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Innovations in Quantitative Risk Management : TU München, September 2013.

Bibliographic Details
Main Author: Glau, Kathrin.
Other Authors: Scherer, Matthias., Zagst, Rudi.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2015.
Edition:1st ed.
Series:Springer Proceedings in Mathematics and Statistics Series
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Preface I
  • Preface II
  • Contents
  • Part I Markets, Regulation,and Model Risk
  • A Random Holding Period Approach for Liquidity-Inclusive Risk Management
  • 1 Introduction
  • 1.1 Earlier Literature
  • 1.2 Different Risk Horizons Are Acknowledged by BCBS
  • 2 The Univariate Case
  • 2.1 A Brief Review on the Stochastic Holding Period Framework
  • 2.2 Semi-analytic Solutions and Simulations
  • 3 Dependence Modeling: A Bivariate Case
  • 4 Calibration with Liquidity Data
  • 4.1 Dependencies Between Liquidity, Credit, and Market Risk
  • 4.2 Marginal Distributions of SHPs
  • 5 Conclusions
  • References
  • Regulatory Developments in Risk Management: Restoring Confidence in Internal Models
  • 1 Introduction
  • 2 Loss of Confidence in Internal Models
  • -How Did It Happen?
  • 2.1 An Example from the First Years of the Crisis
  • 2.2 Divergence of Model Results
  • 3 Alternatives to Internal Models
  • 3.1 Overview
  • 3.2 The Leverage Ratio
  • 3.3 Regulatory Standardised Approaches
  • 4 Ways of Restoring Confidence
  • 4.1 Overview
  • 4.2 Reducing the Variation in Model Results Through Standardisation
  • 4.3 Enhancing Transparency
  • 4.4 Highlighting the Positive Developments as a Result of the Trading Book Review
  • 4.5 Strengthening the Use Test Concept
  • 4.6 A Comprehensive Approach to Model Validation
  • 4.7 Quantification and Capitalisation of Model Risk
  • 4.8 Voluntary Commitment by Banks to a Code of ``Model Ethics''
  • 4.9 Other Approaches
  • 5 Conclusion
  • References
  • Model Risk in Incomplete Markets with Jumps
  • 1 Introduction
  • 2 Losses from Hedged Positions
  • 2.1 Market and Model Setup
  • 2.2 Loss Process
  • 2.3 Loss Distribution
  • 3 Measures of Model Risk
  • 3.1 Value-at-Risk and Expected Shortfall
  • 3.2 Axioms for Measures of Model Risk
  • 4 Hedge Differences
  • 5 Application to Energy Markets
  • References.
  • Part II Financial Engineering
  • Bid-Ask Spread for Exotic Options under Conic Finance
  • 1 Introduction
  • 2 Exotic Bid-Ask Spread
  • 3 Conclusion
  • References
  • Derivative Pricing under the Possibility of Long Memory in the supOU Stochastic Volatility Model
  • 1 Introduction
  • 2 A Review of the supOU Stochastic Volatility Model
  • 3 Martingale Conditions
  • 4 Fourier Pricing in the supOU Stochastic Volatility Model
  • 4.1 A Review on Fourier Pricing
  • 4.2 The Characteristic Function
  • 4.3 Regularity of the Moment Generating Function
  • 5 Examples
  • 5.1 Concrete Specifications
  • 5.2 Calibration and an Illustrative Example
  • References
  • A Two-Sided BNS Model for Multicurrency FX Markets
  • 1 Introduction
  • 2 The Two-Sided Barndorff
  • Nielsen
  • Shephard Model Class
  • 3 A Tractable Multivariate Extension of the Two-Sided Γ-OU-BNS Model
  • 4 Modeling Two FX Rates with a Bivariate Two-Sided Γ-OU-BNS Model
  • 4.1 The Dependence Structure of the Lévy Drivers
  • 4.2 Implicitly Defined Models
  • 5 Application: Calibration to FX Rates and Pricing of Bivariate FX Derivatives
  • 5.1 Data
  • 5.2 Model Setup
  • 5.3 Calibration
  • 6 Conclusion and Outlook
  • References
  • Modeling the Price of Natural Gas with Temperature and Oil Price as Exogenous Factors
  • 1 Introduction
  • 2 A Review of the Model by Stoll and Wiebauer (2010)
  • 3 The Oil Price Dependence of Gas Prices
  • 4 Model Calibration with Temperature and Oil Price
  • 4.1 Oil Price Model
  • 4.2 Temperature Model
  • 4.3 The Residual Stochastic Process
  • 5 Option Valuation by Least Squares Monte Carlo Including Exogenous Components
  • 5.1 Extensions of Least Squares Monte Carlo Algorithm Including Exogenous Components
  • 5.2 Influence of Exogenous Components on Valuation Results
  • 6 Conclusion
  • References
  • Copula-Specific Credit Portfolio Modeling
  • 1 Introduction.
