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

• Author: Glau, Kathrin.
• TeilnehmendeR: Linders, Daniel.; Min, Aleksey.; Scherer, Matthias.; Schneider, Lorenz.; Zagst, Rudi.
• Place / Publishing House: Singapore : : World Scientific Publishing Company,, 2018.; Ã2019.
• Year of Publication: 2018
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (469 pages)

Table of Contents

• 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.