Innovations in quantitative risk management [electronic resource] : TU München, September 2013 / Kathrin Glau, Matthias Scherer, Rudi Zagst, editors.
Glau, Kathrin, editor.; Scherer, Matthias, editor.; Zagst, Rudi, 1961- editor.
2015
HD61
Available Online

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Title Innovations in quantitative risk management [electronic resource] : TU München, September 2013 / Kathrin Glau, Matthias Scherer, Rudi Zagst, editors.
ISBN 9783319091143 electronic book
331909114X electronic book
9783319091136
DOI https://doi.org/10.1007/978-3-319-09114-3
Published Cham : Springer, 2015.
Language English
Description 1 online resource (xi, 438 pages) : illustrations.
Other Standard Identifiers 10.1007/978-3-319-09114-3 doi
Call Number HD61
Dewey Decimal Classification 658.15/5
Summary Quantitative models are omnipresent ?but often controversially discussed? in todays risk management practice. New regulations, innovative financial products, and advances in valuation techniques provide a continuous flow of challenging problems for financial engineers and risk managers alike. Designing a sound stochastic model requires finding a careful balance between parsimonious model assumptions, mathematical viability, and interpretability of the output. Moreover, data requirements and the end-user training are to be considered as well. The KPMG Center of Excellence in Risk Management conference Risk Management Reloaded and this proceedings volume contribute to bridging the gap between academia ?providing methodological advances? and practice ?having a firm understanding of the economic conditions in which a given model is used. Discussed fields of application range from asset management, credit risk, and energy to risk management issues in insurance. Methodologically, dependence modeling, multiple-curve interest rate-models, and model risk are addressed. Finally, regulatory developments and possible limits of mathematical modeling are discussed.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed January 28, 2015).
Added Author Glau, Kathrin, editor.
Scherer, Matthias, editor.
Zagst, Rudi, 1961- editor.
Series Springer proceedings in mathematics & statistics ; 99.
Available in Other Form Print version: 9783319091136
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Part I Markets, Regulation, and Model Risk
A Random Holding Period Approach for Liquidity-Inclusive Risk Management
Regulatory Developments in Risk Management: Restoring Confidence in Internal Models
Model Risk in Incomplete Markets with Jumps
Part II Financial Engineering
Bid-Ask Spread for Exotic Options Under Conic Finance
Derivative Pricing Under the Possibility of Long Memory in the supOU Stochastic Volatility Model
A Two-Sided BNS Model for Multicurrency FX Markets
Modeling the Price of Natural Gas with Temperature and Oil Price as Exogenous Factors
Copula-Specific Credit Portfolio Modeling
Implied Recovery Rates?Auctions and Models
Upside and Downside Risk Exposures of Currency Carry Trades via Tail Dependence
Part III Insurance Risk and Asset Management
Participating Life Insurance Contracts Under Risk Based Solvency Frameworks: How to Increase Capital Efficiency by Product Design
Reducing Surrender Incentives Through Fee Structure in Variable Annuities
A Variational Approach for Mean-Variance-Optimal Deterministic Consumption and Investment
Risk Control in Asset Management: Motives and Concepts
Worst-Case Scenario Portfolio Optimization Given the Probability of a Crash
Improving Optimal Terminal Value Replicating Portfolios
Part IV Computational Methods for Risk Management
Risk and Computation
Extreme Value Importance Sampling for Rare Event Risk Measurement
A Note on the Numerical Evaluation of the Hartman?Watson Density and Distribution Function
Computation of Copulas by Fourier Methods
Part V Dependence Modelling
Goodness-of-fit Tests for Archimedean Copulas in High Dimensions
Duality in Risk Aggregation
Some Consequences of the Markov Kernel Perspective of Copulas
Copula Representations for Invariant Dependence Functions
Nonparametric Copula Density Estimation Using a Petrov?Galerkin Projection.

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