  • 2 Copulas Under Consideration
  • 3 A Comparison Between CreditRisk+ and CreditMetrics
  • 3.1 Preliminary Notes and General Remarks
  • 3.2 Theoretical Background
  • 4 Results on Estimated Copulas and Risk Figures
  • 4.1 Portfolio and Model Calibration
  • 4.2 Parametrization of Marginal Distributions
  • 4.3 Estimation of Copulas
  • 4.4 Effect of the Copula on the Risk Figures and the Tail of the Loss Distribution
  • 5 Summary
  • References
  • Implied Recovery Rates
  • -Auctions and Models
  • 1 Introduction
  • 2 CDS Settlement: Credit Auction
  • 2.1 Initial Biding Period
  • 2.2 Dutch Auction
  • 2.3 Summary of the Auction Procedure
  • 3 Examples of Implied Recovery Models
  • 3.1 Cox
  • Ingersoll
  • Ross Type Reduced-Form Model
  • 3.2 Pure Recovery Model
  • 4 Conclusion and Outlook
  • References
  • Upside and Downside Risk Exposures of Currency Carry Trades via Tail Dependence
  • 1 Currency Carry Trade and Uncovered Interest Rate Parity
  • 2 Interpreting Tail Dependence as Financial Risk Exposure in Carry Trade Portfolios
  • 3 Generalised Archimedean Copula Models for Currency Exchange Rate Baskets
  • 4 Currency Basket Model Estimations via Inference Function for the Margins
  • 4.1 Stage 1: Fitting the Marginal Distributions via MLE
  • 4.2 Stage 2: Fitting the Mixture Copula via MLE
  • 5 Exchange Rate Multivariate Data Description and Currency Portfolio Construction
  • 6 Results and Discussion
  • 6.1 Tail Dependence Results
  • 6.2 Pairwise Decomposition of Basket Tail Dependence
  • 6.3 Understanding the Tail Exposure Associated with the Carry Trade and Its Role in the UIP Puzzle
  • 7 Conclusion
  • References
  • Part III Insurance Riskand Asset Management
  • Participating Life Insurance Contracts under Risk Based Solvency Frameworks: How to Increase Capital Efficiency by Product Design
  • 1 Introduction
  • 2 Considered Products.
