Statistics for Finance /
| Main Authors: | , , |
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| Corporate Authors: | , |
| Format: | eBook |
| Language: | English |
| Published: |
Boca Raton, FL :
Chapman and Hall/CRC,
[2018].
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| Edition: | First edition. |
| Series: | Chapman & Hall/CRC Texts in Statistical Science.
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- IntroductionIntroduction to financial derivatives Financial derivativeswhats the big deal? Stylized factsOverview
- Fundamentals Interest rates Cash flows Continuously compounded interest rates Interest rate options: caps and floors
- Discrete-Time Finance The binomial one period model The one period model The multi period model
- Linear Time Series Models Introduction Linear systems in the time domain Linear stochastic processes Linear processes with a rational transfer functionAutocovariance functions Prediction in linear processes
- Non-Linear Time Series Models Introduction The aim of model buildingQualitative properties of the models Parameter estimationParametric models Model identification Prediction in non-linear models Applications of non-linear models
- Kernel Estimators in Time Series Analysis Non-parametric estimation Kernel estimators for time series Kernel estimation for regression Applications of kernel estimators-- Stochastic Calculus Dynamical systems The Wiener process Stochastic Integrals It stochastic calculus Extensions to jump processes
- Stochastic Differential Equations Stochastic differential equations Analytical solution methods FeynmanKac representation Girsanov measure transformation
- Continuous-Time Security Markets From discrete to continuous time Classical arbitrage theoryModern approach using martingale measures Pricing Model extensions Computational methods
- Stochastic Interest Rate Models Gaussian one-factor models A general class of one-factor models Time-dependent models Multifactor and stochastic volatility models
- The Term Structure of Interest Rates Basic concepts The classical approach The term structure for specific models HeathJarrowMorton framework Credit models Estimation of the term structurecurve-fitting
- Discrete-Time Approximations Stochastic Taylor expansionConvergence Discretization schemes Multilevel Monte Carlo Simulation of SDEs
- Parameter Estimation in Discretely Observed SDEsIntroduction High frequency methods Approximate methods for linear and non-linear modelsState dependent diffusion term MLE for non-linear diffusionsGeneralized method of moments (GMM) Model validation for discretely observed SDEs
- Inference in Partially Observed Processes IntroductionThe model Exact filtering Conditional moment estimators Kalman filter Approximate filters State filtering and predictionThe unscented Kalman filter A maximum likelihood method Sequential Monte Carlo filters Application of non-linear filters
- Appendix A: Projections in Hilbert Spaces Appendix B: Probability Theory
- Bibliography-- Problems appear at the end of each chapter.