The Normal Probability Distribution
93(5)
3.6.1 The Lognormal Probability Distribution
94(1)
3.6.2 The Exponential Distribution
95(1)
3.6.3 The Gamma Probability Distribution
95(1)
3.6.4 The Beta Probability Distribution
96(1)
3.6.5 Binomial Default with Learning
97(1)
3.6.6 The Logistic Distribution
97(1)
3.6.7 The Linear Exponential Family of Distribution
98(1)
3.7 Extreme Distributions and Tail Risks
98(6)
3.7.1 Approximation by a Generalized Pareto Distribution
100(1)
3.7.2 The Weibull Distribution
100(1)
3.7.3 The Burr Distribution
101(3)
3.8 Simulation
104(5)
4 Multivariate Probability Distributions: Applications and Risk Models
109(30)
4.1 Introduction
109(1)
4.2 Measures of Co-variation and Dependence
110(7)
4.2.1 Statistical and Causal Dependence: An Oil Example
110(2)
4.2.2 Statistical Measures of Co-dependence
112(5)
4.3 Multivariate Discrete Distributions
117(11)
4.3.1 Estimating the Bi-variate Bernoulli Parameters
124(2)
4.3.2 The Bivariate Binomial Distribution
126(1)
4.3.3 The Multivariate Poisson Probability Distribution
127(1)
4.4 The Multivariate Normal Probability Distribution
128(1)
4.5 Other Multivariate Probability Distributions (Statistics and Probability Letters, 62, 203, 47--412)
128(2)
4.6 Dependence and Copulas
130(9)
4.6.1 Copulas and Dependence Measures
135(1)
4.6.2 Copulas and Conditional Dependence
136(3)
5 Temporal Risk Processes
139(56)
5.1 Time, Memory and Causal Dependence
139(2)
5.2 Time and Change: Modeling (Markov) Random Walk
141(12)
5.2.1 Modeling Random Walks
142(1)
5.2.2 Stochastic and Independent Processes
143(1)
5.2.3 The Bernoulli-Random Walk: A Technical Definition
143(2)
5.2.4 The Trinomial Random Walk
145(1)
5.2.5 Random Walk as a Difference Equation
145(1)
5.2.6 The Random-Poisson Continuous Time Walk
146(2)
5.2.7 The Continuous Time Continuous State Approximation
148(1)
5.2.8 The Poisson-Jump Process and its Approximation as a Brownian Model
149(1)
5.2.9 The Multiplicative Bernoulli-Random Walk Model
150(1)
5.2.10 The BD Model in Continuous Time with Distributed Times Between Jumps
151(2)
5.3 Inter-Event Times and Run Time Stochastic Models
153(1)
5.4 Randomized Random Walks and Related Processes
154(2)
5.4.1 The Randomized Random Walk Distribution
154(1)
5.4.2 Binomial-Lognormal Process
155(1)
5.5 Markov Chains
156(3)
5.6 Applications
159(7)
5.6.1 The Sums of Poisson Distributed Events Is Also Poisson
159(1)
5.6.2 Collective Risk and the Compound Poisson Process
159(2)
5.6.3 Time VaR
161(2)
5.6.4 A Portfolio Trinomial Process
163(3)
5.7 Risk Uncertainty, Rare Events and Extreme Risk Processes
166(16)
5.7.1 Hurst Index, Fractals and the Range Process
169(3)
5.7.2 R/S and Outliers Risks
172(1)
5.7.3 RVaR, TRVaR and Volatility at Risk
173(5)
5.7.4 The Generalized Pareto Distribution (GPD)
178(2)
5.7.5 The Normal Distribution and Pareto Levy Stable Distributions
180(2)
5.8 Short Term Memory, Persistence, Anti-persistence and Contagion
182(13)
5.8.1 Mathematical Calculations
183(5)
5.8.2 Persistence and the Probability of Losses in a Contagion
188(7)
6 Risk Measurement
195(28)
6.1 Introduction
195(4)
6.2 Big Data and Risk Measurement
199(2)
6.3 Decision and Risk Objective Measurements
201(3)
6.4 Risk Measurement in Various Fields
204(5)
6.4.1 Medical Risk Measurement
204(3)
6.4.2 RAM as Performance and Risk Measures
207(1)
6.4.3 Quality and Statistical Tracking
208(1)
6.4.4 Operations and Services and Risk Measurements
208(1)
6.5 Bayesian Decision Making: EMV and Information
209(2)
6.6 Multi Criteria and Ad-Hoc Objectives
211(2)
6.6.1 Perron-Froebenius Theorem and AHP
212(1)
6.6.2 The Data Envelopment Analysis and Benchmarking
212(1)
6.7 Risk Measurement Models: Axiomatic Foundations
213(5)
6.7.1 Coherent Risk Measures
213(2)
