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[null,"1.","Record Nr.",null,"UNINA9910349465003321"]
[null,null,"Autore",null,"Glau Kathrin"]
[null,null,"Titolo",null,"Innovations In Insurance, Risk- And Asset Management - Proceedings Of The Innovations In Insurance, Risk- And Asset Management Conference"]
[null,null,"Pubbl/distr/stampa",null,"World Scientific Publishing Co, 2018"]
[{"$nulls":4},"Singapore : , : World Scientific Publishing Company, , 2018"]
[{"$nulls":4},"©2019"]
[null,null,"ISBN",null,"981-327-256-2"]
[{"$nulls":4},"981-327-255-4"]
[null,null,"Descrizione fisica",null,"1 online resource (469 pages)"]
[null,null,"Disciplina",null,"368"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Contents -- Foreword -- Preface -- About the Editors -- Part I. Innovations in Risk Management -- 1. Behavioral Value Adjustments for Mortgage Valuation -- 1. Introduction -- 2. Literature review -- 3. A general framework for modeling behavioral risk -- 3.1. Defining behavioral risk -- 3.2. A general framework in parallel with credit risk -- 3.3. Behavioral risk adjustments -- 3.4. A general formula for portfolio valuation -- 4. Mortgage portfolio valuation with BIX model -- 4.1. Heterogeneity and granularity -- 4.2. Market factors -- 4.3. Exogenous factors -- 4.4. Marginal exercise probabilities -- 4.5. Hints for calibration -- 4.6. Survival exercise probabilities -- 4.7. Portfolio pricing -- 4.7.1. Expression for II0(X) -- 4.7.2. Expression for II1(X) -- 4.7.3. Expression for II2(X) -- 4.8. Simulation -- 5. Conclusion -- 6. Appendix -- References -- 2. Wrong-Way Risk Adjusted Exposure: Analytical Approximations for Optionsin Default Intensity Models -- 1. Introduction -- 2. Call and put risk-neutral dynamics -- 3. Expected positive exposures under no WWR -- 4. Expected positive exposures under WWR -- 5. Proxys of  ts -- 5.1. Q-expectation -- 5.2. Approximation of QCT -expectation -- 6. Potential future exposures (PFE) -- 7. Numerical experiments -- 8. Conclusion -- References -- 3. Consistent Iterated Simulation of Multivariate Defaults: Markov Indicators, Lack of Memory, Extreme-Value Copulas, and the Marshall- Olkin Distribution -- 1. Introduction -- 1.1. Problem one: \"Survival-of-"]
[{"$nulls":4},"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."]
[{"$nulls":4},"3.2. Change in dependence when iterating non-self chaining copulas -- 4. Problem two: \"Mixed default/survival\" events -- 4.1. The looping default model and the Freund distribution -- 4.2. Marshall-Olkin distributions -- 4.3. Case study: Iteration bias for selected multivariate distributions -- 5. Conclusions -- Appendix A. Alternative construction of Markovian processes -- Acknowledgments -- References -- 4. Examples of Wrong-Way Risk in CVA Induced by Devaluations on Default -- 1. Introduction -- 1.1. Overview of the modeling framework -- 2. A PDE approach for both FX-driven and equity-driven WWR -- 2.1. FX -- 2.1.1. No-arbitrage drift for the market risk-factor (FX) -- 2.1.2. Final conditions - CVA payoff -- 2.2. Equity -- 2.2.1. No-arbitrage drift for the market risk-factor (equity) -- 2.2.2. Final conditions - CVA payoff -- 3. A structural approach for equity/credit WWR -- 3.1. AT1P -- 3.1.1. Credit risk -- 3.1.2. Equity price -- 3.2. Introducing WWR -- 4. Results -- 4.1. Models calibrations -- 4.2. Equity WWR: Correlation impact -- 4.3. Equity WWR: Devaluation impact -- 4.4. FX WWR: FX Vega -- 5. Conclusions -- References -- 5. Implied Distributions from Risk-Reversals and Brexit/Trump Predictions -- 1. Introduction -- 2. Literature Review -- 3. Method -- 4. Results -- 4.1. 2016 Brexit referendum -- 4.2. 2016 US election - Trump -- 4.3. 2017 French elections -- 4.4. 2017 UK general election -- 5. Conclusions -- References -- 6. Data and Uncertainty in Extreme Risks: A Nonlinear Expectations Approach -- 1. Introduction -- 2. DR-expectations -- 2.1. Data-robust risk measures -- 3. Regularization from data -- 4. Heavy tails -- 4.1. Expected shortfall -- 4.2. Value at risk -- 4.3. Probability of loss -- 4.4. Integrated tail and Cramer-Lundberg failure probability -- 4.5. Distortion risk -- Appendix -- Acknowledgments -- References."]
[{"$nulls":4},"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. "]
[{"$nulls":4},"Model formulation with cost based criterion -- 2.2. Permanent market impact: Optimal adapted solution -- 2.3. Permanent market impact: Optimal deterministic solution."]
