Recent years have witnessed a growing importance of quantitative methods in both financial research and industry. This development requires the use of advanced techniques on a theoretical and applied level, especially when it comes to the quantification of risk and the valuation of modern financial products. Applied Quantitative Finance (2nd edition) provides a comprehensive and state-of-the-art treatment of cutting-edge topics and methods. It provides solutions to and presents theoretical developments in many practical problems such as risk management, pricing of credit derivatives, quantification of volatility and copula modelling. The synthesis of theory and practice supported by computational tools is reflected in the selection of topics as well as in a finely tuned balance of scientific contributions on practical implementation and theoretical concepts. This linkage between theory and practice offers theoreticians insights into considerations of applicability and, vice versa, provides practitioners comfortable access to new techniques in quantitative finance. Themes that are dominant in current research and which are presented in this book include among others the valuation of Collaterized Debt Obligations (CDOs), the high-frequency analysis of market liquidity, the pricing of Bermuda options and realized volatility. All Quantlets for the calculation of the given examples are downloadable from the Springer web pages.
Author(s): Wolfgang Karl Härdle; Cathy Yi Chen; Ludger Overbeck
Publisher: Springer
Year: 2017
Preface to the Third Edition
Contents
Part I Market Risk
1 VaR in High Dimensional Systems-A Conditional Correlation Approach
1.1 Introduction
1.2 Half-Vec Multivariate GARCH Models
1.3 Correlation Models
1.3.1 Motivation
1.3.2 Log-Likelihood Decomposition
1.3.3 Constant Conditional Correlation Model
1.3.4 Dynamic Conditional Correlation Model
1.3.5 Inference in the Correlation Models
1.3.6 Generalizations of the DCC Model
1.4 Value-at-Risk
1.5 An Empirical Illustration
1.5.1 Equal and Value Weighted Portfolios
1.5.2 Estimation Results
References
2 Multivariate Volatility Models
2.1 Introduction
2.1.1 Model Specifications
2.1.2 Estimation of the BEKK Model
2.2 An Empirical Illustration
2.2.1 Data Description
2.2.2 Estimating Bivariate GARCH
2.2.3 Estimating the (co)variance Processes
2.3 Forecasting Exchange Rate Densities
References
3 Portfolio Selection with Spectral Risk Measures
3.1 Introduction
3.2 Backgrounds
3.2.1 Coherent Measures of Risk
3.2.2 Utility Function
3.2.3 Spectral Measures of Risk
3.2.4 Vine Copulae: C- and D-Vines
3.3 Methodology
3.4 Simulation Study
3.4.1 A 2-Dimensional Case
3.4.2 The Impacts of Tail-Dependence
3.4.3 The Impact of the Degrees of Risk Aversion
3.5 Empirical Studies
3.6 Concluding Remarks
References
4 Implementation of Local Stochastic Volatility Model in FX Derivatives
4.1 Introduction
4.2 Model Calibration
4.3 Pricing (Backward PDE and Forward Monte Carlo)
4.4 Empirical Results
4.5 Conclusion and Future Works
References
Part II Credit Risk
5 Estimating Distance-to-Default with a Sector-Specific Liability Adjustment via Sequential Monte Carlo
5.1 Introduction
5.2 DTD Subject to a Sector-Specific Liability Adjustment
5.2.1 The Structural Credit Risk Model with a Common Liability Adjustment
5.2.2 The Transformed-Data Likelihood
5.3 Parameter Estimation by the Density-Tempered Expanding-Data Sequential Monte Carlo
5.4 Empirical Implementation
5.4.1 Data
5.4.2 Results
References
6 Risk Measurement with Spectral Capital Allocation
6.1 Introduction
6.2 Review of Coherent Risk Measures and Allocation
6.2.1 Coherent Risk Measures
6.2.2 Spectral Risk Measures
6.2.3 Coherent Allocation Measures
6.2.4 Spectral Allocation Measures
6.3 Weight Function and Mixing Measure
6.4 Risk Aversion
6.5 Implementation
6.5.1 Mixing Representation
6.5.2 Density Representation
6.6 Credit Portfolio Model
6.7 Examples
6.7.1 Weighting Scheme
6.7.2 Concrete Example
6.8 Summary
References
7 Market Based Credit Rating and Its Applications
7.1 Introduction
7.2 Methodology
7.2.1 Modeling and Forecasting
7.2.2 Clustering
7.3 Empirical Analysis
7.3.1 Modeling and Forecasting
7.3.2 Cluster Analysis
7.3.3 Discussion
7.4 Concluding Remarks
References
8 Using Public Information to Predict Corporate Default Risk
8.1 Introduction
8.2 Literature Review
8.3 Econometric Models
