Innovations In Insurance, Risk- And Asset Management - Proceedings Of The Innovations In Insurance, Risk- And Asset Management Conference

This book covers recent developments in the interdisciplinary fields of actuarial science, quantitative finance, risk- and asset management. The authors are leading experts from academia and practice who participated in Innovations in Insurance, Risk- and Asset Management, an international conferenc...

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Detalles Bibliográficos
Autor principal:	Glau, Kathrin (-)
Autor Corporativo:	Innovations in Insurance, Risk- and Asset Management (Conference) sponsoring body (sponsoring body)
Otros Autores:	Linders, Daniel, Min, Aleksey, Scherer, Matthias, Schneider, Lorenz, Zagst, Rudi
Formato:	Libro electrónico
Idioma:	Inglés
Publicado:	Singapore : World Scientific Publishing Co 2018 2018.
Materias:	Dynamic Hedging Uncertainty Quantification Actuarial Science Copula Exchange-Traded Funds Autoregressive Hidden Markov Models Fixed Income Reinsurance Stochastic Processes for Finance Risk Measure Bayesian Finance Insurance Replicating Portfolio Risk Classification Stochastic Dominance
Ver en Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009438375606719

Tabla de Contenidos:

Intro
Contents
Foreword
Preface
About the Editors
Part I. Innovations in Risk Management
1. Behavioral Value Adjustments for Mortgage Valuation
1. Introduction
2. Literature review
3. A general framework for modeling behavioral risk
3.1. Defining behavioral risk
3.2. A general framework in parallel with credit risk
3.3. Behavioral risk adjustments
3.4. A general formula for portfolio valuation
4. Mortgage portfolio valuation with BIX model
4.1. Heterogeneity and granularity
4.2. Market factors
4.3. Exogenous factors
4.4. Marginal exercise probabilities
4.5. Hints for calibration
4.6. Survival exercise probabilities
4.7. Portfolio pricing
4.7.1. Expression for II0(X)
4.7.2. Expression for II1(X)
4.7.3. Expression for II2(X)
4.8. Simulation
5. Conclusion
6. Appendix
References
2. Wrong-Way Risk Adjusted Exposure: Analytical Approximations for Optionsin Default Intensity Models
1. Introduction
2. Call and put risk-neutral dynamics
3. Expected positive exposures under no WWR
4. Expected positive exposures under WWR
5. Proxys of ts
5.1. Q-expectation
5.2. Approximation of QCT -expectation
6. Potential future exposures (PFE)
7. Numerical experiments
8. Conclusion
References
3. Consistent Iterated Simulation of Multivariate Defaults: Markov Indicators, Lack of Memory, Extreme-Value Copulas, and the Marshall- Olkin Distribution
1. Introduction
1.1. Problem one: "Survival-of-all" events
1.2. Problem two: "Mixed default/survival" events
1.3. Structure of the paper
2. Default-time distributions and survival-indicator processes
2.1. Markovian survival indicator-processes
2.2. Lack-of-memory properties
3. Problem one: Iterating "survival-of-all
3.1. Lack-of-memory properties revisited.
3.2. Change in dependence when iterating non-self chaining copulas
4. Problem two: "Mixed default/survival" events
4.1. The looping default model and the Freund distribution
4.2. Marshall-Olkin distributions
4.3. Case study: Iteration bias for selected multivariate distributions
5. Conclusions
Appendix A. Alternative construction of Markovian processes
Acknowledgments
References
4. Examples of Wrong-Way Risk in CVA Induced by Devaluations on Default
1. Introduction
1.1. Overview of the modeling framework
2. A PDE approach for both FX-driven and equity-driven WWR
2.1. FX
2.1.1. No-arbitrage drift for the market risk-factor (FX)
2.1.2. Final conditions - CVA payoff
2.2. Equity
2.2.1. No-arbitrage drift for the market risk-factor (equity)
2.2.2. Final conditions - CVA payoff
3. A structural approach for equity/credit WWR
3.1. AT1P
3.1.1. Credit risk
3.1.2. Equity price
3.2. Introducing WWR
4. Results
4.1. Models calibrations
4.2. Equity WWR: Correlation impact
4.3. Equity WWR: Devaluation impact
4.4. FX WWR: FX Vega
5. Conclusions
References
5. Implied Distributions from Risk-Reversals and Brexit/Trump Predictions
1. Introduction
2. Literature Review
3. Method
4. Results
4.1. 2016 Brexit referendum
4.2. 2016 US election - Trump
4.3. 2017 French elections
4.4. 2017 UK general election
5. Conclusions
References
6. Data and Uncertainty in Extreme Risks: A Nonlinear Expectations Approach
1. Introduction
2. DR-expectations
2.1. Data-robust risk measures
3. Regularization from data
4. Heavy tails
4.1. Expected shortfall
4.2. Value at risk
4.3. Probability of loss
4.4. Integrated tail and Cramer-Lundberg failure probability
4.5. Distortion risk
Appendix
Acknowledgments
References.
7. Intrinsic Risk Measures
1. Introduction
2. Terminology and preliminaries
2.1. Acceptance sets
2.2. Traditional risk measures
2.2.1. Coherent risk measures
2.2.2. Convex risk measures
2.2.3. Cash-subadditivity and quasi-convexity of risk measures
2.2.4. General monetary risk measures
3. Intrinsic risk measures
3.1. Fundamental concepts
3.2. Representation on conic acceptance sets