[{"$nulls":4},"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."]
[{"$nulls":4},"4.1.2. Option dataset -- 4.1.3. Backtesting -- 4.2. Empirical results -- 4.2.1. 2008-2009 Financial Crisis -- 4.2.2. 2013-2015 Bull markets -- 5. Conclusion -- Appendix A. Extension of Baum-Welch algorithm -- Appendix A.1. Estimation of regime-switching models -- Appendix B. Goodness-of-fit test for ARHMM -- Appendix B.1. Rosenblatt's transform -- Appendix B.2. Test statistic -- Appendix B.3. Parametric bootstrap algorithm -- References -- 13. Interest Rate Swap Valuation in the Chinese Market -- 1. Introduction -- 2. Pricing model -- 2.1. Dual curve discounting -- 2.2. Single curve discounting -- 2.3. Valuation difference -- 3. Candidates for the risk-free rate in the Chinese swap market -- 4. Numerical test -- 5. Conclusion -- References -- 14. On Consistency of the Omega Ratio with Stochastic Dominance Rules -- 1. Introduction -- 2. Omega ratios and stochastic dominance -- 3. Omega ratios and combined concave and convex stochastic dominance -- References -- 15. Chance-Risk Classification of Pension Products: Scientific Concepts and Challenges -- 1. Introduction -- 2. Typical private pension products offered in Germany -- 3. Aspects of chance-risk classification concepts -- 4. Capital market model and simulation of important product ingredients -- 5. Scientific challenges and outlook -- References -- 16. Forward versus Spot Price Modeling -- 1. Introduction -- 2. Spot and forward model -- 2.1. Spot model -- 2.2. Forward model -- 2.2.1. Wealth process model -- 3. First example: CEV model -- 4. Second example: JDCEV model -- 5. Implications for modeling -- 6. Conclusion -- Appendix A. Martingale property of driving process -- Appendix B. Density of ST in JDCEV model -- References -- 17. Replication Methods for Financial Indexes -- 1. Introduction -- 2. Replication methods -- 2.1. Factorial "]
[{"$nulls":4},"approach for strong replication -- 2.2. Weak replication."]
[{"$nulls":4},"2.2.1. Implementation steps."]
[null,null,null,"Sommario/riassunto","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 conference held at the Technical University of Munich in 2017. The topics covered include the mathematics of extreme risks, systemic risk, model uncertainty, interest rate and hybrid models, alternative investments, dynamic investment strategies, quantitative risk management, asset liability management, liability driven investments, and behavioral finance. This timely selection of topics is highly relevant for the financial industry and addresses current issues both from an academic as well as from a practitioner's point of view."]
[null,"2.","Record Nr.",null,"UNINA9910346737603321"]
[null,null,"Autore",null,"Toni Aebischer"]
[null,null,"Titolo",null,"Parasite Infections: From Experimental Models to Natural Systems"]
[null,null,"Pubbl/distr/stampa",null,"Frontiers Media SA, 2018"]
[null,null,"Descrizione fisica",null,"1 online resource (294 p.)"]
[null,null,"Collana",null,"Frontiers Research Topics"]
[null,null,"Soggetti",null,"Plasmodis"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","Eukaryotic parasites (including parasitic protozoans, worms and arthropods) are more complex and heterogeneous organisms than pathogenic bacteria and viruses. This notion implies different evolutionary strategies of host exploitation. Typically, parasites establish long-term infections and induce relatively little mortality, as they often limit pathological changes by modulating host cells and downregulating adverse immune responses. Their pattern of distribution tends to be endemic rather than epidemic. Despite these seemingly benign traits, parasites usually cause substantial chronic morbidity, thus constituting an enormous socioeconomic burden in humans, particularly in resource poor countries, and in livestock worldwide. Parasite-induced fitness costs are an evolutionary force that can shape populations and contribute to species diversity. Therefore, a thorough understanding of parasites and parasitic diseases requires detailed knowledge of the respective biochemical, molecular and immunological aspects as well as of population genetics, epidemiology and ecology. This Research Topic (RT) bridges disciplines to connect molecular, immunological and wildlife aspects of parasitic infections. The RT puts emphases on four groups of parasites: Plasmodium, Toxoplasma, Giardia and intestinal helminths. Co-infections are also covered by the RT as they represent the most common form of parasite infections in wildlife and domestic animal populations. Within the four types of parasites the following topics are addressed: (1) Experimental models: hypothesis testing, translation and limits. (2) Critical appraisal of experimental models. (3) Natural systems: Technological advances for investigations in natural parasite-host systems and studies in "]
[{"$nulls":4},"natural systems. (4) The urgent need for better models and methods in natural parasite systems. Hence, the RT covers and illustrate by the means of four main parasitic infections the parasite-host system at the molecular, cellular and organismic level."]