8.3.1 Logistic Models for Default Rate
8.3.2 Default Models Including News Information
8.3.3 Bayesian Network Model
8.4 Extracting News Information
8.4.1 News Keywords
8.4.2 Keyword Conversion
8.5 Empirical Analysis and Results
8.5.1 Empirical Models
8.5.2 Variable Selection
8.5.3 Adding News Variables
8.6 Conclusions
References
9 Stress Testing in Credit Portfolio Models
9.1 Introduction
9.2 Quantitative Framework for Stress Testing
9.2.1 Definition of Asset and Default Correlations
9.3 Factor Stress Methodology
9.3.1 Specification of Stress Scenarios
9.3.2 Implementation of Stress Scenarios in Credit Portfolio Models
9.3.3 Calculation of Stressed Risk Capital
9.3.4 Case Study
9.4 Stressed Correlations and Default Probabilities
9.4.1 Distribution of Model Variables
9.4.2 Asset Correlations Under Stress
9.4.3 Default Probabilities and Default Correlations Under Stress
9.5 Risk Measures
9.6 Conclusion
References
10 Penalized Independent Factor
10.1 Introduction
10.2 Data
10.3 Penalized Independent Factor
10.3.1 Independent Component's Density: NIG
10.3.2 Penalty Function: SCAD
10.3.3 Estimation
10.3.4 Property of Estimator
10.4 Simulation
10.4.1 Experiment 1: 3 Dimensional Data
10.4.2 Experiment 2: Large Dimensional Data
10.5 Real Data Analysis
10.6 Conclusion
References
11 Term Structure of Loss Cascades in Portfolio Securitisation
11.1 Introduction
11.2 Loss Distribution of Uniform Portfolio
11.3 Time Slicing
11.4 Loss Cascades
11.5 Results
11.5.1 Other Loss Distributions
11.5.2 Variable Portfolio Quality
11.6 Conclusion
References
12 Credit Rating Score Analysis
12.1 Introduction
12.2 Principal Components Analysis of Factor Scores
12.2.1 Cross Validation via Leave-One-Out
12.3 Adjusted Weighting of Factor Scores
12.3.1 Match Expert Score
12.3.2 Cross Validation via Leave-One-Out
12.3.3 Widest Projection Spread
12.4 Conclusion
Reference
Part III Dynamics Risk Measurement
13 Copulae in High Dimensions: An Introduction
13.1 Introduction
13.2 Bivariate Copula
13.2.1 Copula Families
13.2.2 Bivariate Copula and Dependence Measures
13.3 Multivariate Copula: Primer and State-of-Art
13.3.1 Extensions of Simple and Elliptical Bivariate Copulae
13.3.2 Hierarchical Archimedean Copula
13.3.3 Factor Copula
13.3.4 Vine Copula
13.4 Estimation Methods
13.4.1 Parametric Margins
13.4.2 Non-parametric Margins
13.5 Goodness-of-Fit Tests for Copulae
13.6 Empirical Study
13.7 Conclusion
References
14 Measuring and Modeling Risk Using High-Frequency Data
14.1 Introduction
14.2 Market Microstructure Effects
14.3 Stylized Facts of Realized Volatility
14.4 Realized Volatility Models
14.5 Time-Varying Betas
14.5.1 The Conditional CAPM
14.5.2 Realized Betas
14.6 Summary
References
15 Measuring Financial Risk in Energy Markets
15.1 Introduction
15.2 Methodology and Data
15.3 Backtesting Results
15.4 Conclusion
References
16 Risk Analysis of Cryptocurrency as an Alternative Asset Class
16.1 Introduction
16.2 Data Collection
16.2.1 Parse the Balance Information of Each Address from the Downloaded Block Chain Using C++
16.2.2 Parse Other Fundamental Variables of Bitcoin
16.2.3 Historical Price Data for Auroracoin Are from a Data Provider Named Myip
16.2.4 Parse Other Fundamental Variables of Auroracoin from Online Block Chain Explorer Using Python
16.3 Methodology
16.4 Empirical Results
16.4.1 Data Visualization
16.4.2 Power-Law Estimation and Empirical Analysis
16.5 Other Risk Analysis
16.6 Conclusion
References
17 Time Varying Quantile Lasso
17.1 Introduction
17.2 Lasso Method
17.2.1 Lasso as an Optimization Problem
17.2.2 Choosing the Penalization Parameter
17.2.3 Algorithms to Solve Lasso
17.3 Simulation Study
17.3.1 Penalty λ Dependent on Variance σ2
17.3.2 Penalty λ Dependent on Model Size q
17.3.3 Penalty λ Dependent on Design
17.3.4 All Factors Affecting the Value of λ
17.4 Empirical Analysis
17.4.1 Data Description
17.4.2 Construction of Time Series of λ"0362λ
17.4.3 λ"0362λ and Systemic Risk Measures
References
18 Dynamic Topic Modelling for Cryptocurrency Community Forums
18.1 Introduction
18.2 Data
18.3 Topic Modelling
18.4 Preprocessing
18.5 Trends
18.6 Choosing K and Analysis
18.7 Detection
18.8 Conclusion
References
19 Erratum to: Copulae in High Dimensions: An Introduction
Erratum to:Chapter 13 in: W.K. Härdle et al. (eds.), Applied Quantitative Finance, Statistics and Computing, https://doi.org/10.1007/978-3-662-54486-0_13
