Applied quantitative finance : theory and computational tools /

Applied quantitative finance : theory and computational tools /

Saved in:
Bibliographic Details
Author / Creator:Härdle, Wolfgang.
Imprint:Berlin ; New York : Springer, c2002.
Description:pxx, 401 p. : ill. ; 24 cm.
Language:English
Subject:
Finance -- Mathematical models.
Risk -- Mathematical models.
Finance -- Mathematical models.
Risk -- Mathematical models.
Format: E-Resource Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/4753174
Hidden Bibliographic Details
Other authors / contributors:Kleinow, T. (Torsten)
Stahl, Gerhard.
ISBN:3540434607 (pbk. : alk. paper)
Notes:"MD Tech/Method & Data Technologies"--Cover.
"e book"--Cover.
"Also available as e-book on www.i-explore.de. Use the license code at the end of the book to download the e-book"--T.p. verso.
"All Quantlets for the calculation of the given examples are executable on an XploRe Quantlet Server (XQS) and may be modified by the reader via the Internet"--P. [4] of cover.
Includes bibliographical references and index.
Table of Contents:
  • Preface
  • Contributors
  • Frequently Used Notation
  • I. Value at Risk
  • 1. Approximating Value at Risk in Conditional Gaussian Models
  • 1.1. Introduction
  • 1.1.1. The Practical Need
  • 1.1.2. Statistical Modeling for VaR
  • 1.1.3. VaR Approximations
  • 1.1.4. Pros and Cons of Delta-Gamma Approximations
  • 1.2. General Properties of Delta-Gamma-Normal Models
  • 1.3. Cornish-Fisher Approximations
  • 1.3.1. Derivation
  • 1.3.2. Properties
  • 1.4. Fourier Inversion
  • 1.4.1. Error Analysis
  • 1.4.2. Tail Behavior
  • 1.4.3. Inversion of the cdf minus the Gaussian Approximation
  • 1.5. Variance Reduction Techniques in Monte-Carlo Simulation
  • 1.5.1. Monte-Carlo Sampling Method
  • 1.5.2. Partial Monte-Carlo with Importance Sampling
  • 1.5.3. XploRe Examples
  • 2. Applications of Copulas for the Calculation of Value-at-Risk
  • 2.1. Copulas
  • 2.1.1. Definition
  • 2.1.2. Sklar's Theorem
  • 2.1.3. Examples of Copulas
  • 2.1.4. Further Important Properties of Copulas
  • 2.2. Computing Value-at-Risk with Copulas
  • 2.2.1. Selecting the Marginal Distributions
  • 2.2.2. Selecting a Copula
  • 2.2.3. Estimating the Copula Parameters
  • 2.2.4. Generating Scenarios - Monte Carlo Value-at-Risk
  • 2.3. Examples
  • 2.4. Results
  • 3. Quantification of Spread Risk by Means of Historical Simulation
  • 3.1. Introduction
  • 3.2. Risk Categories - a Definition of Terms
  • 3.3. Descriptive Statistics of Yield Spread Time Series
  • 3.3.1. Data Analysis with XploRe
  • 3.3.2. Discussion of Results
  • 3.4. Historical Simulation and Value at Risk
  • 3.4.1. Risk Factor: Full Yield
  • 3.4.2. Risk Factor: Benchmark
  • 3.4.3. Risk Factor: Spread over Benchmark Yield
  • 3.4.4. Conservative Approach
  • 3.4.5. Simultaneous Simulation
  • 3.5. Mark-to-Model Backtesting
  • 3.6. VaR Estimation and Backtesting with XploRe
  • 3.7. P-P Plots
  • 3.8. Q-Q Plots
  • 3.9. Discussion of Simulation Results
  • 3.9.1. Risk Factor: Full Yield
  • 3.9.2. Risk Factor: Benchmark
  • 3.9.3. Risk Factor: Spread over Benchmark Yield
  • 3.9.4. Conservative Approach
  • 3.9.5. Simultaneous Simulation
  • 3.10. XploRe for Internal Risk Models
  • II. Credit Risk
  • 4. Rating Migrations
  • 4.1. Rating Transition Probabilities
  • 4.1.1. From Credit Events to Migration Counts
  • 4.1.2. Estimating Rating Transition Probabilities
  • 4.1.3. Dependent Migrations
  • 4.1.4. Computation and Quantlets
  • 4.2. Analyzing the Time-Stability of Transition Probabilities
  • 4.2.1. Aggregation over Periods
  • 4.2.2. Are the Transition Probabilities Stationary?
