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

• Superior document: Springer Proceedings in Mathematics and Statistics Series ; v.99
• Author: Glau, Kathrin.
• TeilnehmendeR: Scherer, Matthias.; Zagst, Rudi.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2015.; {copy}2015.
• Year of Publication: 2015
• Edition: 1st ed.
• Language: English
• Series: Springer Proceedings in Mathematics and Statistics Series
• Physical Description: 1 online resource (434 pages)

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.