Ebook: Handbook of Computational Finance

Any financial asset that is openly traded has a market price. Except for extreme market conditions, market price may be more or less than a “fair” value. Fair value is likely to be some complicated function of the current intrinsic value of tangible or intangible assets underlying the claim and our assessment of the characteristics of the underlying assets with respect to the expected rate of growth, future dividends, volatility, and other relevant market factors. Some of these factors that affect the price can be measured at the time of a transaction with reasonably high accuracy. Most factors, however, relate to expectations about the future and to subjective issues, such as current management, corporate policies and market environment, that could affect the future financial performance of the underlying assets. Models are thus needed to describe the stochastic factors and environment, and their implementations inevitably require computational finance tools.

Any financial asset that is openly traded has a market price. Except for extreme market conditions, market price may be more or less than a “fair” value. Fair value is likely to be some complicated function of the current intrinsic value of tangible or intangible assets underlying the claim and our assessment of the characteristics of the underlying assets with respect to the expected rate of growth, future dividends, volatility, and other relevant market factors. Some of these factors that affect the price can be measured at the time of a transaction with reasonably high accuracy. Most factors, however, relate to expectations about the future and to subjective issues, such as current management, corporate policies and market environment, that could affect the future financial performance of the underlying assets. Models are thus needed to describe the stochastic factors and environment, and their implementations inevitably require computational finance tools.

Any financial asset that is openly traded has a market price. Except for extreme market conditions, market price may be more or less than a “fair” value. Fair value is likely to be some complicated function of the current intrinsic value of tangible or intangible assets underlying the claim and our assessment of the characteristics of the underlying assets with respect to the expected rate of growth, future dividends, volatility, and other relevant market factors. Some of these factors that affect the price can be measured at the time of a transaction with reasonably high accuracy. Most factors, however, relate to expectations about the future and to subjective issues, such as current management, corporate policies and market environment, that could affect the future financial performance of the underlying assets. Models are thus needed to describe the stochastic factors and environment, and their implementations inevitably require computational finance tools.

Content:
Front Matter....Pages i-xi
Front Matter....Pages 1-1
Computational Finance: An Introduction....Pages 3-11
Front Matter....Pages 13-13
Modeling Asset Prices....Pages 15-33
Diffusion Models of Asset Prices....Pages 35-60
Jump-Diffusion Models Driven by L?vy Processes....Pages 61-88
Multivariate Time Series Models for Asset Prices....Pages 89-115
Option Data and Modeling BSM Implied Volatility....Pages 117-142
Interest Rate Derivatives Pricing with Volatility Smile....Pages 143-201
Volatility Investing with Variance Swaps....Pages 203-219
Front Matter....Pages 221-221
Evaluation of Asset Pricing Models Using Two-Pass Cross-Sectional Regressions....Pages 223-251
Parametric Estimation of Risk Neutral Density Functions....Pages 253-275
Nonparametric Estimation of Risk-Neutral Densities....Pages 277-305
Value at Risk Estimation....Pages 307-333
Volatility Estimation Based on High-Frequency Data....Pages 335-369
Identifying Jumps in Asset Prices....Pages 371-399
Simulation-Based Estimation Methods for Financial Time Series Models....Pages 401-435
Front Matter....Pages 437-437
Filtering Methods....Pages 439-467
Fitting High-Dimensional Copulae to Data....Pages 469-501
Numerical Methods for Nonlinear PDEs in Finance....Pages 503-528
Numerical Solution of Stochastic Differential Equations in Finance....Pages 529-550
Lattice Approach and Implied Trees....Pages 551-577
Front Matter....Pages 437-437
Efficient Options Pricing Using the Fast Fourier Transform....Pages 579-604
Dynamic Programming and Hedging Strategies in Discrete Time....Pages 605-631
Approximation of Dynamic Programs....Pages 633-649
Computational Issues in Stress Testing....Pages 651-673
Portfolio Optimization....Pages 675-702
Low-Discrepancy Simulation....Pages 703-729
Introduction to Support Vector Machines and Their Applications in Bankruptcy Prognosis....Pages 731-761
Front Matter....Pages 763-763
MATLAB® as a Tool in Computational Finance....Pages 765-780
R as a Tool in Computational Finance....Pages 781-804

Any financial asset that is openly traded has a market price. Except for extreme market conditions, market price may be more or less than a “fair” value. Fair value is likely to be some complicated function of the current intrinsic value of tangible or intangible assets underlying the claim and our assessment of the characteristics of the underlying assets with respect to the expected rate of growth, future dividends, volatility, and other relevant market factors. Some of these factors that affect the price can be measured at the time of a transaction with reasonably high accuracy. Most factors, however, relate to expectations about the future and to subjective issues, such as current management, corporate policies and market environment, that could affect the future financial performance of the underlying assets. Models are thus needed to describe the stochastic factors and environment, and their implementations inevitably require computational finance tools.

Content:
Front Matter....Pages i-xi
Front Matter....Pages 1-1
Computational Finance: An Introduction....Pages 3-11
Front Matter....Pages 13-13
Modeling Asset Prices....Pages 15-33
Diffusion Models of Asset Prices....Pages 35-60
Jump-Diffusion Models Driven by L?vy Processes....Pages 61-88
Multivariate Time Series Models for Asset Prices....Pages 89-115
Option Data and Modeling BSM Implied Volatility....Pages 117-142
Interest Rate Derivatives Pricing with Volatility Smile....Pages 143-201
Volatility Investing with Variance Swaps....Pages 203-219
Front Matter....Pages 221-221
Evaluation of Asset Pricing Models Using Two-Pass Cross-Sectional Regressions....Pages 223-251
Parametric Estimation of Risk Neutral Density Functions....Pages 253-275
Nonparametric Estimation of Risk-Neutral Densities....Pages 277-305
Value at Risk Estimation....Pages 307-333
Volatility Estimation Based on High-Frequency Data....Pages 335-369
Identifying Jumps in Asset Prices....Pages 371-399
Simulation-Based Estimation Methods for Financial Time Series Models....Pages 401-435
Front Matter....Pages 437-437
Filtering Methods....Pages 439-467
Fitting High-Dimensional Copulae to Data....Pages 469-501
Numerical Methods for Nonlinear PDEs in Finance....Pages 503-528
Numerical Solution of Stochastic Differential Equations in Finance....Pages 529-550
Lattice Approach and Implied Trees....Pages 551-577
Front Matter....Pages 437-437
Efficient Options Pricing Using the Fast Fourier Transform....Pages 579-604
Dynamic Programming and Hedging Strategies in Discrete Time....Pages 605-631
Approximation of Dynamic Programs....Pages 633-649
Computational Issues in Stress Testing....Pages 651-673
Portfolio Optimization....Pages 675-702
Low-Discrepancy Simulation....Pages 703-729
Introduction to Support Vector Machines and Their Applications in Bankruptcy Prognosis....Pages 731-761
Front Matter....Pages 763-763
MATLAB® as a Tool in Computational Finance....Pages 765-780
R as a Tool in Computational Finance....Pages 781-804
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