  • 4.2.3. Computation and Quantlets
  • 4.2.4. Examples with Graphical Presentation
  • 4.3. Multi-Period Transitions
  • 4.3.1. Time Homogeneous Markov Chain
  • 4.3.2. Bootstrapping Markov Chains
  • 4.3.3. Computation and Quantlets
  • 4.3.4. Rating Transitions of German Bank Borrowers
  • 4.3.5. Portfolio Migration
  • 5. Sensitivity analysis of credit portfolio models
  • 5.1. Introduction
  • 5.2. Construction of portfolio credit risk models
  • 5.3. Dependence modelling
  • 5.3.1. Factor modelling
  • 5.3.2. Copula modelling
  • 5.4. Simulations
  • 5.4.1. Random sample generation
  • 5.4.2. Portfolio results
  • III. Implied Volatility
  • 6. The Analysis of Implied Volatilities
  • 6.1. Introduction
  • 6.2. The Implied Volatility Surface
  • 6.2.1. Calculating the Implied Volatility
  • 6.2.2. Surface smoothing
  • 6.3. Dynamic Analysis
  • 6.3.1. Data description
  • 6.3.2. PCA of ATM Implied Volatilities
  • 6.3.3. Common PCA of the Implied Volatility Surface
  • 7. How Precise Are Price Distributions Predicted by IBT?
  • 7.1. Implied Binomial Trees
  • 7.1.1. The Derman and Kani (D & K) algorithm
  • 7.1.2. Compensation
  • 7.1.3. Barle and Cakici (B & C) algorithm
  • 7.2. A Simulation and a Comparison of the SPDs
  • 7.2.1. Simulation using Derman and Kani algorithm
  • 7.2.2. Simulation using Barle and Cakici algorithm
  • 7.2.3. Comparison with Monte-Carlo Simulation
  • 7.3. Example - Analysis of DAX data
  • 8. Estimating State-Price Densities with Nonparametric Regression
  • 8.1. Introduction
  • 8.2. Extracting the SPD using Call-Options
  • 8.2.1. Black-Scholes SPD
  • 8.3. Semiparametric estimation of the SPD
  • 8.3.1. Estimating the call pricing function
  • 8.3.2. Further dimension reduction
  • 8.3.3. Local Polynomial Estimation
  • 8.4. An Example: Application to DAX data
  • 8.4.1. Data
  • 8.4.2. SPD, delta and gamma
  • 8.4.3. Bootstrap confidence bands
  • 8.4.4. Comparison to Implied Binomial Trees
  • 9. Trading on Deviations of Implied and Historical Densities
  • 9.1. Introduction
  • 9.2. Estimation of the Option Implied SPD
  • 9.2.1. Application to DAX Data
  • 9.3. Estimation of the Historical SPD
  • 9.3.1. The Estimation Method
  • 9.3.2. Application to DAX Data
  • 9.4. Comparison of Implied and Historical SPD
  • 9.5. Skewness Trades
  • 9.5.1. Performance
  • 9.6. Kurtosis Trades
  • 9.6.1. Performance
  • 9.7. A Word of Caution
  • IV. Econometrics
  • 10. Multivariate Volatility Models
  • 10.1. Introduction
  • 10.1.1. Model specifications
  • 10.1.2. Estimation of the BEKK-model
  • 10.2. An empirical illustration
  • 10.2.1. Data description
  • 10.2.2. Estimating bivariate GARCH
  • 10.2.3. Estimating the (co)variance processes
  • 10.3. Forecasting exchange rate densities
  • 11. Statistical Process Control
  • 11.1. Control Charts
  • 11.2. Chart characteristics
  • 11.2.1. Average Run Length and Critical Values
  • 11.2.2. Average Delay
  • 11.2.3. Probability Mass and Cumulative Distribution Function
  • 11.3. Comparison with existing methods
  • 11.3.1. Two-sided EWMA and Lucas/Saccucci
  • 11.3.2. Two-sided CUSUM and Crosier
  • 11.4. Real data example - monitoring CAPM
  • 12. An Empirical Likelihood Goodness-of-Fit Test for Diffusions
  • 12.1. Introduction
  • 12.2. Discrete Time Approximation of a Diffusion
  • 12.3. Hypothesis Testing