6.7.2 Axiomatic Models for Deviation Risk Measurements
215(1)
6.7.3 Absolute Deviations
215(1)
6.7.4 Inequality Measures
216(1)
6.7.5 The Variance and the VaR
216(1)
6.7.6 Entropy and Divergence (Distance) Metrics
216(2)
6.8 Functional and Generalized Risk Measurement Models
218(1)
6.9 Examples and Expectations
219(4)
6.9.1 Models Based on Ordered Distributions' Measurement
220(3)
7 Risk Valuation
223(28)
7.1 Value and Price
223(1)
7.2 Rational Expectations, Martingales and the Arrow-Debreu Complete States Preferences
224(8)
7.2.1 Rational Expectations Models: A Simple Quantitative Definition
227(2)
7.2.2 The Inverse Kernel Problem and Risk Pricing
229(3)
7.3 Utility Models and Valuation
232(10)
7.3.1 Critique of Expected Utility Theory in Measuring Preferences
234(1)
7.3.2 Examples and Problems
235(7)
7.4 Risk Prudence and Background Risk
242(4)
7.4.1 Risk, Uncertainty and Insurance
244(2)
7.5 Expected Utility Bounds
246(1)
7.6 VaR Valuation
246(2)
7.7 Valuation of Operations by Lagrange Multipliers
248(3)
8 Risk Economics and Multi-Agent CCAPM
251(32)
8.1 Introduction
251(4)
8.2 Economic Valuation and Pricing: Supply, Demand and Scarcity
255(10)
8.2.1 Valuation, Risk, and Utility Pricing: One Period Models
256(3)
8.2.2 Aggregate and Competing Consumption and Pricing Risks
259(1)
8.2.3 Two Products and Derived Consumption
260(5)
8.3 The CAPM and the CCAPM
265(5)
8.3.1 The CCAPM Model
266(3)
8.3.2 The Beta Model and Inflation Risk
269(1)
8.4 The Multi-Agent CCAPM Model: A Two Periods Model
270(13)
8.4.1 The CCAPM with Independent Prices
270(2)
8.4.2 Endogenous-Aggregate Consumption and the CCAPM
272(1)
8.4.3 The General Case with Independent Rates of Returns
273(10)
9 Risk Pricing Models: Applications
283(50)
9.1 Debt and Risk Models
283(7)
9.1.1 Market Risk Pricing Models for Credit Risk and Collaterals
284(1)
9.1.2 The Structural-Endogenous Model and the Price of Credit Relative to its Collateral
285(2)
9.1.3 Credit Risk and Swaps: A Reduced Form or Exogenous Models
287(2)
9.1.4 Pricing by Replication: Credit Default Spread
289(1)
9.2 A Debt Multi-Agent CCAPM Model
290(7)
9.3 Global Finance and Risks
297(26)
9.3.1 Pricing International Assets and Foreign Exchange Risk
300(10)
9.3.2 International Credit, Debt Leverage and the Investment Portfolio
310(7)
9.3.3 FX Rates Risk, Bonds and Equity
317(6)
9.4 Additional Applications
323(5)
9.4.1 Finance and Insurance: Pricing Contrasts and Similarities
323(2)
9.4.2 Insurance and Finance: Pricing Examples
325(1)
9.4.3 Contrasts of Actuarial and the Financial Approaches
325(1)
9.4.4 Franchises
326(1)
9.4.5 Outsourcing and Risks
327(1)
9.5 Subjective Kernel Distributions
328(5)
9.5.1 The HARA Utility
329(4)
10 Uncertainty Economics
333(42)
10.1 Introduction
334(1)
10.2 Risk and Uncertainty, Time and Pricing
334(2)
10.3 Assets Pricing with Countable and Non-countable States
336(2)
10.4 Maximization of Boltzmann Entropy
338(4)
10.5 The Subjective, the Q Distributions and BG Entropy
342(2)
10.6 The Tsallis Maximum Entropy and Incomplete Slates Preferences
344(12)
10.6.1 Tsallis Entropy and the Power Law
345(1)
10.6.2 A Mathematical Note: (Abe 1997; Borges and Roditi 1998)
346(2)
10.6.3 The Maximum Tsallis Entropy and the Power Law Distribution
348(1)
10.6.4 The Tsallis Entropy and Subjective Estimate of the M-Distribution
349(2)
10.6.5 Maximum Tsallis Entropy with Escort Probabilities
351(5)
10.7 Choice, Rationality, Bounded Rationality and Making Decision Under Uncertainty
356(8)
10.7.1 Models Sensitivity and Robustness
357(5)
10.7.2 Ex-Post Decisions and Recovery
362(2)
10.8 Uncertainty Economics, Risk Externalities and Regulation
364(11)
10.8.1 Risk Externalities, Industry and the Environmental: Energy and Pollution
366(2)
