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E-grāmata: Carbon Finance: A Risk Management View

(Deloitte Audit Analytic, Germany), (Univ Of Duisburg-essen, Germany)
  • Formāts: 340 pages
  • Izdošanas datums: 24-Nov-2021
  • Izdevniecība: World Scientific Europe Ltd
  • Valoda: eng
  • ISBN-13: 9781800611030
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  • Formāts: 340 pages
  • Izdošanas datums: 24-Nov-2021
  • Izdevniecība: World Scientific Europe Ltd
  • Valoda: eng
  • ISBN-13: 9781800611030
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Iconoclastic physics professor and artist Andrzej Dragan presents a unique feast of knowledge on special relativity in a straightforward, progressive manner that even a savvy high school student could follow. Encompassing the derivation of Lorentz transformations to Wigner rotations and Thomas precession; from non-inertial accelerated reference frames to event horizons, curved spacetime, and static black holes; and from the Doppler effect to relativistic structure of electromagnetism, Dragan peels back the enigmatic layers of modern physics to enable a deeper understanding of Einstein's groundbreaking theory. Comprehensive and elegantly written, full of insightful apparent paradoxes and riddles, but without any complicated math, Dragan's unique overview takes the reader well beyond the orthodox verses of standard Special Relativity to the bleeding edge of ""new-fangled"" superluminal apocrypha and their relation to Quantum Theory. The book is based on a course on Special Relativity and acclaimed by students taught by Dragan who is a leader of a research group on Relativistic Quantum Information theory at the University of Warsaw and the National University of Singapore.



"Mastering climate change has been recognised as a major challenge for the current decade. Besides the physical risks of climate change, the accompanying economic risks are substantial. Carbon Finance: A Risk Management View provides an in-depth analysisof how climate change will affect all aspects of financial markets and how mathematical and statistical methods can be used to analyse, model and manage the ensuing financial risks. There is a focus on the transition risk (termed carbon risk), but also adiscussion of the impact of physical risks (as these risks are closely entangled) on the way to low carbon economies. This is a valuable overview for readers seeking an analysis of carbon risks from the perspective of financial risk management, utilisingquantitative risk management tools"--
Prometheus Bound viii
Preface xi
About the Authors xv
Part I Carbon Finance --- Fundamentals
1(80)
1 Climate Change
3(20)
1.1 Climate Change Is Happening
3(6)
1.1.1 Physical Evidence
3(2)
1.1.2 Risk Perception
5(1)
1.1.3 Scientific Evidence
6(3)
1.2 Global Risk Management
9(5)
1.2.1 International Climate Negotiations: Conference of the Parties (COP)
9(1)
1.2.2 The Paris Agreement
10(3)
1.2.3 The Role of Finance
13(1)
1.3 Analysis
14(9)
1.3.1 Uncertainty
14(3)
1.3.2 Probabilities of Temperature Increases
17(1)
1.3.3 Reference Scenarios
18(2)
1.3.4 Resume
20(3)
2 Climate Economics
23(22)
2.1 Climate Change Policy
23(3)
2.1.1 Economic Tools
23(3)
2.2 Putting a Price on Carbon
26(5)
2.2.1 Tax vs. Emission Trading
27(2)
2.2.2 Multiple Policy Instruments
29(2)
2.3 Emission Trading Schemes
31(8)
2.3.1 Design of an ETS
31(1)
2.3.2 Current Emission Trading Schemes
32(2)
2.3.3 The EU ETS as an Example of Trading Schemes
34(3)
2.3.4 EUA Prices
37(2)
2.4 Carbon Credits
39(6)
3 Climate Risks
45(16)
3.1 Carbon Risks
45(5)
3.2 Carbon Metrics
50(2)
3.3 Sustainable Finance
52(2)
3.4 Stranded Assets and Carbon Bubble
54(2)
3.5 Tail Risk
56(5)
4 Theory of Carbon Risk
61(20)
4.1 What Should the Price for Carbon Be?
61(7)
4.1.1 Utility Framework
61(2)
4.1.2 Risk Aversion and Intertemporal Substitution
63(3)
4.1.3 Ambiguity and Uncertainty
66(2)
4.2 Asset Pricing Approaches
68(6)
4.2.1 Discrete-Time Models
68(2)
4.2.2 Continuous-Time Models
70(4)