  • 12.4. Kernel Estimator
  • 12.5. The Empirical Likelihood concept
  • 12.5.1. Introduction into Empirical Likelihood
  • 12.5.2. Empirical Likelihood for Time Series Data
  • 12.6. Goodness-of-Fit Statistic
  • 12.7. Goodness-of-Fit test
  • 12.8. Application
  • 12.9. Simulation Study and Illustration
  • 12.10. Appendix
  • 13. A simple state space model of house prices
  • 13.1. Introduction
  • 13.2. A Statistical Model of House Prices
  • 13.2.1. The Price Function
  • 13.2.2. State Space Form
  • 13.3. Estimation with Kalman Filter Techniques
  • 13.3.1. Kalman Filtering given all parameters
  • 13.3.2. Filtering and state smoothing
  • 13.3.3. Maximum likelihood estimation of the parameters
  • 13.3.4. Diagnostic checking
  • 13.4. The Data
  • 13.5. Estimating and filtering in XploRe
  • 13.5.1. Overview
  • 13.5.2. Setting the system matrices
  • 13.5.3. Kalman filter and maximized log likelihood
  • 13.5.4. Diagnostic checking with standardized residuals
  • 13.5.5. Calculating the Kalman smoother
  • 13.6. Appendix
  • 13.6.1. Procedure equivalence
  • 13.6.2. Smoothed constant state variables
  • 14. Long Memory Effects Trading Strategy
  • 14.1. Introduction
  • 14.2. Hurst and Rescaled Range Analysis
  • 14.3. Stationary Long Memory Processes
  • 14.3.1. Fractional Brownian Motion and Noise
  • 14.4. Data Analysis
  • 14.5. Trading the Negative Persistence
  • 15. Locally time homogeneous time series modeling
  • 15.1. Intervals of homogeneity
  • 15.1.1. The adaptive estimator
  • 15.1.2. A small simulation study
  • 15.2. Estimating the coefficients of an exchange rate basket
  • 15.2.1. The Thai Baht basket
  • 15.2.2. Estimation results
  • 15.3. Estimating the volatility of financial time series
  • 15.3.1. The standard approach
  • 15.3.2. The locally time homogeneous approach
  • 15.3.3. Modeling volatility via power transformation
  • 15.3.4. Adaptive estimation under local time-homogeneity
  • 15.4. Technical appendix
  • 16. Simulation based Option Pricing
  • 16.1. Simulation techniques for option pricing
  • 16.1.1. Introduction to simulation techniques
  • 16.1.2. Pricing path independent European options on one underlying
  • 16.1.3. Pricing path dependent European options on one underlying
  • 16.1.4. Pricing options on multiple underlyings
  • 16.2. Quasi Monte Carlo (QMC) techniques for option pricing
  • 16.2.1. Introduction to Quasi Monte Carlo techniques
  • 16.2.2. Error bounds
  • 16.2.3. Construction of the Halton sequence
  • 16.2.4. Experimental results
  • 16.3. Pricing options with simulation techniques - a guideline
  • 16.3.1. Construction of the payoff function
  • 16.3.2. Integration of the payoff function in the simulation framework
  • 16.3.3. Restrictions for the payoff functions
  • 17. Nonparametric Estimators of GARCH Processes
  • 17.1. Deconvolution density and regression estimates
  • 17.2. Nonparametric ARMA Estimates
  • 17.3. Nonparametric GARCH Estimates
  • 18. Net Based Spreadsheets in Quantitative Finance
  • 18.1. Introduction
  • 18.2. Client/Server based Statistical Computing
  • 18.3. Why Spreadsheets?
  • 18.4. Using MD*ReX
  • 18.5. Applications
  • 18.5.1. Value at Risk Calculations with Copulas
  • 18.5.2. Implied Volatility Measures
  • Index
Purchased from the Charles W. Boand Fund for Business and Economics bookplate

Similar Items