  • 2.1 The Traditional Product
  • 2.2 Alternative Products
  • 3 Stochastic Modeling and Analyzed Key Figures
  • 3.1 The Financial Market Model
  • 3.2 The Asset-Liability Model
  • 3.3 Key Drivers for Capital Efficiency
  • 4 Results
  • 4.1 Assumptions
  • 4.2 Comparison of Product Designs
  • 4.3 Sensitivity Analyses
  • 4.4 Reduction in the Level of Guarantee
  • 5 Conclusion and Outlook
  • References
  • Reducing Surrender Incentives Through Fee Structure in Variable Annuities
  • 1 Introduction
  • 2 Assumptions and Model
  • 2.1 Variable Annuity
  • 2.2 Benefits
  • 3 Valuation of the Surrender Option
  • 3.1 Notation and Optimal Surrender Decision
  • 3.2 Theoretical Result on Optimal Surrender Behavior
  • 3.3 Valuation of the Surrender Option Using PDEs
  • 4 Numerical Example
  • 4.1 Numerical Results
  • 5 Concluding Remarks
  • References
  • A Variational Approach for Mean-Variance-Optimal Deterministic Consumption and Investment
  • 1 Introduction
  • 2 The Mean-Variance-Optimal Deterministic Consumption and Investment Problem
  • 3 Existence of Optimal Deterministic Control Functions
  • 4 A Pontryagin Maximum Principle
  • 5 Generalized Gradients for the Objective
  • 6 Numerical Optimization by a Gradient Ascent Method
  • 7 Numerical Example
  • References
  • Risk Control in Asset Management: Motives and Concepts
  • 1 Introduction
  • 2 Risk Management for Active Portfolios
  • 2.1 Factor Structure and Portfolio Risk
  • 2.2 Allocation to Active and Passive Funds
  • 3 Dealing with Investors Downside-Risk Aversion
  • 3.1 Portfolio Insurance
  • 3.2 Popular Portfolio Insurance Strategies
  • 3.3 Performance Comparison
  • 3.4 Other Risks
  • 4 Parameter Uncertainty and Model Uncertainty
  • 4.1 Parameter Uncertainty
  • 4.2 Model Uncertainty
  • 5 Conclusion
  • References
  • Worst-Case Scenario Portfolio Optimization Given the Probability of a Crash
  • 1 Introduction.
  • 1.1 Alternative Ansatz of Korn and Wilmott
  • 1.2 Literature Review
  • 2 Setup of the Model
  • 3 Optimal Portfolios Given the Probability of a Crash
  • 4 The q-quantile Crash Hedging Strategy
  • 5 Examples
  • 5.1 Uniformly Distributed Crash Sizes
  • 5.2 Conditional Exponential Distributed Crash Sizes
  • 5.3 Conditional Exponential Distributed Crash Sizes with Exponential Distributed Crash Times
  • 6 Deterministic Portfolio Strategies
  • 7 Conclusion
  • References
  • Improving Optimal Terminal Value Replicating Portfolios
  • 1 Introduction
  • 2 The Mathematical Setup
  • 3 The Theory of Replicating Portfolios
  • 3.1 Cash-Flow Matching
  • 3.2 Discounted Terminal Value Matching
  • 4 Equivalence of Cash-Flow Matching and Discounted Terminal Value Matching
  • 5 Example
  • 6 Conclusion
  • References
  • Part IV Computational Methodsfor Risk Management
  • Risk and Computation
  • 1 Computational Risk
  • 1.1 Efficiency of Algorithms
  • 1.2 Risk of an Algorithm
  • 1.3 Eliminate the Risk
  • 1.4 Effort
  • 1.5 Example
  • 2 Assessing Structural Risk
  • 2.1 Simplest Attractor
  • 2.2 Mean-Field Models
  • 2.3 Artificial Example
  • 2.4 Structure in Phase Spaces
  • 2.5 Risk Index
  • 2.6 Example
  • 2.7 Summary
  • References
  • Extreme Value Importance Sampling for Rare Event Risk Measurement
  • 1 Introduction
  • 2 The One-Dimensional Case
  • 3 Examples
  • 3.1 Example 1: Simulation Estimators of Quantiles and TailVar for the Normal Distribution
  • 3.2 Example 2: Simulating a Portfolio Credit Risk Model
  • 4 Conclusion
  • References
  • A Note on the Numerical Evaluation of the Hartman
  • Watson Density and Distribution Function
  • 1 Introduction
  • 2 Occurrence of the Hartman
  • Watson Law
  • 3 Straightforward Implementation Based on Formula (1)
  • 4 Evaluation via Gaver
  • Stehfest Laplace Inversion.
  • 5 Evaluation via a Complex Laplace Inversion Method for the Bondesson Class.

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