| 000 | 11286nam a22004933i 4500 | ||
|---|---|---|---|
| 001 | EBC6383181 | ||
| 003 | MiAaPQ | ||
| 005 | 20240122001246.0 | ||
| 006 | m o d | | ||
| 007 | cr cnu|||||||| | ||
| 008 | 231124s2018 xx o ||||0 eng d | ||
| 020 |
_a9789813272569 _q(electronic bk.) |
||
| 020 | _z9789813272552 | ||
| 035 | _a(MiAaPQ)EBC6383181 | ||
| 035 | _a(Au-PeEL)EBL6383181 | ||
| 035 | _a(OCoLC)1231605086 | ||
| 040 |
_aMiAaPQ _beng _erda _epn _cMiAaPQ _dMiAaPQ |
||
| 100 | 1 | _aGlau, Kathrin. | |
| 245 | 1 | 0 | _aInnovations In Insurance, Risk- And Asset Management - Proceedings Of The Innovations In Insurance, Risk- And Asset Management Conference. |
| 250 | _a1st ed. | ||
| 264 | 1 |
_aSingapore : _bWorld Scientific Publishing Company, _c2018. |
|
| 264 | 4 | _c�2019. | |
| 300 | _a1 online resource (469 pages) | ||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
||
| 505 | 0 | _aIntro -- 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. | |
| 505 | 8 | _a3.2. 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. | |
| 505 | 8 | _a7. 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. | |
| 505 | 8 | _a2.4. 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& -- 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. | |
| 505 | 8 | _a4.1.2. 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. | |
| 505 | 8 | _a2.2.1. Implementation steps. | |
| 588 | _aDescription based on publisher supplied metadata and other sources. | ||
| 590 | _aElectronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. | ||
| 655 | 4 | _aElectronic books. | |
| 700 | 1 | _aLinders, Daniel. | |
| 700 | 1 | _aMin, Aleksey. | |
| 700 | 1 | _aScherer, Matthias. | |
| 700 | 1 | _aSchneider, Lorenz. | |
| 700 | 1 | _aZagst, Rudi. | |
| 776 | 0 | 8 |
_iPrint version: _aGlau, Kathrin _tInnovations In Insurance, Risk- And Asset Management - Proceedings Of The Innovations In Insurance, Risk- And Asset Management Conference _dSingapore : World Scientific Publishing Company,c2018 _z9789813272552 |
| 797 | 2 | _aProQuest (Firm) | |
| 856 | 4 | 0 |
_uhttps://ebookcentral.proquest.com/lib/bacm-ebooks/detail.action?docID=6383181 _zClick to View |
| 999 |
_c306288 _d306288 |
||