4.3 What Do the Markets Think?
74(7)
4.3.1 Green Impact Investing
74(4)
4.3.2 Green vs. Brown Stocks
78(3)
Part II Carbon Finance --- Data
81(84)
5 Carbon Finance and Artificial Intelligence
83(26)
5.1 A Brief Introduction to Machine Learning
84(8)
5.1.1 Linear and Generalised Linear Models
84(3)
5.1.2 A Classification of ML Methods
87(2)
5.1.3 ML-Based Estimation of Carbon Intensities
89(3)
5.2 Applying AI to Carbon Risk
92(10)
5.2.1 Motivation
92(3)
5.2.2 Building an AI model
95(6)
5.2.3 Data Protection/Differential Privacy
101(1)
5.3 Federated Learning
102(7)
5.3.1 Horizontal Federated Learning
102(3)
5.3.2 Vertical Federated Learning
105(1)
5.3.3 Rationale for the Use of Federated Learning
105(4)
6 Disclosure and Data Requirement
109(16)
6.1 Disclosure
109(4)
6.1.1 The Need for Disclosure Standards
109(1)
6.1.2 Current State of Disclosure
110(2)
6.1.3 Data Provider
112(1)
6.2 Data Quality
113(2)
6.3 Federated Disclosure and Sustainability Ratings
115(10)
6.3.1 Methodology of Ratings
115(3)
6.3.2 Risks
118(2)
6.3.3 Security Techniques
120(2)
6.3.4 Application and Outlook
122(3)
7 Markets for Trading Carbon Risk
125(40)
7.1 Carbon Rights and Offsets
125(11)
7.1.1 Definitions
125(2)
7.1.2 Current Status of the Voluntary Carbon Market
127(1)
7.1.3 Risks Associated with Tradable Carbon Rights
128(4)
7.1.4 Role of the Trading Infrastructure
132(2)
7.1.5 Market Integrity, Transparency, and Fairness
134(1)
7.1.6 Challenges in the Carbon Market
135(1)
7.2 Development of Carbon Credit Markets
136(3)
7.3 Blockchains --- A Short Introduction
139(9)
7.3.1 General Principles
139(7)
7.3.2 Blockchains as Trading Infrastructure
146(2)
7.4 A Blockchain-Based Trading Infrastructure for Carbon Credits
148(17)
7.4.1 Setting Up Trading Infrastructure
151(9)
7.4.2 Blockchain Use Cases
160(5)
Part III Carbon Finance --- Markets
165(104)
8 Emission Certificates on Financial and Energy Markets
167(18)
8.1 Dynamics of EU ETS Certificates
167(4)
8.2 Stochastic Models for Emission Certificates
171(6)
8.2.1 Equilibrium Models
171(3)
8.2.2 Reduced-Form Models
174(3)
8.3 Effects on Financial and Energy Markets
177(8)
8.3.1 EUA Certificates and Financial Markets
177(3)
8.3.2 EUA Certificates on Electricity Markets
180(1)
8.3.3 Green Power Purchase Agreements
181(4)
9 Carbon Risk and Empirical Asset Pricing
185(14)
9.1 Constructing Factor Models
185(3)
9.1.1 The Capital Asset Pricing Model and Multi-Factor Models
185(2)
9.1.2 Panel Regression
187(1)
9.2 Stock Returns and Carbon Risk
188(5)
9.2.1 Carbon Risk as an Additional Factor in Multi-Factor Models
188(2)
9.2.2 Is There a Carbon Risk Premium in Equity Markets?
190(3)
9.3 Carbon Risk, Cost of Equity Capital, and Cost of Debt
193(2)
9.4 Carbon Risk and Machine Learning
195(4)
10 Carbon Risk and Default Risk
199(20)
10.1 Modelling Default Risk
199(5)
10.1.1 Merton's Structural Model
199(3)
10.1.2 Credit Default Swaps
202(1)
10.1.3 The Importance of CDS Spreads for (Bank) Risk Management
203(1)
10.1.4 Determinants of CDS Spreads
203(1)
10.2 CDS Spreads and Carbon Risk
204(5)
10.2.1 ESG Factors
204(2)
10.2.2 ESG-Based Carbon Indicator
206(3)
10.3 Heterogeneity of Carbon Risk Effect
209(3)
10.3.1 Quantile (Panel-) Regression
209(1)
10.3.2 Quantile Analysis of CDS Spreads
210(2)
10.4 Carbon Risk and Bonds
212(7)
10.4.1 Bond Prices and Carbon Risk
213(2)
10.4.2 Further Bond Market Studies
215(4)
11 Green Bonds
219(12)
11.1 Green Bond Markets
219(5)
11.2 Green Bond Standards
224(2)
11.3 Green Bond Premium
226(5)
12 Carbon Risk and Financial Institutions
231(14)
12.1 Standard Risk-Management Considerations
231(2)
12.2 The Role of Central Banks
233(2)
12.3 Stress Tests
235(10)
12.3.1 Design of Stress Tests
235(5)
12.3.2 Applications of Stress Tests
240(5)
13 Carbon Risk and Investors
245(24)
13.1 Carbon Risk and Investment Portfolios
245(3)
13.1.1 The Current State of Play
245(2)
13.1.2 Carbon Accounting for Equity Portfolios
247(1)
13.2 Hedging Climate Risk
248(4)
13.2.1 A News-Based Climate Risk Indicator
248(1)
13.2.2 Portfolio Approaches
249(3)
13.3 Sustainability Optimisation
252(17)
13.3.1 Introduction
252(1)
13.3.2 Sustainability Filters: Relevant Data and Example Applications
252(9)
13.3.3 Portfolio Optimisation under Sustainability Constraints: A Mathematical Model
261(8)
Appendix A Basics from Probability Theory and Financial Mathematics
269(12)
Appendix B Federated Learning Algorithm
281(16)
B.1 The Algorithm
281(7)
B.1.1 Federated Median in the Range [ 0, 1]
281(2)
B.1.2 Rank Statistics
283(1)
B.1.3 Determining Participating Companies
283(1)
B.1.4 Remarks on Optimisation
284(1)
B.1.5 Local Rank Determination
285(3)
B.2 Simulation
288(5)
B.3 Concluding Remarks
293(4)
Bibliography 297(14)
Index 311