3.3. Efficiency of the intrinsic approach
3.4. Dual representations on convex acceptance sets
4. Conclusion
Bibliography
8. Pathwise Construction of Affine Processes
1. Introduction
2. Preliminaries
2.1. Notation
2.2. Affine processes
2.3. Towards the multivariate Lamperti transform
2.4. Affine processes of Heston type
3. Existence of the solution of the time-change equation
3.1. The setting
3.2. The core of the proof
3.2.1. Approximation of the vector field
3.2.2. The algorithm
4. Pathwise construction of affine processes with time-change
Bibliography
Part II. Innovations in Insurance and Asset Management
9. Fixed-Income Returns from Hedge Funds with Negative Fee Structures: Valuation and Risk Analysis
1. Introduction
2. Hedge fund fee structures: From traditional fee structures to negative fees
2.1. Traditional fee structures
2.2. From first-loss to negative first-loss fee structure
3. Pricing the payoffs
4. Risk analysis of the investor's position as a bond
4.1. Impact of the manager's deposit c
5. Conclusion
References
10. Static Versus Adapted Optimal Execution Strategies in Two Benchmark Trading Models
1. Introduction
2. Discrete time trading with information flow
2.1. Model formulation with cost based criterion
2.2. Permanent market impact: Optimal adapted solution
2.3. Permanent market impact: Optimal deterministic solution.
2.4. Permanent market impact: Adapted vs deterministic solution
3. Continuous time trading with risk function
3.1. Model formulation with cost and risk based criterion
3.2. Optimal adapted solution under temporary and permanent impact
3.3. Optimal static solution under temporary and permanent impact
3.4. Comparison of optimal static and adapted solutions
4. Conclusions and further research
References
11. Liability Driven Investments with a Link to Behavioral Finance
1. Introduction
2. A model for assets and liabilities
3. Expected utility framework
3.1. The optimization problem
4. Extension to cumulative prospect theory
4.1. The optimization problem
4.2. Probability distortion function
5. Comparison
5.1. Partial surplus optimization
5.2. Connection between CPT optimization, funding ratio optimization and partial surplus optimization
6. Conclusion
Acknowledgment
Appendix A. Solution of the HJB equation
Appendix B. Quantile optimization approach
Appendix C. Probability distortion
Appendix D. Replicating strategies for selected pay-offs
Bibliography
12. Option Pricing and Hedging for Discrete Time Autoregressive Hidden Markov Model
1. Introduction
2. Regime-switching autoregressive models
2.1. Regime prediction
2.1.1. Filtering algorithm
2.1.2. Conditional distribution
2.1.3. Stationary distribution in the Gaussian case
2.2. Estimation of parameters
2.3. Goodness-of-fit test and selection of the number of regimes
2.4. Application to S&amp
P 500 daily returns
3. Optimal discrete time hedging
3.1. Implementation issues
3.1.1. Using regime predictions
3.2. Optimal hedging vs delta-hedging
3.3. Simulated hedging errors
4. Out-of-sample vanilla pricing and hedging
4.1. Methodology
4.1.1. The underlying asset.
4.1.2. Option dataset
4.1.3. Backtesting
4.2. Empirical results
4.2.1. 2008-2009 Financial Crisis
4.2.2. 2013-2015 Bull markets
5. Conclusion
Appendix A. Extension of Baum-Welch algorithm
Appendix A.1. Estimation of regime-switching models
Appendix B. Goodness-of-fit test for ARHMM
Appendix B.1. Rosenblatt's transform
Appendix B.2. Test statistic
Appendix B.3. Parametric bootstrap algorithm
References
13. Interest Rate Swap Valuation in the Chinese Market
1. Introduction
2. Pricing model
2.1. Dual curve discounting
2.2. Single curve discounting
2.3. Valuation difference
3. Candidates for the risk-free rate in the Chinese swap market
4. Numerical test
5. Conclusion
References
14. On Consistency of the Omega Ratio with Stochastic Dominance Rules
1. Introduction
2. Omega ratios and stochastic dominance
3. Omega ratios and combined concave and convex stochastic dominance
References
15. Chance-Risk Classification of Pension Products: Scientific Concepts and Challenges
1. Introduction
2. Typical private pension products offered in Germany
3. Aspects of chance-risk classification concepts
4. Capital market model and simulation of important product ingredients
5. Scientific challenges and outlook
References
16. Forward versus Spot Price Modeling
1. Introduction
2. Spot and forward model
2.1. Spot model
2.2. Forward model
2.2.1. Wealth process model
3. First example: CEV model
4. Second example: JDCEV model
5. Implications for modeling
6. Conclusion
Appendix A. Martingale property of driving process
Appendix B. Density of ST in JDCEV model
References
17. Replication Methods for Financial Indexes
1. Introduction
2. Replication methods
2.1. Factorial approach for strong replication
2.2. Weak replication.
2.2.1. Implementation steps.

Innovations In Insurance, Risk- And Asset Management - Proceedings Of The Innovations In Insurance, Risk- And Asset Management Conference

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