10.8.2 Networks and Externalities
368(1)
10.8.3 Infrastructure and Externalities
369(1)
10.8.4 Economics and Externalities: Pigou and Coase
370(5)
11 Strategic Risk Control and Regulation
375(62)
11.1 Introduction
375(2)
11.2 Statistical Risk Control: Inspection and Acceptance Sampling
377(10)
11.2.1 Elements Statistical Sampling
378(4)
11.2.2 Bayesian Controls---A Medical Care Case
382(3)
11.2.3 Temporal Bayesian Controls
385(2)
11.3 Risk Control with Control Charts
387(7)
11.3.1 Interpreting Charts
389(3)
11.3.2 6 Sigma and Process Capability
392(2)
11.4 Queue Control
394(9)
11.4.1 The Simple M/M/1 Queue
395(1)
11.4.2 The Simple M/M/1 Queue and Non-compliance
396(2)
11.4.3 The Continuous CSP-1 Control of Queues and Banking
398(2)
11.4.4 Networks and Queues
400(3)
11.5 Strategic Inspections and Controls (See Also Chap. 12 for a Review of Game Theory)
403(9)
11.5.1 Yield and Control in a Supplier-Customer Relationship
404(8)
11.6 Financial Regulation and Controls
412(25)
11.6.1 Financial Regulation in a Post Crisis World
412(2)
11.6.2 Statistical Controls and Regulation
414(14)
11.6.3 Private Information, Type I and II Risks and Externality Risks
428(9)
12 Games, Risk and Uncertainty
437(28)
12.1 Introduction
437(2)
12.1.1 Games, Risk and Uncertainty
439(1)
12.2 Concepts of Games and Risk
439(4)
12.3 Two-Persons Zero-Sum and Non-zero Sum Games
443(8)
12.3.1 Terms and Solution Concepts
443(1)
12.3.2 The Nash Conjecture
444(5)
12.3.3 The Numerical Solution of Two Persons-Games: The Lemke-Howson Algorithm
449(1)
12.3.4 Negotiated Solution and the Nash Equilibrium
450(1)
12.4 The Stackelberg Strategy
451(1)
12.5 Random Payoff and Strategic Risk Games
452(4)
12.5.1 A Risk Constrained Random Payoff Games: A Heuristic Interior Solution
454(2)
12.6 Bayesian Theory and Bayesian Games
456(5)
12.6.1 Bayes Decision Making
458(1)
12.6.2 Examples: Bayesian Calculus
458(3)
12.7 Mean Field Games and Finance
461(4)
References 465(38)
Index 503
Charles S. Tapiero is the Topfer Chair Distinguished Professor of Financial Engineering and Technology Management at the Polytechnic Institute of New York University. He is also founder and department head of the Risk and Financial Engineering Department, and serves as the director of its Advanced Degrees Programs. Professor Tapiero has earned a worldwide reputation as a researcher and consultant, and has sat on the boards of large firms. Professor Tapiero is currently the co-editor in chief of Risk and Decision Analysis. His fields of interests span financial engineering, risk assessment and analysis, actuarial and insurance science, computational finance, infrastructure finance, networks and supply chain risk. Professor Tapiero has contributed more than 350 papers in academic refereed journals and 14 books. His research spans risk insurance and finance, operations risk and quality, supply chain risk, stochastic and dynamic systems, range processes and R/S statistics, logistics and industrial management, operations research and decisions analysis. Professor Tapiero has held numerous positions of responsibility at the highest levels of an industrial conglomerate (Koor Industries, 1994-2000), quasi-government and government agencies (1978-1982) and professorial positions in the United States, Europe, Israel and Asia. He received his doctoral degree in Operation Research and Management from New York University's Graduate school of Business Administration, and held University positions at Columbia University, the University of Washington, Case Western Reserve University, the Hebrew University of Jerusalem, the Institute of Financial Mathematics in Montreal and ESSEC (France) before joining NYU-Poly.
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